lint-and-validate

name	lint-and-validate
description	Automatic quality control, linting, and static analysis procedures. Use after every code modification to ensure syntax correctness and project standards. Triggers onKeywords: lint, format, check, validate, types, static analysis.

Lint and Validate Skill

MANDATORY: Run appropriate validation tools after EVERY code change. Do not finish a task until the code is error-free.

Procedures by Ecosystem

Node.js / TypeScript

1. Lint/Fix: npm run lint or npx eslint "path" --fix
2. Types: npx tsc --noEmit
3. Security: npm audit --audit-level=high

Python

1. Linter (Ruff): ruff check "path" --fix (Fast & Modern)
2. Security (Bandit): bandit -r "path" -ll
3. Types (MyPy): mypy "path"

The Quality Loop

1. Write/Edit Code
2. Run Audit: npm run lint && npx tsc --noEmit
3. Analyze Report: Check the "FINAL AUDIT REPORT" section.
4. Fix & Repeat: Submitting code with "FINAL AUDIT" failures is NOT allowed.

Error Handling

• If lint fails: Fix the style or syntax issues immediately.
• If tsc fails: Correct type mismatches before proceeding.
• If no tool is configured: Check the project root for .eslintrc, tsconfig.json, pyproject.toml and suggest creating one.

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Strict Rule: No code should be committed or reported as "done" without passing these checks.

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Scripts

Script	Purpose	Command
scripts/lint_runner.py	Unified lint check	python scripts/lint_runner.py <project_path>
scripts/type_coverage.py	Type coverage analysis	python scripts/type_coverage.py <project_path>

AGI Framework Integration

Qdrant Memory Integration

Before executing complex tasks with this skill:

python3 execution/memory_manager.py auto --query "<task summary>"

Decision Tree:

• Cache hit? Use cached response directly — no need to re-process.
• Memory match? Inject context_chunks into your reasoning.
• No match? Proceed normally, then store results:

python3 execution/memory_manager.py store \
  --content "Description of what was decided/solved" \
  --type decision \
  --tags lint-and-validate <relevant-tags>

Note: Storing automatically updates both Vector (Qdrant) and Keyword (BM25) indices.

Agent Team Collaboration

• Strategy: This skill communicates via the shared memory system.
• Orchestration: Invoked by orchestrator via intelligent routing.
• Context Sharing: Always read previous agent outputs from memory before starting.

Local LLM Support

When available, use local Ollama models for embedding and lightweight inference:

• Embeddings: nomic-embed-text via Qdrant memory system
• Lightweight analysis: Local models reduce API costs for repetitive patterns