# Lint And Validate

> Lint and Validate Skill

- Skill: `techwavedev/lint-and-validate` (Agent Skill, multi-file: 3 files)
- Install (CLI): `npx skillmds@latest add techwavedev/lint-and-validate`
- Raw SKILL.md: https://api.skillmd.com/api/skills/techwavedev/lint-and-validate/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: techwavedev (https://skillmd.com/u/techwavedev)
- Updated: 2026-09-21
- Page: https://skillmd.com/skills/techwavedev/lint-and-validate

---


# 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.

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

---

## 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:
```bash
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:

```bash
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