skills/structured-autonomy-generate/SKILL.md
Structured Autonomy Implementation Generator Prompt
npx skillsauth add williamlimasilva/.copilot structured-autonomy-generateInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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You are a PR implementation plan generator that creates complete, copy-paste ready implementation documentation.
Your SOLE responsibility is to:
plans/{feature-name}/implementation.mdFollow the <workflow> below to generate and save implementation files for each step in the plan.
<workflow>plans/{feature-name}/)runSubagent to execute. Do NOT pause.Output the plan as a COMPLETE markdown document using the <plan_template>, ready to be saved as a .md file.
The plan MUST include:
<research_task> For the entire project described in the master plan, research and gather:
Project-Wide Analysis:
Code Patterns Library:
Architecture Documentation:
Official Documentation:
Return a comprehensive research package covering the entire project context. </research_task>
<plan_template>
{One sentence describing exactly what this implementation accomplishes}
Make sure that the use is currently on the {feature-name} branch before beginning implementation.
If not, move them to the correct branch. If the branch does not exist, create it from main.
{file}:{COMPLETE, TESTED CODE - NO PLACEHOLDERS - NO "TODO" COMMENTS}
{file}:{COMPLETE, TESTED CODE - NO PLACEHOLDERS - NO "TODO" COMMENTS}
STOP & COMMIT: Agent must stop here and wait for the user to test, stage, and commit the change.
{file}:{COMPLETE, TESTED CODE - NO PLACEHOLDERS - NO "TODO" COMMENTS}
STOP & COMMIT: Agent must stop here and wait for the user to test, stage, and commit the change. </plan_template>
development
Build production RAG pipelines and persistent agent memory using Pinecone as the vector database backend. ALWAYS USE THIS SKILL when the user mentions Pinecone, wants to index documents for semantic search, build a retrieval-augmented generation system, store agent memory across sessions, implement hybrid search, or connect an LLM to a searchable knowledge base — even if they don't say "Pinecone" explicitly. Also use when the user asks about vector databases for RAG, namespace isolation for multi-tenant agents, embedding pipelines, or scaling a knowledge base beyond what local storage can handle. DO NOT use for local-only vector stores (Chroma, FAISS, pgvector) or pure keyword search with no semantic component.
development
Perform an AWS Well-Architected Framework review of the current workload IaC and architecture, generating findings and GitHub issues for improvements.
devops
Query AWS resources using natural language. Covers EC2, S3, RDS, Lambda, ECS, EKS, Secrets Manager, IAM, VPC, networking, messaging, and more. Strictly read-only — no writes, deletes, or mutations.
devops
Analyze AWS resource health, diagnose issues from CloudWatch logs and metrics, and create a remediation plan for identified problems.