Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/joneqian/claude-skills-suiteWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/joneqian/claude-skills-suite/refactor-clean)<a href="https://agentmods.dev/commands/joneqian/claude-skills-suite/refactor-clean"><img src="https://agentmods.dev/badge/commands/joneqian/claude-skills-suite/refactor-clean/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/joneqian/claude-skills-suite/refactor-clean"><img src="https://agentmods.dev/badge/commands/joneqian/claude-skills-suite/refactor-clean.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00000 | $0.00173 |
| Opus 5 | $0.00000 | $0.00086 |
| Sonnet 5 | $0.00000 | $0.00035 |
| Haiku 4.5 | $0.00000 | $0.00017 |
Grade A, and why
refactor-clean scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
86% identical to refactor-clean — 15 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
Refactor Clean
Safely identify and remove dead code with test verification:
-
Run dead code analysis tools:
- knip: Find unused exports and files
- depcheck: Find unused dependencies
- ts-prune: Find unused TypeScript exports
-
Generate comprehensive report in .reports/dead-code-analysis.md
-
Categorize findings by severity:
- SAFE: Test files, unused utilities
- CAUTION: API routes, components
- DANGER: Config files, main entry points
-
Propose safe deletions only
-
Before each deletion:
- Run full test suite
- Verify tests pass
- Apply change
- Re-run tests
- Rollback if tests fail
-
Show summary of cleaned items
Never delete code without running tests first!
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 29 lines · 0 tokens per session scan A 52b0fdafc917
refactor-clean is a command published in the GitHub repository joneqian/claude-skills-suite (32 stars, last pushed 7mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 173 tokens. A static security scan graded it A with 0 findings. It is 86% identical to refactor-clean, differing in 15 lines, and is treated as a copy.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.