skills/audit-integrity/SKILL.md
Shared audit integrity framework for all AppSec agents — enforces output quality, intellectual honesty, and continuous improvement through anti-rationalization guards, self-critique loops, retry protocols, non-negotiable behaviors, self-reflection quality gates (1-10 scoring, ≥8 threshold), and a self-learning system with lesson/memory governance for security analysis agents.
npx skillsauth add williamlimasilva/.copilot audit-integrityInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Enforces output quality, intellectual honesty, and continuous improvement across all AppSec agents.
This skill provides 7 reusable capabilities. Agents apply all 7 unless their scope excludes a specific component.
| Component | Reference File | Purpose | |-----------|---------------|---------| | Clarification Protocol | clarification-protocol.md | Ask ≤2 targeted questions before analysis when scope is ambiguous | | Anti-Rationalization Guard | anti-rationalization-guard.md | Table of prohibited rationalizations with mandatory responses | | Self-Critique Loop | self-critique-loop.md | Mandatory second-pass review after initial analysis | | Retry Protocol | retry-protocol.md | Tool failure handling — retry once, then document | | Non-Negotiable Behaviors | non-negotiable-behaviors.md | Hard rules: never fabricate, always cite evidence, report gaps | | Self-Reflection Quality Gate | self-reflection-quality-gate.md | 1–10 scoring rubric with ≥8 threshold per category | | Self-Learning System | self-learning-system.md | Lesson/Memory templates and governance rules |
Each agent customizes the Self-Critique Loop checklist and Self-Reflection Quality Gate categories to match its domain. The reference files provide the base templates; agents extend them with domain-specific items.
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.