lint

name	lint
version	1.0.0
description	[Code Quality] Use when you need to run linters and fix issues for backend or frontend.
disable-model-invocation	false

Quick Summary

Goal: Run the project's configured linters/analyzers (resolve the commands from docs/project-config.json / project-reference docs) and report or auto-fix code quality issues. Commands shown below are illustrative examples — substitute your stack's tooling.

Workflow:

1. Parse — Determine scope from arguments: backend, frontend, or both; fix mode or report-only
2. Execute — Run the repository's configured lint/analyzer commands from project config and package/build manifests
3. Report — Group issues by severity (error/warning/info) with file paths and line numbers

Key Rules:

• No argument = run both backend + frontend in report-only mode
• fix argument = apply safe auto-fixes, report remaining manual items
• Always show file paths and line numbers in output

Be skeptical. Apply critical thinking, sequential thinking. Every claim needs traced proof, confidence percentages (Idea should be more than 80%).

Run linting: $ARGUMENTS

Instructions

1. Parse arguments:

   • backend or be → Run the backend analyzers/linters
   • frontend or fe → Run the frontend linters/formatters
   • fix → Auto-fix issues where possible
   • No argument → Run both, report only

2. For Backend — run the configured backend analyzer/build command:

   dotnet build {SolutionName}.sln /p:TreatWarningsAsErrors=false
   

   • Check for analyzer warnings (CA*, IDE*, etc.)
   • Report code style violations

3. For Frontend — run the configured frontend lint/format command:

   cd {frontend-workspace-path}
   nx lint <app-name>
   nx lint {lib-name}
   

With auto-fix:

```bash
nx lint <app-name> --fix
npx prettier --write "apps/**/*.{ts,html,scss}" "libs/**/*.{ts,html,scss}"
```

4. Report format: - Group issues by severity (error, warning, info) - Show file paths and line numbers - Suggest fixes for common issues

5. Auto-fix behavior:
   • If fix argument provided, apply safe auto-fixes
   • Report what was fixed vs what needs manual attention

---

[IMPORTANT] Use TaskCreate to break ALL work into small tasks BEFORE starting — including tasks for each file read. This prevents context loss from long files. For simple tasks, AI MUST ATTENTION ask user whether to skip.

Prerequisites: MUST ATTENTION READ before executing:

Critical Thinking Mindset — Apply critical thinking, sequential thinking. Every claim needs traced proof, confidence >80% to act. Anti-hallucination: Never present guess as fact — cite sources for every claim, admit uncertainty freely, self-check output for errors, cross-reference independently, stay skeptical of own confidence — certainty without evidence root of all hallucination.

Understand Code First — HARD-GATE: Do NOT write, plan, or fix until you READ existing code.

1. Search 3+ similar patterns (grep/glob) — cite file:line evidence
2. Read existing files in target area — understand structure, base classes, conventions
3. Run python .claude/scripts/code_graph trace <file> --direction both --json when .code-graph/graph.db exists
4. Map dependencies via connections or callers_of — know what depends on your target
5. Write investigation to .ai/workspace/analysis/ for non-trivial tasks (3+ files)
6. Re-read analysis file before implementing — never work from memory alone. — why: long context drifts from the file; the file is ground truth
7. NEVER invent new patterns when existing ones work — match exactly or document deviation. — why: divergent patterns fragment the codebase and slow every future reader

BLOCKED until: - [ ] Read target files - [ ] Grep 3+ patterns - [ ] Graph trace (if graph.db exists) - [ ] Assumptions verified with evidence

AI Mistake Prevention — Failure modes to avoid on every task:

Re-read files after context changes. Context compaction, resume, or long-running work can make memory stale; verify current files before acting. Verify generated content against source evidence. AI hallucinates APIs, names, claims, and document facts. Check the relevant source before documenting or referencing. Check downstream references before deleting or renaming. Removing an artifact can stale docs, generated mirrors, configs, and callers; map references first. Trace the full impact chain after edits. Changing a definition can miss derived outputs and consumers. Follow the affected chain before declaring done. Verify ALL affected outputs, not just the first. One green check is not all green checks; validate every output surface the change can affect. Assume existing values are intentional — ask WHY before changing. Before changing a constant, limit, flag, wording, or pattern, read nearby context and history. Surface ambiguity before acting — don't pick silently. Multiple valid interpretations require an explicit question or stated assumption with risk. Keep shared guidance role-relevant. Universal guidance must help every receiving skill or agent; code-specific obligations belong only in code-specific protocols.

IMPORTANT MUST ATTENTION search 3+ existing patterns and read code BEFORE any modification. Run graph trace when graph.db exists.

• MANDATORY IMPORTANT MUST ATTENTION cite file:line evidence for every claim. Confidence >80% to act, <60% = do NOT recommend.

MUST ATTENTION apply critical + sequential thinking — every claim needs appropriate traced evidence (file:line for repo/code claims; source URL or artifact section for research, product, content, and docs claims); confidence >80% to act, <60% DO NOT recommend. Anti-hallucination: never present guess as fact, admit uncertainty freely, cross-reference independently, stay skeptical of own confidence.

MUST ATTENTION apply AI mistake prevention — verify generated content against evidence, trace downstream references before deleting or renaming, verify all affected outputs, re-read files after context loss, and surface ambiguity before acting.

Closing Reminders

IMPORTANT MUST ATTENTION — Protocols in force (concise digest of the SYNC/shared blocks this skill carries):

• Critical Thinking: apply critical + sequential thinking; traced file:line proof, confidence >80%.
• Understand Code First: search 3+ patterns and read code before any modification.
• AI Mistake Prevention: verify generated content against evidence, trace downstream references, verify all affected outputs, re-read after context loss, surface ambiguity.

IMPORTANT MUST ATTENTION break work into small todo tasks using TaskCreate BEFORE starting IMPORTANT MUST ATTENTION search codebase for 3+ similar patterns before creating new code IMPORTANT MUST ATTENTION cite file:line evidence for every claim (confidence >80% to act) IMPORTANT MUST ATTENTION add a final review todo task to verify work quality

[TASK-PLANNING] Before acting, analyze task scope and systematically break it into small todo tasks and sub-tasks using TaskCreate.