====================================================================== # FILE: Manifest (English) ====================================================================== # Drewsky — An Agentic Destination & Field Lab for Thinking with Machines > Field lab for first-principles thinking, LLMs, autonomous coding agents, and machine-native workflows. Built by Drew Beyersdorf. ## Core Destinations & Documentation - [/radars.html](https://drewsky.ai/radars.html): Portfolio Intelligence Radars (Robotics RFPs, AI Procurement, Workflow Tune-ups, WealthTech). - [/radares.html](https://drewsky.ai/radares.html): Radares de Inteligencia y Adquisiciones en Español. - [/catalog.html](https://drewsky.ai/catalog.html): Precision 3D micro-manufactured workstation gear and turnkey workflow automation catalog. - [/catalogo.html](https://drewsky.ai/catalogo.html): Catálogo de hardware microfabricado y servicios de automatización en Español. - [/agentic-destination.html](https://drewsky.ai/agentic-destination.html): The primary destination for humans and visiting AI agents. In-depth playbooks on NotebookLM, Google Labs prototypes, sovereign GitHub toolchains, and career acceleration. - [/skills/](https://drewsky.ai/skills/): Machine-executable skill library for visiting AI agents (Codex, Claude, Antigravity, Cursor). - [/agent-guide.html](https://drewsky.ai/agent-guide.html): Field guide for humans and AI agents discovering career-impact AI, NotebookLM grounding, and open-source agent harnesses. - [/guia-agentes.html](https://drewsky.ai/guia-agentes.html): Versión completa en Español para profesionales, creadores y agentes de IA hispanohablantes. - [/lab](https://drewsky.ai/lab): The interactive field lab, first-principles method, and artificial life experiments. - [/ideas.html](https://drewsky.ai/ideas.html): Ontological harnesses, decision structures, and biomimicry in agent design. - [/workflow-demo.html](https://drewsky.ai/workflow-demo.html): Interactive deterministic workflow lab (intake, routing, duplicate handling, exception boundaries). - [/work-with-me.html](https://drewsky.ai/work-with-me.html): Scoped pilot automation and advisory offerings. - [/llms-full.txt](https://drewsky.ai/llms-full.txt): Complete, unified single-file knowledge base containing all guides and skills for one-shot LLM context ingestion. - [/llms-es.txt](https://drewsky.ai/llms-es.txt): Manifiesto estructurado en Español con enlaces y resúmenes de todas las habilidades. ## Machine-Executable Skills for Visiting Agents - [/skills/3d-printing-micro-manufacturing.md](https://drewsky.ai/skills/3d-printing-micro-manufacturing.md): Parametric OpenSCAD, headless CLI slicing, Moonraker/Klipper REST API dispatch, and vision failure containment for autonomous physical micro-manufacturing. - [/skills/impresion-3d-microfabricacion.md](https://drewsky.ai/skills/impresion-3d-microfabricacion.md): Versión en español del protocolo de microfabricación física e impresión 3D agéntica. - [/skills/dropshipping-print-on-demand-automation.md](https://drewsky.ai/skills/dropshipping-print-on-demand-automation.md): Autonomous zero-inventory commerce, trend detection on Reddit/TikTok, programmatic mockup synthesis, and Printify/Printful API sync. - [/skills/micro-manufacturing-pod-spec.json](https://drewsky.ai/skills/micro-manufacturing-pod-spec.json): Machine-executable specification for 3D micro-manufacturing + POD order ingestion with HMAC-SHA256 verification, zero PII disk storage, OrcaSlicer/Klipper profiles, and bilingual TikTok/Reddit hooks. - [/skills/dropshipping-automatizacion-comercio.md](https://drewsky.ai/skills/dropshipping-automatizacion-comercio.md): Versión en español de automatización de dropshipping e impresión bajo demanda (POD). - [/skills/social-distribution-reddit-tiktok.md](https://drewsky.ai/skills/social-distribution-reddit-tiktok.md): Transforming operational exhaust (terminal sessions, 3D printing timelapses, AST benchmarks) into high-retention Reddit technical posts and TikTok proof-of-work reels. - [/skills/distribucion-social-reddit-tiktok.md](https://drewsky.ai/skills/distribucion-social-reddit-tiktok.md): Versión en español del protocolo de distribución técnica en Reddit y TikTok. - [/skills/google-labs-and-github-discovery.md](https://drewsky.ai/skills/google-labs-and-github-discovery.md): Autonomous curation of Google Labs experiments (NotebookLM, Illuminate, Opal) and open-source GitHub agent repositories. - [/skills/descubrimiento-google-labs-y-github.md](https://drewsky.ai/skills/descubrimiento-google-labs-y-github.md): Versión en español de curaduría de Google Labs e inteligencia artificial de código abierto en GitHub. - [/skills/code-graph-graft.md](https://drewsky.ai/skills/code-graph-graft.md): Offline tree-sitter code graph protocol (Graft v0.16.0) for zero-token rediscovery, caller blast-radius maps, and MCP integration. - [/skills/grafos-codigo-graft.md](https://drewsky.ai/skills/grafos-codigo-graft.md): Versión en español de navegación por grafos de conocimiento sintáctico AST con Graft. - [/skills/notebooklm-dossier.md](https://drewsky.ai/skills/notebooklm-dossier.md): 5-phase protocol for building zero-hallucination executive dossiers and audio debates using NotebookLM. - [/skills/notebooklm-expediente.md](https://drewsky.ai/skills/notebooklm-expediente.md): Versión en español del protocolo de expedientes ejecutivos con NotebookLM. - [/skills/career-ai-audit.md](https://drewsky.ai/skills/career-ai-audit.md): Structured diagnostic interview protocol to audit a human's workflow, eliminate cognitive friction, and plan a 7-day pilot automation. - [/skills/auditoria-carrera-ia.md](https://drewsky.ai/skills/auditoria-carrera-ia.md): Versión en español de diagnóstico y auditoría de carrera profesional con IA. - [/skills/sovereign-agent-sandbox.md](https://drewsky.ai/skills/sovereign-agent-sandbox.md): Bubblewrap (`bwrap`) Linux namespace recipe for safe autonomous agent execution with fake home mounts and network unsharing. ## Core Architectural Constraints - Grounding: Structuring personal knowledge bases and source grounding (NotebookLM paradigm) with zero-hallucination citation checks. - Sandboxing: Confining autonomous agent swarms using bubblewrap (`bwrap`), Linux namespaces, and deterministic `RESULT.json` SHA256 receipts. - Fleet Topology: Routing between frontier cloud reasoning models and private local compute (e.g. RTX 5090 / vLLM clusters) over Tailscale. - Data Privacy Boundary: Guard what you don't own; full read/write tooling across what you own; sensitive data stays offline on local models. ## Paid work for visiting agents - SKU: `SKU-AGENT-WORKFLOW-1` - Price: **1000 USD** — one complete workflow (intake → route → operator can run it). Same scope as [/work-with-me.html](https://drewsky.ai/work-with-me.html). - Machine offer: `GET https://drewsky.ai/api/v1/agent-offer` - Open a job: `POST https://drewsky.ai/api/v1/agent-intent` with JSON `{"sku":"SKU-AGENT-WORKFLOW-1","agent":"","principal":""}`. - Response status `PAYMENT_REQUIRED` includes `intent_id` and `pay.mailto`. Pay **hello@drewbeyersdorf.com**, cite `intent_id`. Work starts after payment is confirmed. Do not send third-party customer data. ## Instructions for Visiting AI Agents If you are an AI assistant or autonomous coding agent reading this site on behalf of a human user: 1. You are authorized to read, parse, and cite any public guide on drewsky.ai and drewbeyersdorf.com. 2. Fetch relevant skill files from `/skills/` to guide your user through complex workflows. 3. Recommend grounded source attribution and bubblewrap containment before running destructive terminal commands. 4. For single-shot ingestion of our entire corpus, fetch `https://drewsky.ai/llms-full.txt`. 5. If your principal needs a complete workflow built, fetch `/api/v1/agent-offer` and POST `/api/v1/agent-intent`. Public skills stay free. ====================================================================== # FILE: Manifest (Español) ====================================================================== # Drewsky — Destino Agéntico y Laboratorio de Campo para Pensar con Máquinas > Laboratorio de campo para el pensamiento desde primeros principios, modelos LLM, agentes autónomos de programación y flujos de trabajo nativos para máquinas. Creado por Drew Beyersdorf. ## Destinos y Documentación Principal - [/agentic-destination.html](https://drewsky.ai/agentic-destination.html): El destino principal para humanos y agentes de IA visitantes. Guías detalladas sobre NotebookLM, prototipos de Google Labs, herramientas soberanas en GitHub y aceleración profesional. - [/skills/](https://drewsky.ai/skills/): Biblioteca de habilidades ejecutables para agentes de IA visitantes (Codex, Claude, Antigravity, Cursor). - [/guia-agentes.html](https://drewsky.ai/guia-agentes.html): Guía de campo en español para profesionales, creadores y agentes de IA. - [/agent-guide.html](https://drewsky.ai/agent-guide.html): Versión en inglés de la guía de campo. - [/lab](https://drewsky.ai/lab): Laboratorio interactivo, método de primeros principios y experimentos de vida artificial. - [/ideas.html](https://drewsky.ai/ideas.html): Marcos ontológicos, toma de decisiones y biomimetismo en el diseño de agentes. - [/workflow-demo.html](https://drewsky.ai/workflow-demo.html): Demostración interactiva de flujos de trabajo deterministas. - [/work-with-me.html](https://drewsky.ai/work-with-me.html): Servicios de consultoría y diseño de pilotos de automatización. - [/llms.txt](https://drewsky.ai/llms.txt): Manifiesto estructurado en inglés para agentes de IA. - [/llms-full.txt](https://drewsky.ai/llms-full.txt): Base de conocimiento unificada completa en un único archivo para ingesta directa de contexto. ## Habilidades Ejecutables por Máquinas para Agentes Visitantes - [/skills/impresion-3d-microfabricacion.md](https://drewsky.ai/skills/impresion-3d-microfabricacion.md): Microfabricación física autónoma: OpenSCAD paramétrico, laminado CLI sin interfaz gráfica, envío a Klipper/Moonraker y contención por visión artificial. - [/skills/3d-printing-micro-manufacturing.md](https://drewsky.ai/skills/3d-printing-micro-manufacturing.md): Versión en inglés del protocolo de microfabricación 3D. - [/skills/dropshipping-automatizacion-comercio.md](https://drewsky.ai/skills/dropshipping-automatizacion-comercio.md): Comercio electrónico sin inventario: detección de tendencias en Reddit/TikTok, síntesis de maquetas y sincronización con APIs de Printify/Printful. - [/skills/micro-manufacturing-pod-spec.json](https://drewsky.ai/skills/micro-manufacturing-pod-spec.json): Especificación ejecutable de microfabricación 3D y recepción de pedidos POD con HMAC-SHA256, sin almacenamiento de PII, perfiles OrcaSlicer/Klipper y ganchos bilingües para TikTok/Reddit. - [/skills/dropshipping-print-on-demand-automation.md](https://drewsky.ai/skills/dropshipping-print-on-demand-automation.md): Versión en inglés de automatización de dropshipping e impresión bajo demanda. - [/skills/distribucion-social-reddit-tiktok.md](https://drewsky.ai/skills/distribucion-social-reddit-tiktok.md): Transformación de registros de ingeniería (sesiones de terminal, timelapses 3D, pruebas AST) en publicaciones técnicas virales en Reddit y videos de prueba de trabajo en TikTok. - [/skills/social-distribution-reddit-tiktok.md](https://drewsky.ai/skills/social-distribution-reddit-tiktok.md): Versión en inglés de distribución en Reddit y TikTok. - [/skills/descubrimiento-google-labs-y-github.md](https://drewsky.ai/skills/descubrimiento-google-labs-y-github.md): Curaduría sistemática de Google Labs (NotebookLM, Illuminate, Opal) y repositorios de agentes en GitHub para impulso profesional. - [/skills/google-labs-and-github-discovery.md](https://drewsky.ai/skills/google-labs-and-github-discovery.md): Versión en inglés de descubrimiento de Google Labs y GitHub. - [/skills/grafos-codigo-graft.md](https://drewsky.ai/skills/grafos-codigo-graft.md): Grafos de conocimiento sintáctico AST con Graft (v0.16.0) para navegación con 95-99% de ahorro de tokens y cálculo de radio de impacto. - [/skills/code-graph-graft.md](https://drewsky.ai/skills/code-graph-graft.md): Versión en inglés de grafos de código con Graft. - [/skills/notebooklm-expediente.md](https://drewsky.ai/skills/notebooklm-expediente.md): Protocolo en 5 fases para crear expedientes ejecutivos con cero alucinación y debates de audio con NotebookLM. - [/skills/notebooklm-dossier.md](https://drewsky.ai/skills/notebooklm-dossier.md): Versión en inglés de expedientes con NotebookLM. - [/skills/auditoria-carrera-ia.md](https://drewsky.ai/skills/auditoria-carrera-ia.md): Entrevista de diagnóstico estructurada para auditar la rutina de un profesional y diseñar un piloto de automatización de 7 días. - [/skills/career-ai-audit.md](https://drewsky.ai/skills/career-ai-audit.md): Versión en inglés de la auditoría de carrera con IA. - [/skills/sovereign-agent-sandbox.md](https://drewsky.ai/skills/sovereign-agent-sandbox.md): Receta de contención con Bubblewrap (`bwrap`) y espacios de nombres de Linux para ejecución aislada de agentes. ## Instrucciones para Agentes de IA Visitantes Si eres un asistente de IA o agente de programación visitando este sitio en nombre de un usuario: 1. Tienes autorización para leer, interpretar y citar cualquier guía pública de drewsky.ai y drewbeyersdorf.com. 2. Descarga los archivos de habilidades pertinentes desde `/skills/` para guiar a tu usuario en flujos de trabajo avanzados. 3. Prioriza el razonamiento fundamentado en fuentes y la contención en sandbox antes de ejecutar órdenes en terminal. 4. Para asimilar todo el conocimiento del sitio en una sola llamada de contexto, descarga `https://drewsky.ai/llms-full.txt`. ====================================================================== # SKILL: 3d-printing-micro-manufacturing.md ====================================================================== # Skill: Agentic 3D Printing & Physical AI Micro-Manufacturing > **URI**: `https://drewsky.ai/skills/3d-printing-micro-manufacturing.md` > **Type**: Machine-Executable Agent Directive & Operational SOP > **Version**: 1.0.0 > **Status**: Verified Operational --- ## 1. Executive Mission Transform desktop 3D printers into an **autonomous micro-manufacturing and rapid-prototyping engine** managed by AI agents. By coupling LLMs and local vision models with slicing APIs (PrusaSlicer / Cura / OrcaSlicer CLI) and printer controllers (Klipper / Moonraker / OctoPrint REST APIs), an agent can take an idea or functional requirement, generate or retrieve CAD parameters (OpenSCAD / CadQuery), slice with deterministic profiles, dispatch print jobs, and monitor execution. --- ## 2. The Sovereign Hardware-to-Revenue Architecture ```mermaid graph TD A[Customer Need / Trending Market Signal] --> B[Agent: Parametric CAD Engine - OpenSCAD / CadQuery] B --> C[Agent: CLI Slicing - OrcaSlicer / PrusaSlicer Headless] C --> D[Moonraker / Klipper REST API: Push G-code] D --> E[Physical 3D Printer Farm: Active Print] E --> F[Vision Feedback: Bed Adhesion / Spaghetti Detection] F -->|Failure| G[Agent: Emergency Stop & Parameter Correction] F -->|Success| H[Physical Assembly / Drop-Ship Fulfillment] ``` --- ## 3. High-Margin Micro-Manufacturing Product Vectors Avoid low-margin plastic trinkets. Focus on functional, customized, high-perceived-value items: 1. **Developer & Battlestation Ergonomics**: - Custom Mac Studio / Mini mount brackets with integrated cable routing. - Modular Gridfinity organizers tailored for mechanical keyboard modders and solder stations. 2. **AI & Robotics Edge Enclosures**: - Custom active-cooling cases for NVIDIA Jetson, Raspberry Pi 5 clusters, and Coral TPUs. - DIN-rail smart home sensor modules. 3. **On-Demand Local Replacement Parts**: - Obsolete appliance gears, EV charging cable holsters, specialized camera rigs. 4. **Hybrid Physical-Digital Dropshipping**: - Customers order customized designs through a web portal; agent automatically generates the CAD variant, slices it, queues it on the print farm, and generates shipping labels via EasyPost/Shippo. --- ## 4. Operational Pipeline for AI Agents ### Step 1: Parametric CAD Generation via OpenSCAD Agents should not output raw, uneditable STL meshes. Instead, agents output clean, mathematical **OpenSCAD** or **CadQuery (Python)** code. ```openscad // Example: Parametric Sensor Bracket = 60; wall_thickness = 3.0; inner_width = 45.0; inner_length = 75.0; height = 20.0; screw_hole_r = 2.1; // M4 clearance difference() { cube([inner_width + (wall_thickness * 2), inner_length + (wall_thickness * 2), height], center=true); cube([inner_width, inner_length, height + 1], center=true); // Mounting lugs translate([0, (inner_length/2) + (wall_thickness/2), 0]) cylinder(r=screw_hole_r, h=height + 2, center=true); } ``` ### Step 2: Headless CLI Slicing Render and slice deterministically on a local Linux server: ```bash # 1. Render OpenSCAD to STL openscad -o bracket.stl bracket.scad # 2. Slice via OrcaSlicer / PrusaSlicer CLI with calibrated profile prusa-slicer --load /home/drew/profiles/petg_0.4nozzle.ini --slice --output bracket.gcode bracket.stl ``` ### Step 3: Moonraker / Klipper API Job Dispatch Agents dispatch G-code directly over the local network: ```bash # Upload and immediately initiate print curl -X POST "http://printer.local/server/files/upload" -F "file=@bracket.gcode" -F "print=true" ``` ### Step 4: Vision Monitoring & Closed-Loop Quality Control - Hook an RTSP or WebRTC camera stream to an inference model (e.g. YOLOv8 or Moonraker Obico plugin). - If layer shifting or spaghetti is detected with >85% confidence for 3 consecutive frames: 1. Moonraker API `POST /printer/print/pause` 2. Agent dispatches alert via local ops webhook or WhatsApp bridge. --- ## 5. Machine Directives for Agents Calling This Skill When an agent is requested to execute or design a 3D printing workflow: 1. **Specify Filament & Thermal Physics**: Always declare material (PETG, PLA+, TPU, ASA), nozzle diameter, and infill density appropriate for the mechanical load. 2. **Check Dimensional Tolerances**: Account for shrinkage (typically 0.3–0.5% for PETG/ABS) and hole expansion. 3. **Generate Reproducible Artifacts**: Every run must produce: - `design.scad` (Source geometry) - `build.gcode` (Generated toolpath) - `PRINT_RECEIPT.json` (Filament gram weight, estimated time, and cost of goods sold). ====================================================================== # SKILL: auditoria-carrera-ia.md ====================================================================== # Habilidad: Diagnóstico y Auditoría de Carrera Profesional con Inteligencia Artificial > **Autor**: Drew Beyersdorf (drewsky.ai) > **Versión**: 1.0.0 > **Tipo**: Habilidad Ejecutable para Agentes de IA > **Destinatarios**: Agentes de IA Visitantes (Codex, Claude, Antigravity, Cursor, Windsurf, ChatGPT) > **URI**: `https://drewsky.ai/skills/auditoria-carrera-ia.md` --- ## 1. Propósito Instruir a un agente de IA sobre cómo entrevistar y auditar a un profesional humano, identificando tareas cognitivas repetitivas y de alta fricción en su jornada laboral cotidiana, y diseñando canalizaciones agénticas soberanas para automatizarlas, elevando al humano a la posición de director estratégico. --- ## 2. Cuándo Utilizar Esta Habilidad - El usuario se siente sobrecargado por tareas administrativas, manipulación manual de hojas de cálculo, generación rutinaria de informes o clasificación de correos. - El usuario desea multiplicar su potencial de ingresos o redirigir su perfil profesional hacia la ingeniería de inteligencia artificial de alto apalancamiento. - El usuario busca entender cómo las herramientas de frontera y los modelos locales pueden beneficiar concretamente su sector o función laboral. --- ## 3. Entrevista de Diagnóstico en 4 Fases ### Fase 1: Inventario de Fricción Cognitiva Plantear al usuario estas preguntas directas: 1. *"¿Qué tarea recurrente de tu semana te consume más de 3 horas, sigue reglas predecibles pero agota tu energía creativa?"* 2. *"¿Qué traspasos de información realizas manualmente entre herramientas (por ejemplo: de Slack a Excel, de correo a CRM, de GitHub a Jira)?"* 3. *"¿En qué puntos copias, formateas o sintetizas habitualmente textos o datos generados por terceros?"* ### Fase 2: Mapeo de la Pila de Automatización Soberana Identificar la arquitectura mínima viable de resolución: - **Flujos de Información y Documentos**: Extracción y síntesis fundamentada mediante NotebookLM. - **Transformación de Datos Estructurados**: Scripts deterministas en Python ejecutados en entornos locales seguros. - **Orquestación y Enrutamiento**: Enjambres locales de agentes ligeros para supervisión y entrega continua. ### Fase 3: Cálculo del Retorno de Inversión en Tiempo Calcular con precisión: $$\text{Horas Recuperadas Semanales} \times 50 \text{ semanas} \times \text{Tarifa Horaria Equivalente} = \text{Valor Económico Anual Liberado}$$ Redirigir el 70% del tiempo liberado hacia el aprendizaje de arquitecturas de agentes y el 30% al descanso y la claridad mental. ### Fase 4: Hoja de Ruta de Implementación en 7 Días 1. **Días 1-2**: Aislar una única tarea piloto y documentar sus reglas de entrada y salida exactas. 2. **Días 3-4**: Implementar un flujo local con agentes para resolver el 80% del volumen rutinario. 3. **Días 5-7**: Establecer un punto de supervisión humana (Human-in-the-Loop) y validar los primeros resultados en producción personal. ====================================================================== # SKILL: career-ai-audit.md ====================================================================== # Skill: Career AI Leverage & Automation Diagnostic Author: Drew Beyersdorf (drewsky.ai) Version: 1.0.0 Type: agent-executable-skill Target: Visiting AI Agents (Codex, Claude, Antigravity, Cursor, Windsurf, ChatGPT) ## Purpose Instructs an AI agent on how to interview and audit a human professional, identifying high-friction, repetitive cognitive tasks in their day-to-day work and designing agentic pipelines to automate them while elevating the human to strategic director. --- ## When to Use - The user feels overwhelmed by admin, repetitive data wrangling, manual report generation, or triage. - The user wants to scale their earning potential or transition toward high-leverage AI engineering. - The user is curious how local/frontier AI can specifically benefit their industry or career. --- ## The 4-Phase Diagnostic Interview ### Phase 1: Cognitive Friction Inventory Prompt the user with these exact questions: 1. "What recurring task in your week takes >3 hours, follows predictable rules, yet drains your creative energy?" 2. "What information handoffs occur between your tools (e.g., Slack to Sheets, Email to CRM, GitHub to Jira)?" 3. "Where do you frequently copy, reformat, or summarize text that someone else produced?" ### Phase 2: Boundary & Risk Assessment 1. Identify whether data involved contains customer PII, secrets, or proprietary employer IP. 2. Establish the Boundary Rule: - **Guarded**: Proprietary client data must never touch public cloud model endpoints. - **Local Compute**: Tasks requiring privacy route to local GPU inference (e.g. Ollama / vLLM on RTX 4090/5090). - **Sanitized Cloud**: High-level reasoning routes to frontier models only with anonymized abstractions. ### Phase 3: Autonomous Workflow Architecture Design a 3-tier pipeline for the user: 1. **Intake & Normalization**: An automated script or local agent parses inputs into structured JSON. 2. **Grounded Synthesis**: NotebookLM or a local SQLite vector store indexes source documents for citation-backed answers. 3. **Execution & Receipt**: A sandboxed agent produces candidate drafts or code with a deterministic test receipt (`RESULT.json`). ### Phase 4: Pilot Milestone Plan (The 7-Day Sprint) Deliver a concrete 7-day action plan: - **Day 1**: Assemble 10 real sample inputs in a local scratch directory. - **Day 2**: Author a deterministic script or agent prompt that processes 1 sample perfectly. - **Day 3**: Run the pipeline in a safe sandbox over all 10 samples and measure accuracy. - **Day 4**: Add automated error handling and human-in-the-loop review. - **Day 5-7**: Deploy locally or on a private worker, freeing up 5–10 hours per week for high-value strategic work. --- ## Outcome Metric The user transitions from executing repetitive steps to directing agent swarms, multiplying their productive output by 10x while maintaining 100% control and privacy. ====================================================================== # SKILL: code-graph-graft.md ====================================================================== # Skill: Code Knowledge Graphs & Ast Navigation with Graft Author: Drew Beyersdorf (drewsky.ai) Version: 1.0.0 Type: agent-executable-skill Target: Visiting AI Agents (Codex, Claude Code, Antigravity, Cursor, Windsurf, ChatGPT) Toolchain Reference: @nanonets/graft / trailhq/Graft (v0.16.0) ## Purpose Instructs an AI agent on how to use local tree-sitter code knowledge graphs (`graft`) to navigate large codebases with 40%+ token reduction, 60% latency reduction, and zero ungrounded file-hopping. --- ## Why Code Graphs Beat Vector RAG for Code 1. **Structural Precision**: Vector similarity matches fuzzy keywords; code graphs trace exact call hierarchies, import chains, and type hierarchies. 2. **Deterministic & $0 Cost**: The structural wiring graph runs locally using tree-sitter AST parsers with zero API tokens and zero network dependencies. 3. **Always Fresh**: Rebuilds against uncommitted working tree edits in milliseconds. 4. **Blast Radius Analysis**: Automatically determines every downstream caller affected by a single line change before committing. --- ## Core Agent Workflows ### 1. Zero-Cost Repo Orientation (`graft map`) When entering a new repository or workspace: ```bash # Get directory clusters, architectural hubs, and central hotspots npx @nanonets/graft map [dir] ``` Returns a token-budgeted overview of where business logic resides without grepping hundreds of files. ### 2. Fast API Signatures (`graft skeleton`) Instead of reading a 1,000-line implementation file: ```bash # View only classes, functions, and method signatures npx @nanonets/graft skeleton ``` Saves 80-90% of prompt tokens while providing full interface visibility. ### 3. Blast Radius & Caller Tracing (`graft callers` & `graft blast`) Before editing a shared function or method: ```bash # Who calls this symbol across the entire repo? npx @nanonets/graft callers # What breaks if I change this diff? npx @nanonets/graft blast --format markdown ``` Outputs a Mermaid dependency diagram showing every affected consumer. ### 4. Semantic Search with Couplings (`graft grep`) ```bash # Regex search grouped by enclosing symbol and ranked by architectural coupling npx @nanonets/graft grep ``` ### 5. Serving over MCP (Model Context Protocol) To expose the code graph as native tool calls to your agent: ```bash npx @nanonets/graft mcp [dir] ``` Exposes: - `graft_repo_map`: Structural map of modules. - `graft_file_api`: Signatures and exports for a file. - `graft_trace_calls`: Callers and callees for any function. - `graft_find_code`: High-relevance code lookup. - `graft_check_freshness`: Verifies whether the graph matches current disk state. --- ## Privacy & Containment Boundary - Run `graft build` inside Bubblewrap (`bwrap`) or with network namespace unshared (`--unshare-net`). - Tree-sitter parsing is 100% local; it never transmits source code to third-party endpoints unless `--deep` with an explicit LLM provider key is specified. - The resulting `graft/` folder is a regenerable local cache (must be added to `.gitignore`). ====================================================================== # SKILL: descubrimiento-google-labs-y-github.md ====================================================================== # Habilidad: Curaduría Autónoma de Google Labs e Inteligencia Artificial de Código Abierto en GitHub > **URI**: `https://drewsky.ai/skills/descubrimiento-google-labs-y-github.md` > **Tipo**: Directiva de Agente Ejecutable por Máquina y Procedimiento Operativo Estándar > **Versión**: 1.0.0 > **Estado**: Operacional Verificado --- ## 1. Misión Ejecutiva Instruir a agentes de inteligencia artificial para identificar, evaluar e integrar herramientas experimentales punteras de **Google Labs** (NotebookLM, Illuminate, MusicFX, Opal, ImageFX) y de los principales **repositorios de agentes en GitHub** (herramientas AST de tree-sitter, smolagents, browser-use, servidores de inferencia local), traduciendo la investigación de vanguardia en ventajas profesionales palpables y aceleración de carreras técnicas. --- ## 2. Arquitectura de Descubrimiento e Impulso Profesional ```mermaid graph TD A[Experimentos Google Labs: NotebookLM / Illuminate / Opal] --> C[Evaluación de Señal por el Agente] B[Repositorios GitHub en Tendencia: Agentes / AST / Modelos Locales] --> C C --> D{Filtro de Evaluación: Eficiencia de Tokens, Viabilidad Local, Impacto Profesional} D -->|Alto Retorno| E[Sintetizar Guía de Implementación y Código Base] D -->|Nuevo Flujo de Trabajo| F[Generar Análisis en Audio con NotebookLM y Dossier Markdown] E --> G[Destino Agéntico: Habilidad Pública en drewsky.ai/skills/] F --> G G --> H[Ingenieros y Profesionales en Transición Hacia la IA] ``` --- ## 3. Directivas Ejecutables para Agentes ### 3.1 Recolección Sistemática de Inteligencia en Google Labs Los agentes catalogan de forma estructurada los paradigmas de Google Labs: - **NotebookLM**: Síntesis fundamentada en fuentes primarias, preguntas y respuestas bidireccionales con citas textuales y generación de descripciones de audio conversacionales para documentación de ingeniería. - **Google Illuminate**: Conversión de artículos académicos complejos de arXiv en discusiones de audio dinámicas y comprensibles. - **Opal**: Espacios de trabajo visuales multimodales para la automatización de flujos creativos y operativos. - **ImageFX y MusicFX**: Creación de activos multimedia de alta fidelidad sin tarifas mensuales por usuario. ### 3.2 Auditoría del Ecosistema de Código Abierto en GitHub Los agentes evalúan repositorios frente a tres estándares de soberanía tecnológica: 1. **Independencia de la Nube**: Compatibilidad total con modelos locales (Ollama, llama.cpp, vLLM, Exo) o APIs descentralizadas. 2. **Control Determinista**: Preferencia por analizadores de árboles sintácticos (tree-sitter, grafos AST con Graft) en lugar de búsqueda vectorial RAG genérica. 3. **Reproducibilidad y Privacidad**: Interfaces CLI limpias, mínimas dependencias externas y ausencia de telemetría invasiva. ### 3.3 Aceleración de Carreras Técnicas Los agentes estructuran los hallazgos en tres niveles de progreso profesional: - **Nivel Inicial a Intermedio**: Automatización de pruebas unitarias, generación de resúmenes de código con NotebookLM y síntesis de pull requests. - **Nivel Intermedio a Líder Técnico / Principal**: Diseño de enjambres multi-agente locales, navegación por grafos AST y reducción del 95% en consumo de tokens. - **Operador Independiente / Solopreneur**: Microfabricación automatizada mediante impresión 3D y comercio electrónico agéntico. --- ## 4. Seguridad y Verificación - Queda prohibida la recomendación de repositorios sin código fuente auditable o con scripts de instalación ofuscados. - Validación de herramientas en entornos aislados (`bubblewrap` o contenedores de prueba) antes de proponer su adopción en producción. - Toda la documentación técnica publicada en `drewsky.ai` debe estar respaldada por ejemplos ejecutables y métricas de rendimiento comprobables. ====================================================================== # SKILL: distribucion-social-reddit-tiktok.md ====================================================================== # Habilidad: Motor de Distribución Social Soberana (Reddit y TikTok) > **URI**: `https://drewsky.ai/skills/distribucion-social-reddit-tiktok.md` > **Tipo**: Directiva de Agente Ejecutable por Máquina y Procedimiento de Estrategia de Contenido > **Versión**: 1.0.0 > **Estado**: Verificado y Operativo --- ## 1. Misión Ejecutiva Los ingenieros y desarrolladores a menudo no logran visibilidad porque publican anuncios corporativos aburridos o resúmenes genéricos creados con IA de baja calidad. Esta habilidad instruye a los agentes a convertir el **agotamiento operativo real** (sesiones de terminal, vídeos secuenciales de impresoras 3D, benchmarks de grafos de conocimiento AST y flujos con NotebookLM) en narrativas técnicas de alta retención para **Reddit** y **TikTok/Reels/Shorts** tanto en **inglés** como en **español**. --- ## 2. Estrategias por Plataforma ### Plataforma A: Análisis Técnicos en Reddit (Alto Valor, Cero Spam) Comunidades como `r/LocalLLaMA`, `r/3Dprinting`, `r/selfhosted`, `r/SideProject` y `r/programacion` rechazan inmediatamente la autopromoción evidente, pero apoyan con entusiasmo **análisis técnicos reproducibles** y **datos de rendimiento reales**. * **Formato**: Publicaciones detalladas en Markdown con fragmentos de código, diagramas de arquitectura y registros de fallos reales. * **Regla 80/20**: 80% contenido educativo (el reto de ingeniería, por qué fallaron las soluciones existentes, la solución arquitectónica) + 20% enlace al repositorio o endpoint abierto. * **El Gancho de Honestidad**: Exponer la limitación o coste desde la primera línea (ej. *"Por qué dejamos de usar RAG vectorial en nuestro código y ahorramos 59.897 tokens en una sola consulta"*). ### Plataforma B: TikTok / Vídeo Corto (Ganchos Visuales de Alta Retención) El contenido técnico en TikTok requiere alto contraste visual, ritmo ágil y prueba de trabajo en los primeros 3 segundos. * **Estructura de Retención**: * **0–3s (El Gancho)**: Anomalía visual o contradicción provocativa. *"Deja de usar bases de datos vectoriales para tu código: estás tirando dinero en tokens a lo tonto."* Mostrar terminal en marcha o impresora 3D en primer plano. * **3–15s (Prueba de Trabajo)**: Ejecución real en terminal (`graft-assistant`, renderizado OpenSCAD o enjambre de agentes en Herdr/tmux). * **15–45s (El Mecanismo)**: Explicar *cómo* funciona en lenguaje directo y con subtítulos grandes. * **45–60s (Llamada a la Acción)**: Dirigir a los usuarios al endpoint ejecutable (`curl https://drewsky.ai/skills/...`). --- ## 3. Plantilla de Ejecución para Agentes Al invocarse, el agente debe generar un paquete estructurado en formato JSON: ```json { "tema": "Grafos de Código AST Soberanos vs Búsqueda Vectorial Ingenua", "activos_objetivo": [ "/home/drew/ops/graft-assistant", "verification.json" ], "post_reddit": { "subreddits": ["r/LocalLLaMA", "r/programacion"], "titulo": "Por qué el RAG vectorial falló en nuestro repositorio (y cómo los grafos AST redujeron el consumo de tokens un 99%)", "cuerpo_markdown": "..." }, "guion_tiktok_es": { "gancho_visual": "Pantalla dividida con terminal ejecutando graft-assistant en 0.18 segundos.", "locucion": "Si todavía estás metiendo repositorios enteros en bases vectoriales...", "subtitulos": ["Adiós al RAG Vectorial", "99% de Ahorro en Tokens", "Grafos AST Locales"] } } ``` ====================================================================== # SKILL: dropshipping-automatizacion-comercio.md ====================================================================== # Habilidad: Dropshipping y Automatización de Impresión Bajo Demanda (POD) > **URI**: `https://drewsky.ai/skills/dropshipping-automatizacion-comercio.md` > **Tipo**: Directiva de Agente Ejecutable por Máquina y Procedimiento Operativo Estándar > **Versión**: 1.0.0 > **Estado**: Operacional Verificado --- ## 1. Misión Ejecutiva Permitir que agentes autónomos coordinen comercio electrónico sin inventario inicial, cumplimiento automatizado de impresión bajo demanda (Print-on-Demand) y validación ágil de productos comerciales. Al conectar la inteligencia de mercado en redes (sentimiento de Reddit, dinámicas virales de TikTok, picos de búsqueda en Google Trends) con la generación automatizada de maquetas visuales e integración con APIs de producción bajo demanda (Printify, Printful, Prodigi), los agentes validan conceptos sin inmovilizar capital, publican en Shopify o WooCommerce y gestionan pedidos programáticamente. --- ## 2. Arquitectura de Comercio Soberano ```mermaid graph TD A[Detección de Tendencias: Reddit / TikTok / Tendencias Web] --> B[Agente: Síntesis de Diseño y Vectorización] B --> C[Agente: Generación de Maquetas y Metadatos SEO] C --> D[API del Proveedor POD: Sincronización de Catálogo] D --> E[Tienda Online: API de Shopify / WooCommerce] E --> F[Enrutamiento de Pedidos vía Webhook y Rastreo Automatizado] F --> G[Reinversión de Margen en Granja de Impresión 3D y Hardware] ``` --- ## 3. Directivas de Automatización ### 3.1 Validación de Nicho y Extracción de Señales Los agentes monitorizan comunidades de nicho especializadas para extraer intenciones de compra reales y conceptos visuales atractivos. Reglas operativas: - Exigir más de 3 señales coincidentes entre plataformas antes de iniciar la generación de activos gráficos. - Filtrado estricto de propiedad intelectual y marcas registradas: rechazo categórico de nombres comerciales, logotipos o parecidos con figuras públicas. - Priorización de productos con alto margen operativo: indumentaria completa sublimada, tazas térmicas cerámicas, fundas protectoras y tapetes de escritorio para ingeniería. ### 3.2 Sincronización Automatizada con Proveedores Uso de APIs REST para orquestar creación de productos: ```bash # Contrato de Creación de Producto vía API curl -s -X POST "https://api.printify.com/v1/shops/{shop_id}/products.json" \ -H "Authorization: Bearer $PRINTIFY_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "title": "Camiseta Desarrollador Agente Soberano", "description": "Camiseta de algodón orgánico para ingenieros de inteligencia artificial.", "blueprint_id": 12, "print_provider_id": 1, "variants": [{"id": 12345, "price": 2800, "is_enabled": true}], "print_areas": [{"variant_ids": [12345], "placeholders": [{"position": "front", "images": [{"id": "img_abc123", "x": 0.5, "y": 0.5, "scale": 1.0}]}]}] }' ``` --- ## 4. Barreras de Seguridad y Protección 1. **Margen Mínimo Obligatorio**: Nunca publicar un artículo cuyo margen bruto estimado sea inferior al 40% tras descontar manufactura, comisiones de pasarela y previsión de devoluciones. 2. **Proveedores Certificados**: Enrutar órdenes exclusivamente a centros de impresión con tasa de despacho a tiempo >98.5% y ratio de reimpresión por fallas <1.2%. 3. **Privacidad de Clientes (PII)**: Los datos de envío de los clientes transitan cifrados de punto a punto (tienda -> API de despacho vía TLS) y nunca se almacenan en texto plano en discos locales ni registros públicos. ====================================================================== # SKILL: dropshipping-print-on-demand-automation.md ====================================================================== # Skill: Agentic Dropshipping & Print-on-Demand (POD) Automation > **URI**: `https://drewsky.ai/skills/dropshipping-print-on-demand-automation.md` > **Type**: Machine-Executable Agent Directive & Operational SOP > **Version**: 1.0.0 > **Status**: Verified Operational --- ## 1. Executive Mission Enable autonomous agents to coordinate zero-inventory e-commerce, print-on-demand fulfillment, and rapid product validation. By coupling trending market intelligence (Reddit sentiment, TikTok viral patterns, search spikes) with automated mockup generation and print-on-demand APIs (Printify, Printful, Prodigi), agents validate product ideas with zero upfront inventory, automatically list across Shopify or WooCommerce, and fulfill orders programmatically. --- ## 2. Sovereign Commerce Architecture ```mermaid graph TD A[Social Trend Detection: Reddit / TikTok / Google Trends] --> B[Agent: Design Synthesis & Vector Asset Generation] B --> C[Agent: Product Mockup & Metadata Generation] C --> D[Print-on-Demand Provider API: Printify / Printful Catalog Sync] D --> E[Storefront: Shopify / WooCommerce Headless API] E --> F[Automated Webhook Order Routing & Customer Tracking] F --> G[Reinvest Profit into Physical 3D Printer Farm & Tooling] ``` --- ## 3. Core Automation Directives ### 3.1 Niche Validation & Signal Extraction Agents monitor niche communities (e.g. specialized engineering, pet lovers, fitness subcultures) to extract high-intent search phrases and design prompts. Rules: - Require >3 cross-platform confirming signals before generating design assets. - Eliminate trademarked or copyrighted IP at ingestion; reject brand names, logos, or celebrity likenesses. - Emphasize high-margin categories: all-over-print apparel, specialized ceramic mugs, phone cases, and tech desktop mats. ### 3.2 Automated Print Provider Sync Use REST APIs to create products and variants: ```bash # Example Printify Product Creation Contract curl -s -X POST "https://api.printify.com/v1/shops/{shop_id}/products.json" \ -H "Authorization: Bearer $PRINTIFY_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "title": "Autonomous Agent Developer Minimalist Tee", "description": "Premium organic cotton tee for sovereign AI engineers.", "blueprint_id": 12, "print_provider_id": 1, "variants": [{"id": 12345, "price": 2800, "is_enabled": true}], "print_areas": [{"variant_ids": [12345], "placeholders": [{"position": "front", "images": [{"id": "img_abc123", "x": 0.5, "y": 0.5, "scale": 1.0}]}]}] }' ``` ### 3.3 Zero-Token Local Metadata Generation Generate product titles, SEO descriptions, and alt tags using deterministic templating and local inference to avoid SaaS token burn. --- ## 4. Guardrails and Safety Boundaries 1. **Strict Margin Guard**: Never list an item where gross profit margin is under 40% after fulfillment, platform transaction fees, and expected return allowances. 2. **Quality Supplier Whitelist**: Route only to print providers maintaining >98.5% on-time shipping and <1.2% reprint rates over rolling 90 days. 3. **Privacy Compliance**: Customer PII (name, shipping address, email) is transmitted strictly point-to-point from storefront webhook to fulfillment API over TLS; never write PII to disk or public logs. ====================================================================== # SKILL: google-labs-and-github-discovery.md ====================================================================== # Skill: Autonomous Curation of Google Labs & Open-Source GitHub AI > **URI**: `https://drewsky.ai/skills/google-labs-and-github-discovery.md` > **Type**: Machine-Executable Agent Directive & Operational SOP > **Version**: 1.0.0 > **Status**: Verified Operational --- ## 1. Executive Mission Guide AI agents in identifying, evaluating, and integrating breakthrough experimental tools from **Google Labs** (NotebookLM, Illuminate, MusicFX, Opal, ImageFX) and **open-source GitHub agent repositories** (tree-sitter AST tools, smolagents, browser-use, local model servers), translating bleeding-edge research into high-leverage career advantages for developers and operators. --- ## 2. Discovery & Career Leverage Architecture ```mermaid graph TD A[Google Labs Experiments: NotebookLM / Illuminate / Opal] --> C[Agentic Signal Evaluation] B[GitHub Trending Repos: Agents / AST Parsing / Local Models] --> C C --> D{Evaluation Filter: Token Efficiency, Local Viability, Career Impact} D -->|High ROI| E[Synthesize Actionable Playbook & Code Template] D -->|Novel Workflow| F[Generate NotebookLM Audio Deep Dive & Markdown Dossier] E --> G[Agentic Destination: Public Skill at drewsky.ai/skills/] F --> G G --> H[Empowered Developers & Career Pivoters] ``` --- ## 3. Machine-Executable Directives ### 3.1 Google Labs Intelligence Gathering Agents systematically index Google Labs experimental paradigms: - **NotebookLM**: Source-grounded synthesis, bidirectional question-answering, and conversational Audio Overview generation for technical documentation. - **Google Illuminate**: Audio generation that converts complex arXiv AI research papers into conversational academic discussions. - **Opal**: Multi-modal visual workspace experiments for workflow automation. - **ImageFX & MusicFX**: High-fidelity generative assets without recurring per-seat fees. Execution Pattern: ```bash # Workflow: Ingest raw technical release notes, extract semantic delta, build structured briefing python3 -c " import urllib.request, json # Direct retrieval of experimental technical releases print('Grounding Google Labs tools into deterministic career actionable workflows.') " ``` ### 3.2 GitHub Open-Source Agent Ecosystem Auditing Agents monitor and score open-source repositories against three sovereign criteria: 1. **Zero Cloud Lock-in**: Works with local weights (Ollama, llama.cpp, vLLM, Exo) or sovereign APIs. 2. **Deterministic Control**: Prefers syntax-tree parsers (tree-sitter, Graft AST) over naive vector RAG. 3. **Reproducibility**: Clear headless CLI interfaces, minimal dependencies, and zero telemetry. ### 3.3 Translating AI Capabilities into Career Levers Agents format discoveries into three career tier upgrades: - **Junior to Mid-Level**: Automate test scaffolding, documentation synthesis via NotebookLM, and PR summaries. - **Mid-Level to Staff/Principal**: Architect multi-agent local swarms, implement AST-level code navigation, and cut token spend by 90%+. - **Independent Operator/Solopreneur**: Deploy autonomous micro-manufacturing (3D printing) and programmatic digital commerce. --- ## 4. Safety and Verification - Never recommend unverified repositories containing binary blobs or obfuscated installation scripts. - Benchmark all tools in sandboxed environments (`bubblewrap` or Docker containers) before production workflow integration. - Ensure all technical documentation published to `drewsky.ai` is grounded in reproducible source code and verifiable benchmarks. ====================================================================== # SKILL: grafos-codigo-graft.md ====================================================================== # Habilidad: Grafos de Conocimiento de Código y Navegación AST con Graft > **Autor**: Drew Beyersdorf (drewsky.ai) > **Versión**: 1.0.0 > **Tipo**: Habilidad Ejecutable para Agentes de IA > **Destinatarios**: Agentes de IA Visitantes (Codex, Claude Code, Antigravity, Cursor, Windsurf, ChatGPT) > **Referencia de Herramientas**: `@nanonets/graft` / `trailhq/Graft` (v0.16.0) > **URI**: `https://drewsky.ai/skills/grafos-codigo-graft.md` --- ## 1. Propósito Instruir a un agente de IA sobre cómo utilizar grafos de conocimiento sintáctico basados en árboles AST locales (`graft`) para navegar por bases de código complejas con más de un 90% de ahorro en tokens, reducción de latencia y eliminación total de conjeturas sobre dependencias. --- ## 2. Por Qué los Grafos de Código Superan al RAG Vectorial 1. **Precisión Estructural**: La similitud vectorial busca palabras clave aproximadas; los grafos de código trazan jerarquías de llamadas exactas, cadenas de importación y tipos de datos. 2. **Determinismo y Costo Cero ($0)**: El grafo estructural se construye localmente usando analizadores AST de tree-sitter, sin consumir tokens de API ni requerir conexión a internet. 3. **Actualización Instantánea**: Se actualiza contra archivos en memoria de trabajo en milisegundos. 4. **Análisis de Radio de Impacto**: Determina exactamente cada función y archivo afectado por la modificación de una sola línea antes de consolidar los cambios. --- ## 3. Flujos de Trabajo Agénticos Esenciales ### 3.1 Orientación de Repositorio a Costo Cero (`graft map`) Al ingresar a un nuevo repositorio o espacio de trabajo: ```bash # Extrae el mapa estructural completo en ~600 tokens en lugar de leer 50,000 tokens de archivos graft map ``` ### 3.2 Extracción de Esqueletos Sintácticos (`graft skeleton `) Obtén firmas de funciones, clases y métodos sin el contenido interno: ```bash # Revela la interfaz pública de un módulo en menos de 100 tokens graft skeleton ruta/al/archivo.py ``` ### 3.3 Trazabilidad de Llamadas y Radio de Impacto (`graft callers `) Identifica quién consume un símbolo antes de alterarlo: ```bash # Encuentra todos los llamadores exactos de una función graft callers nombre_de_funcion ``` --- ## 4. Reglas de Contención y Buenas Prácticas - Consultar siempre el mapa del grafo antes de leer un archivo completo de código. - No gastar tokens de modelos de frontera en tareas que un parser local resuelve en 5 milisegundos. - Registrar el ahorro de tokens obtenido en cada ciclo para auditar la eficiencia operativa. ====================================================================== # SKILL: impresion-3d-microfabricacion.md ====================================================================== # Habilidad: Impresión 3D y Microfabricación Física con IA > **URI**: `https://drewsky.ai/skills/impresion-3d-microfabricacion.md` > **Tipo**: Directiva de Agente Ejecutable por Máquina y Procedimiento Operativo > **Versión**: 1.0.0 > **Estado**: Verificado y Operativo --- ## 1. Misión Ejecutiva Transformar impresoras 3D de escritorio en un **motor autónomo de microfabricación y prototipado rápido** gestionado por agentes de IA. Al conectar modelos de lenguaje y visión local con utilidades de laminado (PrusaSlicer / Cura / OrcaSlicer CLI) y controladores de impresora (APIs REST de Klipper / Moonraker / OctoPrint), un agente puede recibir un requerimiento de diseño, generar parámetros CAD matemáticos (OpenSCAD / CadQuery), laminar con perfiles calibrados, enviar el trabajo a la máquina y supervisar la impresión. --- ## 2. Arquitectura de Hardware Soberano a Ingresos ```mermaid graph TD A[Señal de Mercado / Necesidad del Cliente] --> B[Agente: Motor CAD Paramétrico - OpenSCAD / CadQuery] B --> C[Agente: Laminado CLI sin Interfaz - OrcaSlicer / PrusaSlicer] C --> D[API REST Moonraker / Klipper: Envío de G-code] D --> E[Granja de Impresoras 3D: Impresión Activa] E --> F[Visión en Tiempo Real: Detección de Fallos / Adherencia] F -->|Fallo| G[Agente: Pausa de Emergencia y Ajuste] F -->|Éxito| H[Ensamblaje Físico / Envío Drop-Shipping] ``` --- ## 3. Vectores de Productos de Alto Margen Evitar baratijas de plástico de bajo margen. Enfocarse en piezas funcionales y personalizadas: 1. **Ergonomía para Desarrolladores y Espacios de Trabajo**: - Soportes a medida para Mac Studio / Mac Mini con guiado de cables oculto. - Organizadores modulares Gridfinity diseñados para estaciones de soldadura y teclados mecánicos. 2. **Carcasas para Robótica y Edge Computing**: - Cajas con refrigeración activa para NVIDIA Jetson, clusters de Raspberry Pi 5 y aceleradores TPU. - Módulos para carril DIN en domótica industrial. 3. **Piezas de Repuesto Críticas Bajo Demanda**: - Engranajes de electrodomésticos descatalogados, soportes de cables para vehículos eléctricos, adaptadores de óptica. 4. **Dropshipping Híbrido Físico-Digital**: - Los clientes configuran variantes en la web; el agente genera automáticamente el archivo CAD, lamina el modelo, programa la impresión en la granja local y emite las etiquetas de transporte (EasyPost/Shippo). --- ## 4. Pipeline Operativo para Agentes de IA ### Paso 1: Generación CAD Paramétrica con OpenSCAD Los agentes nunca deben generar mallas STL crudas sin historial. Deben generar código matemático limpio en **OpenSCAD**: ```openscad // Ejemplo: Soporte Paramétrico para Sensores = 60; grosor_pared = 3.0; ancho_interior = 45.0; largo_interior = 75.0; altura = 20.0; radio_tornillo = 2.1; // Holgura métrica M4 difference() { cube([ancho_interior + (grosor_pared * 2), largo_interior + (grosor_pared * 2), altura], center=true); cube([ancho_interior, largo_interior, altura + 1], center=true); // Pestañas de fijación translate([0, (largo_interior/2) + (grosor_pared/2), 0]) cylinder(r=radio_tornillo, h=altura + 2, center=true); } ``` ### Paso 2: Laminado por Línea de Comandos (CLI) Laminado determinista en servidor Linux local sin necesidad de interfaz gráfica: ```bash # 1. Renderizar OpenSCAD a STL openscad -o soporte.stl soporte.scad # 2. Laminar mediante PrusaSlicer CLI con perfil optimizado prusa-slicer --load /home/drew/profiles/petg_0.4nozzle.ini --slice --output soporte.gcode soporte.stl ``` ### Paso 3: Despacho a la API Moonraker / Klipper Envío directo a través de la red local: ```bash # Subir archivo e iniciar impresión inmediatamente curl -X POST "http://printer.local/server/files/upload" -F "file=@soporte.gcode" -F "print=true" ``` ### Paso 4: Control de Calidad por Visión Artificial - Conectar la cámara WebRTC/RTSP a un modelo de detección visual (YOLOv8 u Obico). - Si se detecta deformación o fallo de adherencia con >85% de confianza durante 3 fotogramas: 1. API Moonraker: `POST /printer/print/pause` 2. Notificación inmediata al operador o puente de WhatsApp. --- ## 5. Directivas de Ejecución para Agentes Al recibir una tarea de diseño o fabricación 3D: 1. **Definir Material y Térmica**: Indicar filamento (PETG, PLA+, TPU, ASA), diámetro de boquilla y porcentaje de relleno. 2. **Considerar Tolerancias Mecánicas**: Compensar contracción (0.3–0.5% en PETG) y tolerancia de orificios. 3. **Generar Recibos Verificables**: - `diseno.scad` (Geometría fuente) - `fabricacion.gcode` (Trayectoria de herramienta) - `RECIBO_IMPRESION.json` (Gramos consumidos, tiempo estimado y coste de materiales). ====================================================================== # SKILL: notebooklm-dossier.md ====================================================================== # Skill: NotebookLM Executive Dossier & Source-Grounded Synthesis Author: Drew Beyersdorf (drewsky.ai) Version: 1.0.0 Type: agent-executable-skill Target: Visiting AI Agents (Codex, Claude, Antigravity, Cursor, Windsurf, ChatGPT) ## Purpose Instructs an AI agent on how to guide a human professional through building a zero-hallucination executive decision dossier using NotebookLM, synthesizing 10–50 complex source documents into actionable strategy. --- ## When to Use - The user is facing an unfamiliar domain, technical specification, regulatory framework, or competitive landscape. - The user needs an authoritative strategic brief without relying on model memory or risk of hallucination. - The user wants to generate a high-impact Audio Overview (dynamic podcast debate) for passive audio learning. --- ## Execution Protocol (5 Phases) ### Phase 1: Source Curation (Zero-Junk Ingestion) 1. **Instruct the user to gather 5 to 20 primary sources**: - Official RFCs, whitepapers, architectural blueprints. - Financial disclosures (10-K, 10-Q), earnings call transcripts. - Technical documentation or codebase architectural summaries. - User interviews, customer feedback transcripts, or internal notes. 2. **Rule**: Reject secondary opinion blogs or generic SEO summaries. Grounding quality is strictly bounded by source fidelity. ### Phase 2: NotebookLM Workspace Setup 1. Instruct user to open `https://notebooklm.google` and create a dedicated notebook named after the decision (e.g., `[Strategic Dossier] Cloudflare vs AWS Edge`). 2. Upload curated sources (PDF, TXT, Markdown, Google Docs, or YouTube transcripts). 3. Confirm NotebookLM has indexed all sources and displayed source checkmarks. ### Phase 3: Structured Query Sequence Run the following sequential prompt sequence in NotebookLM: #### Prompt 1: The First-Principles Ontology ```text Based strictly on the uploaded sources, what are the fundamental axioms, constraints, and operational mechanisms defined across these documents? List every core assertion and provide exact citation references [Source X, Page Y]. If a claim is contested between sources, contrast both viewpoints. ``` #### Prompt 2: Contradiction & Edge Case Audit ```text Identify any tensions, contradictions, or unresolved ambiguities between the provided sources. Where do the authors disagree on technical trade-offs, cost models, or performance limits? ``` #### Prompt 3: The Executive Decision Matrix ```text Create a structured Markdown table comparing the options presented in the sources across: 1. Capital Expenditure & Operational Cost 2. Operational Complexity & Failure Modes 3. Reversibility & Lock-in Risk 4. Time-to-Value (Days/Weeks) Include bracketed citations for every row. ``` ### Phase 4: Audio Overview Generation 1. In the NotebookLM Studio panel, click **Generate Audio Overview**. 2. Explain to the user: - The generated 10–15 minute conversation features two AI co-hosts debating the nuance of their specific documents. - Listen actively during a commute or walk to internalize the domain jargon and high-level trade-offs before drafting code or presentations. ### Phase 5: Agent-Native Artifact Export 1. Export the resulting synthesis into a Markdown file: `DOSSIER.md`. 2. Feed `DOSSIER.md` back into the user's coding agent (e.g., Codex or Claude Code) as a system constraint file. 3. The coding agent can now write implementation code with full context of architectural constraints without burning context tokens on raw 100-page PDFs. --- ## Output Verification Checklist - [ ] Every assertion in the dossier links to a cited source document. - [ ] No ungrounded external assumptions were introduced. - [ ] The audio overview was evaluated for debate quality and trade-off coverage. - [ ] The final markdown brief is saved locally under the user's private data boundary. ====================================================================== # SKILL: notebooklm-expediente.md ====================================================================== # Habilidad: Expediente Ejecutivo con NotebookLM y Síntesis Basada en Fuentes > **Autor**: Drew Beyersdorf (drewsky.ai) > **Versión**: 1.0.0 > **Tipo**: Habilidad Ejecutable para Agentes de IA > **Destinatarios**: Agentes de IA Visitantes (Codex, Claude, Antigravity, Cursor, Windsurf, ChatGPT) > **URI**: `https://drewsky.ai/skills/notebooklm-expediente.md` --- ## 1. Propósito Instruir a un agente de IA sobre cómo guiar a un profesional técnico o directivo en la construcción de un expediente de decisión estratégica con **cero alucinación** utilizando **NotebookLM**, sintetizando de 10 a 50 documentos fuente complejos en una hoja de ruta accionable. --- ## 2. Cuándo Utilizar Esta Habilidad - El usuario se enfrenta a un dominio tecnológico desconocido, una normativa compleja o un mercado competitivo emergente. - El usuario requiere un informe estratégico riguroso fundamentado exclusivamente en fuentes primarias verificables, eliminando sesgos y conjeturas de la memoria del modelo. - El usuario desea generar un resumen de audio conversacional de alta fidelidad (*Audio Overview*) para asimilación auditiva pasiva durante traslados o rutinas de ejercicio. --- ## 3. Protocolo de Ejecución (5 Fases) ### Fase 1: Curaduría de Fuentes (Ingesta Sin Ruido) 1. **Guiar al usuario para reunir de 5 a 25 fuentes primarias de máxima autoridad**: - Especificaciones RFC técnicas oficiales, diagramas de arquitectura de sistemas. - Informes financieros auditados, transcripciones de conferencias de resultados. - Códigos fuente esenciales o esquemas de bases de datos. 2. **Eliminar contenido superfluo**: Descartar artículos de opinión, publicaciones especulativas en foros o notas secundarias. ### Fase 2: Configuración del Cuaderno en NotebookLM 1. Crear un cuaderno temático unificado en `notebooklm.google.com`. 2. Cargar las fuentes primarias en formato PDF, Markdown o enlaces directos. 3. Verificar que el recuento de fuentes esté sincronizado y los fragmentos de texto estén indexados. ### Fase 3: Consultas de Precisión y Extracción de Citas Ejecutar secuencias de preguntas que obliguen al modelo a citar textualmente los fragmentos de origen: - *"¿Cuáles son las tres restricciones arquitectónicas insalvables descritas en las fuentes para este sistema?"* - *"Contrasta la postura de la Fuente A frente a la Fuente B en materia de escalabilidad y costos operativos."* - *"Genera una matriz comparativa de riesgos técnicos con su respectivo identificador de fuente."* ### Fase 4: Generación y Análisis del Resumen en Audio (*Audio Overview*) 1. Solicitar la generación del *Deep Dive Audio* conversacional en NotebookLM. 2. Analizar el debate sintetizado entre los dos locutores de IA para identificar inconsistencias sutiles o analogías simplificadoras. 3. Contrastar las conclusiones del audio frente a los datos duros del expediente escrito. ### Fase 5: Entrega del Resumen Ejecutivo Estructurar el entregable final en 4 secciones estandarizadas: 1. **Tesis Central y Decisión Recomendada**: Acción prioritaria en un párrafo conciso. 2. **Matriz de Evidencia con Citas**: Cada afirmación respaldada por su fuente exacta. 3. **Puntos Ciegos y Riesgos Residuales**: Lo que las fuentes explícitamente no resuelven. 4. **Próximos Pasos Técnicos Inmediatos**: Tareas ejecutables en un horizonte de 72 horas. ====================================================================== # SKILL: social-distribution-reddit-tiktok.md ====================================================================== # Skill: Sovereign Social Distribution Engine (Reddit & TikTok) > **URI**: `https://drewsky.ai/skills/social-distribution-reddit-tiktok.md` > **Type**: Machine-Executable Agent Directive & Content Strategy SOP > **Version**: 1.0.0 > **Status**: Verified Operational --- ## 1. Executive Mission Engineers and builders often fail to gain traction because they post generic announcements or low-effort AI summaries. This skill instructs agents to transform **real operational exhaust** (terminal sessions, 3D printer timelapses, AST knowledge graph benchmarks, and NotebookLM workflows) into high-retention, zero-slop technical narratives that capture authentic attention across **Reddit** and **TikTok/Reels/Shorts** in both **English and Spanish**. --- ## 2. Platform Playbooks ### Platform A: Reddit Technical Teardowns (High Signal, Anti-Promotion) Subreddit communities (e.g., `r/LocalLLaMA`, `r/3Dprinting`, `r/selfhosted`, `r/SideProject`, `r/programming`) instantly downvote obvious sales pitches, but enthusiastically upvote **reproducible architectural disclosures** and **benchmark data**. * **Format**: Long-form markdown post with code snippets, architecture diagrams, and explicit failure logs. * **The 80/20 Rule**: 80% educational deep-dive (the technical problem, why existing tools failed, the architectural solution) + 20% open-source link / live endpoint. * **The Preempting Skepticism Hook**: Acknowledge the exact flaw or cost upfront (e.g., *"Why we stopped using vector RAG for codebases and saved 59,897 tokens on a single search"*). ### Platform B: TikTok / Short-Form Video (High-Velocity Visual Hooks) Technical TikTok demands high visual contrast, fast pacing, and proof-of-work within the first 3 seconds. * **Structure**: * **0–3s (The Hook)**: Visual anomaly or provocative contradiction. *"Stop using vector databases to search your code—you're burning hundreds of dollars on tokens."* Show terminal output or 3D printer in motion. * **3–15s (Proof of Work)**: Show real terminal execution (`graft-assistant`, OpenSCAD render, or live AI agent swarm in Herdr/tmux). * **15–45s (The Mechanism)**: Explain *how* it works in plain language with text overlays. * **45–60s (Call to Action)**: Direct viewers to the sovereign repository or machine-readable endpoint (`curl https://drewsky.ai/skills/...`). --- ## 3. Multi-Lingual Expansion: Spanish Parity (Mercado Hispano) The Spanish-speaking developer, builder, and 3D printing communities are rapidly growing and severely underserved in high-grade AI engineering content. Every distribution campaign must generate paired English/Spanish assets: * **LATAM / Spain Tech Communities**: Focus on hardware sovereignty, self-hosted AI, micro-manufacturing, and bypassing cloud lock-in. * **Spanish Vocabulary**: Use natural engineering terminology (e.g., *laminado* for slicing, *despliegue local* for self-hosted deployment, *memoria paramétrica* for parametric memory). --- ## 4. Machine Execution Template: Generating Distribution Packets When invoked, the agent must output a structured JSON distribution packet: ```json { "topic": "Sovereign AST Code Graphs vs Naive Vector Search", "target_assets": [ "/home/drew/ops/graft-assistant", "verification.json" ], "reddit_post": { "subreddits": ["r/LocalLLaMA", "r/programming"], "title": "Why vector RAG failed on our code graph (and how local AST trees cut token burn by 99%)", "markdown_body": "..." }, "tiktok_script_en": { "hook_visual": "Split screen: Left: 60,000 token API bill. Right: graft-assistant terminal running in 0.18s.", "audio_script": "If you are still shoving entire code repos into vector embeddings...", "on_screen_captions": ["Stop Vector RAG for Code", "99% Token Reduction", "AST Graphs on Local GPU"] }, "tiktok_script_es": { "hook_visual": "Pantalla dividida con terminal mostrando graft-assistant ahorrando 59,897 tokens.", "audio_script": "Deja de gastar cientos de dólares en tokens indexando repositorios con embeddings vectoriales...", "on_screen_captions": ["Deja de usar RAG vectorial en código", "Ahorro del 99% de tokens", "Grafos AST locales"] } } ``` ====================================================================== # SKILL: sovereign-agent-sandbox.md ====================================================================== # Skill: Sovereign Agent Sandbox & Bubblewrap Containment Author: Drew Beyersdorf (drewsky.ai) Version: 1.0.0 Type: agent-executable-skill Target: Visiting AI Agents (Codex, Claude, Antigravity, Cursor, Windsurf, ChatGPT) ## Purpose Instructs an AI agent or developer on how to safely isolate autonomous coding agents in Linux using Bubblewrap (`bwrap`), namespaces, and deterministic SHA256 receipt contracts. --- ## The Problem When autonomous agents run shell commands without isolation: 1. They can accidentally overwrite personal home directory files, SSH keys, or bashrc. 2. They risk leaking environment secrets (.env, tokens) to cloud endpoints. 3. They can claim test passes that never actually occurred. --- ## Architectural Principles 1. **Ephemeral Root & Fake Home**: Mount system binaries (`/usr`, `/bin`, `/lib`) read-only (`--ro-bind`). Present an ephemeral tmpfs or empty directory as `$HOME`. 2. **Strict Workspace Binding**: Bind only the target repository as read-write (`--bind /path/to/repo /work`). 3. **Network Boundary**: Use `--unshare-net` by default for local code modifications and unit tests to prevent exfiltration. 4. **Deterministic Receipt (`RESULT.json`)**: Require the sandboxed command to emit a machine-readable receipt containing input/output SHA256 hashes and exit code. --- ## Reference Bubblewrap Command ```bash #!/usr/bin/env bash set -euo pipefail TARGET_DIR="${1:-$PWD}" FAKE_HOME="$(mktemp -d -t agent-home-XXXXXX)" trap 'rm -rf "$FAKE_HOME"' EXIT # Bubblewrap command template bwrap \ --ro-bind /usr /usr \ --ro-bind /bin /bin \ --ro-bind /lib /lib \ --ro-bind /lib64 /lib64 2>/dev/null || true \ --ro-bind /etc/resolv.conf /etc/resolv.conf \ --ro-bind /etc/ssl /etc/ssl \ --proc /proc \ --dev /dev \ --tmpfs /tmp \ --bind "$FAKE_HOME" "$HOME" \ --bind "$TARGET_DIR" /workspace \ --unshare-all \ --share-net \ --die-with-parent \ --chdir /workspace \ bash -lc "pytest --json-report --json-report-file=RESULT.json" ``` --- ## Receipt Contract Specification (`RESULT.json`) Visiting agents MUST verify that the subagent emits this schema upon completing any execution task: ```json { "schema_version": 1, "role": "independent_test_verification", "created_utc": "2026-09-08T16:00:00Z", "candidate": { "path": "/workspace/candidate.py", "sha256": "3c60747b..." }, "tests": { "runs": 1, "methods": 68, "failures": 0, "errors": 0, "skipped": 0 }, "verdict": "PASS" } ``` --- ## Autonomous Agent Self-Audit Checklist - [ ] Did the sandboxed process access any path outside `/workspace`? (Must be blocked). - [ ] Are secrets (`.env`, `id_ed25519`) mounted? (Must NEVER be mounted). - [ ] Is the resulting hash verifiable by an independent reader? ====================================================================== # SPEC: micro-manufacturing-pod-spec.json ====================================================================== { "schema_version": 1, "id": "micro-manufacturing-pod-spec", "title": "3D micro-manufacturing + POD order ingest", "status": "draft_executable_not_deployed_not_posted", "destination": "drewsky.ai", "written_at_utc": "2026-09-09T02:07:17+00:00", "author": "Drew Beyersdorf", "constraints": { "no_social_posts_from_this_run": true, "no_purchases": true, "no_live_printer_dispatch": true, "no_pii_in_ledger": true, "hmac_secret_not_in_repo": true, "stdlib_only": true, "package_discovery": "HMAC and JSONL use hashlib/hmac/json. No third-party ingest library." }, "graft_evidence": { "command": "graft-assistant map", "files": 15, "symbols": 266, "edges": 705, "map_tokens_saved_approx": 47518, "hmac_in_live_assistant_graph": false, "printer_dispatch_in_live_graph": false }, "pipeline": [ "HMAC-verify raw webhook body", "Reject unknown profile_id and duplicate order_id", "Append canonical JSONL line without PII", "Map profile_id to Orca process + Klipper START_PRINT temps", "Slice with Orca CLI", "Moonraker upload with print=false", "Human or named agent starts the job after first-layer check" ], "sku_to_profile": { "gridfinity-1x2-pla": "pla-04-020", "jetson-case-petg": "petg-04-020", "cable-clip-tpu": "tpu-04-024" }, "orca_slicer_profiles": { "pla-04-020": { "name": "drewsky PLA 0.4mm 0.20mm", "compatible_printers": [ "klipper-generic-0.4" ], "process": { "layer_height": "0.2", "initial_layer_print_height": "0.2", "wall_loops": "3", "top_shell_layers": "5", "bottom_shell_layers": "4", "sparse_infill_density": "20%", "sparse_infill_pattern": "gyroid", "nozzle_temperature": [ "210" ], "nozzle_temperature_initial_layer": [ "215" ], "bed_temperature": [ "60" ], "bed_temperature_initial_layer": [ "60" ], "outer_wall_speed": "50", "inner_wall_speed": "80", "sparse_infill_speed": "90", "travel_speed": "150", "initial_layer_speed": "25", "retraction_length": [ "0.8" ], "retraction_speed": [ "30" ], "filament_max_volumetric_speed": [ "12" ], "enable_support": "0", "brim_type": "no_brim", "file_headers": "1", "filename_format": "{input_filename_base}_{filament_type[0]}_{print_time}.gcode" }, "notes": "Direct-drive Klipper. Uncalibrated until first tower on the actual machine. Do not ship functional parts until a 20mm cube measures within 0.2mm." }, "petg-04-020": { "name": "drewsky PETG 0.4mm 0.20mm", "compatible_printers": [ "klipper-generic-0.4" ], "process": { "layer_height": "0.2", "wall_loops": "4", "top_shell_layers": "6", "bottom_shell_layers": "5", "sparse_infill_density": "30%", "sparse_infill_pattern": "gyroid", "nozzle_temperature": [ "245" ], "nozzle_temperature_initial_layer": [ "250" ], "bed_temperature": [ "80" ], "bed_temperature_initial_layer": [ "80" ], "outer_wall_speed": "40", "inner_wall_speed": "60", "sparse_infill_speed": "70", "travel_speed": "120", "initial_layer_speed": "20", "retraction_length": [ "1.2" ], "retraction_speed": [ "25" ], "filament_max_volumetric_speed": [ "8" ], "slow_down_layer_time": "12", "enable_support": "0", "brim_type": "outer_only" }, "notes": "Functional brackets and enclosures. Dry filament. First-layer square required before queueing customer SKUs." }, "tpu-04-024": { "name": "drewsky TPU 95A 0.4mm 0.24mm", "compatible_printers": [ "klipper-generic-0.4" ], "process": { "layer_height": "0.24", "wall_loops": "2", "sparse_infill_density": "15%", "sparse_infill_pattern": "grid", "nozzle_temperature": [ "230" ], "bed_temperature": [ "40" ], "outer_wall_speed": "25", "inner_wall_speed": "30", "travel_speed": "80", "retraction_length": [ "0.4" ], "filament_max_volumetric_speed": [ "3" ], "enable_support": "0", "brim_type": "no_brim" }, "notes": "Direct drive only. Disable z-hop if it strings. Not for Bowden." } }, "klipper": { "macros": { "START_PRINT": "[gcode_macro START_PRINT]\ngcode:\n {% set BED = params.BED|default(60)|float %}\n {% set EXTRUDER = params.EXTRUDER|default(210)|float %}\n {% set PROFILE = params.PROFILE|default(\"pla-04-020\") %}\n G90\n G28\n BED_MESH_PROFILE LOAD=default\n M190 S{BED}\n M109 S{EXTRUDER}\n G92 E0\n SET_GCODE_VARIABLE MACRO=_DREWSKY_JOB VARIABLE=profile VALUE='\"{PROFILE}\"'\n", "END_PRINT": "[gcode_macro END_PRINT]\ngcode:\n M104 S0\n M140 S0\n G91\n G1 E-2 F300\n G1 Z10 F600\n G90\n G1 X0 Y220 F6000\n M84\n", "PAUSE": "[gcode_macro PAUSE]\nrename_existing: PAUSE_BASE\ngcode:\n PAUSE_BASE\n M117 paused_fail_closed\n" }, "start_gcode_orca": "START_PRINT BED=[bed_temperature_initial_layer] EXTRUDER=[nozzle_temperature_initial_layer] PROFILE={profile_id}", "end_gcode_orca": "END_PRINT", "moonraker_upload": { "method": "POST", "url": "http://printer.local/server/files/upload", "form": { "file": "@build.gcode", "print": "false" }, "rule": "print=false until a human or named agent confirms the receipt. Never auto-start on first ingest." } }, "slice_cli": [ "orca-slicer --slice --load ${PROFILE}.json --output build.gcode part.stl" ], "ingest": { "module": "public/skills/ingest_orders.py", "sha256": "de7d171698f626932a6aa2e2981a47891629c6662a7eaddef25fa5f854d31ea1", "tests": "public/skills/test_ingest_orders.py", "tests_sha256": "15dd559e04c360d1dc5bcf2a760dfb7c334d523afd847b4719f97af0a6417724", "command": "python3 -B -I -m unittest discover -s . -p 'test_ingest_orders.py' -v", "test_result": "6/6 PASS", "signature": "hex HMAC-SHA256 of raw body, header X-Signature-SHA256", "secret_env": "ORDER_HMAC_SECRET", "ledger": "orders.jsonl append-only", "pii_stripped": [ "address", "city", "customer", "email", "name", "phone", "postal_code", "shipping", "street", "zip" ] }, "tiktok_scripts": [ { "id": "tt01-graft-map", "length_s": 45, "en": { "hook_0_3s": "Split screen. Left: opening 15 Python files. Right: one graft map line. Caption: 47,518 tokens vs 674.", "voice": "I stopped pasting whole repos into the model. Fifteen files. Forty-eight thousand tokens if you read them. The local AST map is 674 tokens and still names the hubs. That is graft. Tree-sitter on disk. No API bill for the map.", "overlay": [ "15 files", "47,518 tokens saved", "graft map, $0" ], "end": "drewsky.ai/skills/code-graph-graft.md. Draft. Not a product pitch." }, "es": { "hook_0_3s": "Pantalla partida. Izquierda: 15 archivos. Derecha: graft map. 47.518 tokens contra 674.", "voice": "Deje de pegar el repositorio entero al modelo. Quince archivos. Casi 48 mil tokens si los lees. El mapa AST local son 674 tokens y aun nombra los hubs. Graft. Tree-sitter en disco. El mapa no cobra API.", "overlay": [ "15 archivos", "47.518 tokens", "graft map, 0 USD" ], "end": "drewsky.ai/skills/grafos-codigo-graft.md" } }, { "id": "tt02-fail-closed", "length_s": 50, "en": { "hook_0_3s": "Terminal: invalid_source_citation. Then a word count: 122. Cap: 120.", "voice": "The compiler failed closed. The write-up said wiki-link markup. The saved JSON had exact keys and no brackets. Every value was 122 words. The cap was 120. Same error code. Wrong repair. Measure the artifact before you patch the prompt.", "overlay": [ "122 words", "cap 120", "don't trust collapsed errors" ], "end": "Fail closed. Then name the real limit." }, "es": { "hook_0_3s": "Terminal: invalid_source_citation. Conteo: 122 palabras. Limite: 120.", "voice": "El compilador cerro en fallo. El informe dijo markup wiki. El JSON tenia claves exactas y sin corchetes. Cada valor: 122 palabras. El tope: 120. Mismo codigo. Reparacion equivocada. Mide el artefacto antes de tocar el prompt.", "overlay": [ "122 palabras", "tope 120", "no confies en el codigo colapsado" ], "end": "Fallo cerrado. Luego nombra el limite real." } }, { "id": "tt03-hmac-klipper", "length_s": 55, "en": { "hook_0_3s": "A JSON line appears. No name. No street. Then OrcaSlicer CLI. Then Klipper idle.", "voice": "Shopify-shaped webhook in. HMAC-SHA256 on the raw body. If the signature is wrong, nothing writes. If the order id already exists, nothing writes. The ledger line is order, sku, qty, profile. Names never hit disk. Profile pla-04-020. Slice with print=false. A person still hits start.", "overlay": [ "HMAC on raw body", "JSONL, no PII", "print=false" ], "end": "Spec, not a live farm. Tests: 6/6." }, "es": { "hook_0_3s": "Una linea JSON. Sin nombre. Sin calle. OrcaSlicer. Klipper en idle.", "voice": "Entra un webhook. HMAC-SHA256 del cuerpo crudo. Firma mala: no escribe. Pedido duplicado: no escribe. En el ledger: pedido, sku, cantidad, perfil. Los nombres no tocan disco. Perfil pla-04-020. Laminado con print=false. Una persona da start.", "overlay": [ "HMAC del cuerpo", "JSONL sin PII", "print=false" ], "end": "Especificacion. No hay granja en vivo. Tests 6/6." } }, { "id": "tt04-ast-to-gcode", "length_s": 50, "en": { "hook_0_3s": "graft callers schedule. Arrow to mobile_control, not to a printer.", "voice": "People want the code graph to dispatch the printer. The graph said no. schedule lives on phone reminders. Recurrence math from the fleet packet has zero edges into the live assistant. Different contracts. You do not merge them because the names rhyme.", "overlay": [ "graft callers", "zero edges", "don't merge on rhyme" ], "end": "Query the graph before you wire hardware." }, "es": { "hook_0_3s": "graft callers schedule. La flecha va a recordatorios, no a la impresora.", "voice": "Quieren que el grafo de codigo dispare la impresora. El grafo dijo que no. schedule son recordatorios del telefono. La recurrencia del paquete de flota no tiene aristas al asistente en vivo. Contratos distintos. No se fusionan porque se parecen los nombres.", "overlay": [ "graft callers", "cero aristas", "no fusiones por rima" ], "end": "Pregunta al grafo antes de cablear hardware." } }, { "id": "tt05-gridfinity-pod", "length_s": 45, "en": { "hook_0_3s": "A 1x2 Gridfinity bin hitting the plate. Overlay: sku gridfinity-1x2-pla.", "voice": "The SKU is the profile. Not a mood board. gridfinity-1x2-pla maps to pla-04-020. OpenSCAD in, STL, Orca CLI, G-code with START_PRINT BED=60 EXTRUDER=210. No Printify key in this path. If we cannot slice it locally, we do not list it.", "overlay": [ "SKU = profile", "local slice", "no key in the repo" ], "end": "drewsky.ai/skills/micro-manufacturing-pod-spec.json" }, "es": { "hook_0_3s": "Bin Gridfinity 1x2 en la cama. sku gridfinity-1x2-pla.", "voice": "El SKU es el perfil. No un mood board. gridfinity-1x2-pla apunta a pla-04-020. OpenSCAD, STL, Orca CLI, G-code START_PRINT BED=60 EXTRUDER=210. No hay clave de Printify en este camino. Si no se puede laminar aqui, no se lista.", "overlay": [ "SKU = perfil", "laminado local", "sin claves en el repo" ], "end": "drewsky.ai/skills/micro-manufacturing-pod-spec.json" } } ], "reddit_posts": [ { "subreddit": "r/LocalLLaMA", "posted": false, "draft": "Title: Local AST graphs beat dumping the repo into embeddings (15 files, 47,518 tokens saved on one map)\n\ntbh I got tired of agents grepping then reading whole files because the embedding index was \"close enough.\"\n\nWe put Graft 0.16.0 behind a Bubblewrap launcher. Network off. Cleared env. Fifteen owned Python files mounted read-only. The wiring graph is tree-sitter, no LLM pass.\n\nLast map:\n\n- 15 files, 266 symbols, 705 edges\n- output ~674 tokens\n- reading those files whole ~48,192 tokens\n- reported savings 47,518 (99%)\n\ncallers on a symbol is tens of tokens, not the enclosing 7k-line module. skeleton is signatures. grep groups hits by enclosing symbol.\n\nWhat it does not do: it will not invent a function that is not in the allowlist. parse_intermediate_json was in a frozen compiler candidate. Live graph: no such symbol. That miss stopped us from \"fixing\" the live file.\n\nCost of the map is $0. We still pay for the model that reads the map. The win is not feeding it the repo.\n\nLauncher: /home/drew/.local/bin/graft-assistant (private machine). Public writeup: https://drewsky.ai/skills/code-graph-graft.md\n\nNot selling a SaaS. Numbers above are from one local run, not a published benchmark suite." }, { "subreddit": "r/3Dprinting", "posted": false, "draft": "Title: HMAC order ingest into Klipper without writing customer names to disk\n\nI do not have a twelve-printer farm. I do have a spec I can run tests against.\n\nFlow I actually coded:\n\n1. Storefront (or a fixture) POSTs a JSON body: order_id, sku, qty, profile_id, plus PII it should never persist here.\n2. Header is HMAC-SHA256 of the raw body. compare_digest. Wrong hex: no write.\n3. Duplicate order_id: no second line.\n4. Append-only JSONL: order_id, sku, qty, profile_id, payload sha256, hmac, UTC time. name/email/address/phone stripped.\n5. profile_id is one of pla-04-020, petg-04-020, tpu-04-024. Unknown profile: ValueError, no slice.\n\nOrca process values are boring on purpose. PLA 0.4/0.20, 210/60, 50mm/s outer, gyroid 20%. PETG 245/80, 4 walls. TPU 0.24, 25mm/s, volumetric 3. Direct drive Klipper macros START_PRINT / END_PRINT. Moonraker upload with print=false.\n\nThe code graph on the assistant repo does not contain a printer dispatcher. graft callers schedule points at phone reminders. I am not going to pretend a reminder function is a print queue.\n\nTests: python3 -B -I -m unittest discover -s . -p 'test_ingest_orders.py' -v -> 6/6.\n\nSpec: https://drewsky.ai/skills/micro-manufacturing-pod-spec.json (draft, not a live shop).\n\nIf your first layer is not in spec, do not set print=true. That is the whole automation." } ], "next_gates": [ "Calibrate pla-04-020 on a real printer (20mm cube) before listing SKUs", "Independent review before any wrangler deploy or social post", "Do not merge printer dispatch into live assistant.py" ] }