linter-setup

name	linter-setup
version	1.0.0
description	[Quality] Use when you need to research and configure code quality tooling for any tech stack — linters, formatters, static analysis, pre-commit hooks, and CI gates.

[BLOCKING] Execute skill steps in declared order. NEVER skip, reorder, or merge steps without explicit user approval. [BLOCKING] Before each step or sub-skill call, update task tracking: set in_progress when step starts, set completed when step ends. [BLOCKING] Every completed/skipped step MUST include brief evidence or explicit skip reason. [BLOCKING] If Task tools are unavailable, create and maintain an equivalent step-by-step plan tracker with the same status transitions.

Quick Summary

Goal: Install the full computational feedback sensor layer for any tech stack — linters, formatters, type checkers, static analyzers, pre-commit hooks, and CI quality gates.

Output: Config files at project root + pre-commit hook config + CI quality gate step + .editorconfig.

When invoked: After /scaffold in the greenfield workflow, before /harness-setup.

Design principles:

• Generic — No hardcoded tool names in the research protocol. AI researches the stack's ecosystem.
• Research-driven — Per-stack research → present top 2-3 options → user picks → configure.
• Strict-by-default — Propose strictest reasonable settings; loosen only with explicit user approval.
• Purpose-first — Every category has a WHY; understanding purpose prevents cargo-culting.
• Integration-ready — Every tool must work both locally (fast feedback) AND in CI (enforcement gate).

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Stack Detection Protocol

Read from (in priority order):

1. plan.md YAML frontmatter — look for tech_stack, language, framework fields
2. Architecture-design report — look for tech stack comparison table
3. Tech-stack-comparison report — look for chosen stack

Extract: primary language(s), framework(s), CI platform, test framework, package manager.

Write detected profile to .ai/workspace/linter-setup/stack-profile.md:

# Stack Profile

Language: {language}
Framework: {framework}
Package Manager: {npm/pip/dotnet/go/cargo/etc}
CI Platform: {github-actions/gitlab-ci/azure-pipelines/etc}
Test Framework: {framework}

If any critical field undetectable → AskUserQuestion to confirm before research.

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Tool Research Protocol

MANDATORY IMPORTANT MUST ATTENTION — This section uses QUERY TEMPLATES, not tool names. DO NOT hardcode specific tool recommendations. Research current ecosystem for the detected stack and present options.

For each tech stack layer detected, research these TOOL CATEGORIES using the query templates below:

Category	Purpose (WHY)	Research Query Template
Linter	Catch bugs, enforce style, prevent common errors at author time	"{language} best linter {year} community standard"
Formatter	Eliminate style debates, enforce consistent code shape	"{language} opinionated code formatter {year}"
Type Checker	Catch type errors without runtime — strongest computational sensor	"{language} static type checker {year}"
Static Analyzer	Deep bug patterns, complexity, dead code, security CWEs	"{language} static analysis SAST tool {year}"
Dependency Scanner	Known CVEs in dependencies — supply chain security	"{language} dependency vulnerability scanner {year}"
Architecture Fitness	Enforce module boundaries, dependency direction	"{language} architecture linting module boundaries {year}"

Research process per category:

1. Search with the query template (WebSearch if available, otherwise apply knowledge with explicit confidence %)
2. Score top 3 candidates: community adoption, last release date, CI integration ease, config complexity
3. Present to user via AskUserQuestion: "For {category} in {language}, which tool?" with top 2-3 as options + brief pros/cons

IMPORTANT: If confidence in current ecosystem is <80% (e.g., fast-moving ecosystem, unfamiliar stack) → use WebSearch to verify before presenting options.

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Installation & Configuration Protocol

After user selects tools for each category:

1. Generate install command for the detected package manager
2. Generate config file with STRICTEST reasonable defaults
   • Rationale: starting strict is easier to loosen than starting loose is to tighten
   • Loosen only with explicit user approval via AskUserQuestion
3. Document what each enabled rule catches and why (one line per rule group)
4. Generate sample config file: .{tool}rc, {tool}.config.{ext}, pyproject.toml section, etc.
5. Add tool cache directories to .gitignore

.editorconfig (ALWAYS generate — stack-agnostic):

root = true

[*]
indent_style = space
indent_size = 2
end_of_line = lf
charset = utf-8
trim_trailing_whitespace = true
insert_final_newline = true

Adjust indent_size and end_of_line for the detected stack's conventions.

---

Pre-Commit Hook Setup

Note on framework names: Pre-commit hook frameworks are ecosystem infrastructure standards, not research choices. Naming them here is correct — they are the glue layer, not the quality tools invoked through them. The quality tools (linter, formatter) invoked inside hooks are the research-driven selections from the Tool Research Protocol above.

Detect pre-commit framework for the stack:

• Node.js / JavaScript / TypeScript → Husky + lint-staged OR lefthook (research current community preference)
• Python → pre-commit framework (pre-commit package)
• .NET / C# → dotnet tool restore + custom .git/hooks/pre-commit shell script
• Go → pre-commit framework or custom Makefile target
• Rust → cargo-husky OR pre-commit framework
• Java / Kotlin → pre-commit framework or Maven/Gradle Git hooks plugin
• Ruby → overcommit OR pre-commit framework

Configure hooks to run in this order (fastest first to fail fast):

1. Formatter (check only — do not auto-fix in hook)
2. Linter (fail on any error)
3. Type-check (fail on any error)

Performance constraint: Hooks MUST run in <30 seconds total for good DX. If slower:

• Configure to run only on staged files (not full codebase)
• Defer slow checks (static analysis, full type-check) to CI only

Generate:

• Hook config file (.husky/pre-commit, .lefthook.yml, .pre-commit-config.yaml, etc.)
• README.md section: "## Code Quality — Pre-commit Hooks" with setup instructions for new team members

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CI Quality Gate Configuration

Detect CI platform from project files:

• .github/workflows/ → GitHub Actions
• .gitlab-ci.yml → GitLab CI
• azure-pipelines.yml → Azure Pipelines
• Jenkinsfile → Jenkins
• bitbucket-pipelines.yml → Bitbucket Pipelines

If not detected → AskUserQuestion: "Which CI platform does this project use?"

Generate CI job/step that:

1. Restores tool cache (install only on cache miss)
2. Runs formatter check (fail on diff — --check mode, no auto-fix)
3. Runs linter (fail on any error)
4. Runs type checker (fail on any error)
5. Runs static analyzer (fail on threshold: configurable complexity and duplication)
6. Runs dependency vulnerability scanner (fail on HIGH/CRITICAL CVEs)
7. Reports test coverage (fail if below threshold — AskUserQuestion to confirm threshold, recommended: 80%)

MANDATORY: CI gate must match pre-commit hooks. If a check runs locally, it runs in CI. No divergence.

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Verification Checklist

After all config files generated, verify MUST ATTENTION each item:

• Config files exist at project root (linter, formatter, type-checker configs)
• .editorconfig created at project root
• Pre-commit hook fires on git commit — test with an intentional violation (e.g., add a lint error, attempt commit, verify hook blocks)
• CI step defined and references the correct config files
• Team setup documented in README.md — new devs know to run {hook install command} after clone
• .gitignore updated with tool cache directories

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Next Steps

AskUserQuestion:

• "/harness-setup continues (Recommended)" — Set up feedforward guides + inferential sensors to complete the outer harness
• "/cook" — Skip harness inventory and begin implementation
• "Skip" — Continue manually

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[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.

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.

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

Check downstream references before deleting. Deleting components causes documentation and code staleness cascades. Map all referencing files before removal. Verify AI-generated content against actual code. AI hallucinates APIs, class names, and method signatures. Always grep to confirm existence before documenting or referencing. Trace full dependency chain after edits. Changing a definition misses downstream variables and consumers derived from it. Always trace the full chain. Trace ALL code paths when verifying correctness. Confirming code exists is not confirming it executes. Always trace early exits, error branches, and conditional skips — not just happy path. When debugging, ask "whose responsibility?" before fixing. Trace whether bug is in caller (wrong data) or callee (wrong handling). Fix at responsible layer — never patch symptom site. Assume existing values are intentional — ask WHY before changing. Before changing any constant, limit, flag, or pattern: read comments, check git blame, examine surrounding code. Verify ALL affected outputs, not just the first. Changes touching multiple stacks require verifying EVERY output. One green check is not all green checks. Holistic-first debugging — resist nearest-attention trap. When investigating any failure, list EVERY precondition first (config, env vars, DB names, endpoints, DI registrations, data preconditions), then verify each against evidence before forming any code-layer hypothesis. Surgical changes — apply the diff test. Bug fix: every changed line must trace directly to the bug. Don't restyle or improve adjacent code. Enhancement task: implement improvements AND announce them explicitly. Surface ambiguity before coding — don't pick silently. If request has multiple interpretations, present each with effort estimate and ask. Never assume all-records, file-based, or more complex path.

Prompt-Enhance Closing Anchors

IMPORTANT MUST ATTENTION follow declared step order for this skill; NEVER skip, reorder, or merge steps without explicit user approval IMPORTANT MUST ATTENTION for every step/sub-skill call: set in_progress before execution, set completed after execution IMPORTANT MUST ATTENTION every skipped step MUST include explicit reason; every completed step MUST include concise evidence IMPORTANT MUST ATTENTION if Task tools unavailable, maintain an equivalent step-by-step plan tracker with synchronized statuses

Closing Reminders

MUST ATTENTION use QUERY TEMPLATES in Tool Research — never hardcode tool names in the research phase MUST ATTENTION present top 2-3 options per category via AskUserQuestion — never auto-select MUST ATTENTION verify pre-commit hook fires with an intentional violation before marking complete MUST ATTENTION CI gate must match pre-commit hooks — no divergence between local and CI checks MUST ATTENTION loosen strict defaults ONLY with explicit user approval

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