Lint plugin agent/skill prompts against rules derived from Anthropic model cards (Opus 4.6, Sonnet 4.6). Use when authoring or reviewing skills and agents — "lint instructions", "audit prompts", "model card rules".
From dev-toolsnpx claudepluginhub alexei-led/cc-thingz --plugin dev-toolsThis skill is limited to using the following tools:
Searches, retrieves, and installs Agent Skills from prompts.chat registry using MCP tools like search_skills and get_skill. Activates for finding skills, browsing catalogs, or extending Claude.
Searches prompts.chat for AI prompt templates by keyword or category, retrieves by ID with variable handling, and improves prompts via AI. Use for discovering or enhancing prompts.
Compares coding agents like Claude Code and Aider on custom YAML-defined codebase tasks using git worktrees, measuring pass rate, cost, time, and consistency.
Review agent and skill instructions against rules derived from the Claude Opus 4.6 and Sonnet 4.6 system cards. Combines a fast regex pre-pass with deep model-based semantic review.
Read the lint rules rubric:
Read docs/instruction-lint-rules.md
This contains 12 rules in 3 tiers:
Run the fast regex linter for a structural baseline:
uv run python scripts/lint-instructions.py
Note which files have structural issues. These are heuristic — the model review in Step 3 is authoritative.
For each model tier, spawn a review agent that reads the actual instruction files and evaluates them semantically against the rules. The agent should understand INTENT, not just keyword presence.
Parse $ARGUMENTS:
go-dev) → review only that pluginopus / sonnet / haiku → review only agents using that modelFor each batch of files, spawn an Agent with:
You are reviewing Claude Code plugin instructions for quality against
rules derived from the Opus 4.6 and Sonnet 4.6 system cards.
## Rules (apply based on model in frontmatter)
### Universal (all models)
- U-SCOPE: Must have clear scope boundaries (what's in, what's out)
- U-OUTPUT: Must define expected output format
- U-TOOL-FIRST: If agent has Bash, must require running tools before manual analysis
- U-FAILURE: Must handle failure/impossibility (prevents over-eager workarounds)
- U-GROUND: Must instruct to ground claims in actual tool output
- U-NO-DESTROY: If agent has Bash, must warn about destructive actions
### Opus agents (model: opus)
- O-EFFICIENCY: Must include efficiency constraints (Opus over-explores)
- O-SCOPE-ONLY: Should have "ONLY these" or "exclusively" markers
- O-EFFORT-MATCH: effort:high must be justified by complex multi-dimensional tasks
### Sonnet agents (model: sonnet)
- S-NO-LECTURE: Must NOT contain lecture-inducing patterns (Sonnet tends to lecture)
- S-DECISIVE: Should include decisive action language
- S-ANTI-EAGER: Should include anti-over-eagerness (Sonnet is steerable here)
## Review these files
[list of file paths]
For each file:
1. Read it fully
2. Note the model from frontmatter
3. Apply the matching rules SEMANTICALLY — check intent, not just keywords
4. Rate each applicable rule: PASS / WARN / FAIL
5. For WARN/FAIL: explain specifically what's missing and suggest a fix
## Output Format
For each file, output:
### `<relative path>` (model: <model>, kind: <agent|skill>)
| Rule | Verdict | Notes |
|------|---------|-------|
| U-SCOPE | PASS/WARN/FAIL | ... |
...
Then at the end, output a summary:
- Total files reviewed
- Pass/warn/fail counts per rule
- Top 5 most impactful improvements to make
Collect results from all review agents. Present:
Present findings as a structured report:
## Instruction Lint Report
### Summary
- Files reviewed: N (X opus, Y sonnet, Z haiku)
- Regex pre-pass: N errors, N warnings
- Model review: N pass, N warn, N fail
### Critical Findings
1. ...
### Top 5 Improvements
1. ...
### Per-Plugin Results
...