Linter for AI instruction files — skills, prompts, and agent specs.

skill-lint scans AI instruction files (CLAUDE.md, AGENTS.md, GEMINI.md, SKILL.md, .cursorrules, .windsurfrules, .cursor/rules/*.mdc, .github/copilot-instructions.md, .github/instructions/, and agent/skill directories) for issues that cause token waste, hallucination risk, and unpredictable agent behavior. 61 rules across 13 categories with fix suggestions, auto-fix, MCP server, and watch mode.

pip install ai-skill-lint   # or: pipx install ai-skill-lint

skill-lint .                                    # Scan current project
skill-lint /path/to/project                     # Scan a local directory
skill-lint https://github.com/org/repo          # Scan a GitHub repo
skill-lint . --format sarif --fail-on warning   # CI gate (severity)
skill-lint . --fail-under 80                    # CI gate (score)
skill-lint . -v                                 # Verbose
skill-lint . --exclude "vendor/*.md"            # Exclude patterns
skill-lint fix . --dry-run                      # Preview auto-fixes
skill-lint fix .                                # Apply safe fixes
skill-lint rule TCOST001                        # Explain a rule
skill-lint rule                                 # List all 61 rules
skill-lint . --format html > report.html        # HTML report
skill-lint serve                                # Start MCP server (stdio)
skill-lint serve --transport http --port 8000   # MCP server (HTTP)
skill-lint watch .                              # Re-lint on file changes

Each file scored 0-100 with actionable fix suggestions.

# Basic — one line
- uses: rajusem/skill-lint@v0

# With SARIF upload to GitHub Code Scanning
- uses: rajusem/skill-lint@v0
  with:
    format: sarif
    fail-on: warning
- uses: github/codeql-action/upload-sarif@v3
  if: always()
  with:
    sarif_file: results.sarif

# Score gate — fail if average score below 80
- uses: rajusem/skill-lint@v0
  with:
    fail-under: '80'

pip install ai-skill-lint
skill-lint . --fail-on warning

repos:
  - repo: https://github.com/rajusem/skill-lint
    rev: v0.6.0
    hooks:
      - id: skill-lint

skill-lint . --save-baseline   # Save current findings
skill-lint . --diff            # Show only NEW issues

<!-- skill-lint: disable TCOST005 -->
<!-- skill-lint: disable TCOST003, HRISK001 -->

For VS Code/Cursor autocomplete, add to the top of your .skill-lint.yaml:

# yaml-language-server: $schema=https://raw.githubusercontent.com/rajusem/skill-lint/main/skill-lint-schema.json

disable:
  - HRISK002
  - OQUAL001
fail_on: warning
fail_under: 80        # exit 1 if avg score < 80
thresholds:
  max_tokens: 8000   # default: 5000
  max_lines: 800     # default: 500
include:
  - "prompts/*.md"
  - "docs/agents/**/*.md"
exclude:
  - "vendor/*.md"

[tool.skill-lint]
disable = ["HRISK002", "OQUAL001"]
fail_on = "warning"
fail_under = 80
thresholds = {max_tokens = 8000, max_lines = 800}
include = ["prompts/*.md", "docs/agents/**/*.md"]
exclude = ["vendor/*.md"]

Precedence: CLI flags > .skill-lint.yaml > pyproject.toml

skill-lint can run as an MCP server, integrating with Claude Code, Cursor, and other MCP-enabled tools.

pip install ai-skill-lint[mcp]

Claude Code / Cursor config (.claude/settings.json or MCP settings):

{
  "mcpServers": {
    "skill-lint": {
      "command": "skill-lint",
      "args": ["serve"]
    }
  }
}

Or with uvx (no install needed):

{
  "mcpServers": {
    "skill-lint": {
      "command": "uvx",
      "args": ["--from", "ai-skill-lint[mcp]", "skill-lint", "serve"]
    }
  }
}

Tools provided: scan (with summary mode), rule (lookup/list), suggest_fix (dry-run diff), apply_fix (write fixes).

Re-lint automatically when skill files change:

pip install ai-skill-lint[watch]
skill-lint watch .                    # Watch current directory
skill-lint watch . --debounce 1.0     # Custom debounce interval
skill-lint watch . --disable TCOST003 # Suppress specific rules

Write your own rules by extending the Rule base class:

from skill_lint.scanner import Rule, Issue, register_rule

class MyRule(Rule):
    id = "CUSTOM_001"
    description = "Check for company-specific patterns"

    def check(self, ctx):
        issues = []
        if "legacy API" in ctx.content:
            issues.append(Issue(
                category="best-practice",
                severity="suggestion",
                message="References legacy API",
                fix="Use the new v2 API instead",
                rule_id=self.id,
            ))
        return issues

register_rule(MyRule())

Custom rule IDs must use the CUSTOM_ prefix. The ctx object provides: content, lines, regions, filepath, root, tokens, and content_text (code-fence-filtered).

1. Help, don't restrict — every finding is a suggestion, not a gate
2. Show, don't enforce — display impact, let users decide
3. Honest numbers — no inflated claims; validated across 88+ repos

Want Claude to fix your instruction files automatically? Copy our official skill:

cp -r examples/fix-instruction-files/ .claude/skills/fix-instruction-files/

The skill runs skill-lint, interprets findings, and proposes fixes with before/after diffs. See examples/fix-instruction-files/SKILL.md.

See docs/rules.md for detailed documentation on all 61 rules.

Apache-2.0