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.
npx skills add artivilla/agents-config --skill knipgit clone --depth 1 https://github.com/artivilla/agents-configWrote 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/skills/artivilla/agents-config/knip)<a href="https://agentmods.dev/skills/artivilla/agents-config/knip"><img src="https://agentmods.dev/badge/skills/artivilla/agents-config/knip.svg" alt="Measured on agentmods" 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 | $0.00028 | $0.00669 |
| Opus 5 | $0.00014 | $0.00334 |
| Sonnet 5 | $0.00006 | $0.00134 |
| Haiku 4.5 | $0.00003 | $0.00067 |
Grade A, and why
knip 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 7d 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.
How it starts
The opening of the file, as written. The whole thing — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Knip Code Cleanup
Run knip to find and remove unused files, dependencies, and exports from this codebase.
Setup
-
Check if knip is available:
- Run
npx knip --versionto test - If it fails or is very slow, check if
knipis in package.json devDependencies - If not installed locally, install with
npm install -D knip(or pnpm/yarn equivalent based on lockfile present)
- Run
-
If no
knip.jsonorknip.jsoncconfig exists and knip reports many false positives, consider creating a minimal config based on the frameworks detected in package.json
Execution
- Run
npx knipto get initial report - Review the output categories:
- Unused files - files not imported anywhere
- Unused dependencies - packages in package.json not imported
- Unused devDependencies - dev packages not used
- Unused exports - exported functions/variables not imported elsewhere
- Unused types - exported types not used
Cleanup Strategy
Auto-delete (high confidence):
- Unused exports that are clearly internal (not part of public API)
- Unused type exports
- Unused dependencies (remove from package.json)
- Unused files that are clearly orphaned (not entry points, not config files)
For these, proceed with deletion without asking. Use --fix --allow-remove-files for automated fixes, or manually delete/edit as needed.
Ask first (needs clarification):
- Files that might be entry points or dynamically imported
- Exports that might be part of a public API (index.ts, lib exports)
- Dependencies that might be used via CLI or peer dependencies
- Anything in paths like
src/index,lib/, or files with "public" or "api" in the name
Use the AskUserQuestion tool to clarify before deleting these.
Workflow
- Run knip, capture full output
- Categorize each issue as auto-delete or needs-clarification
- Ask about uncertain items in a single batch question
- Perform all deletions (use Edit tool to remove exports, Bash to remove files/deps)
- Re-run knip to verify cleanup is complete
- Repeat until no issues remain or only intentionally-ignored items exist
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.
- 7d ago First seen · 81 lines · 28 tokens per session scan A 72f6fc930e90
knip is a skill published in the GitHub repository artivilla/agents-config (0 stars, last pushed 3mo ago), licensed MIT. It adds 28 tokens to every session and 669 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
testing-dags
Complex DAG testing workflows with debugging and fixing cycles. Use for multi-step testing requests like "test this dag and fix it if it fails", "test and debug", "run the pipeline and troubleshoot issues". For simple test requests ("test dag", "run dag"), the airflow entrypoint skill handles it directly. This skill…
debugging-dags
Comprehensive DAG failure diagnosis and root-cause analysis with structured investigation and prevention recommendations. Use when deep failure investigation is needed, a DAG fails to import/parse or 'airflow dags list' errors on a file; a task or run is failing and must be diagnosed and fixed; requests like 'why did…
bug-fix-brief
Generates a structured Bug Fix Brief (BFB) to document issue corrections. Includes root cause analysis, repro steps, fix options, and fix checklist. Use when user asks to create a BFB, document a bug fix, or generate a bug correction document.
observability-debugging
Feilsøk produksjonsproblemer med Mimir-metrikker, Loki-logger og Tempo-traces — strukturerte debugging-workflows for Nav-utviklere.
loom-code-migration
Strategies and patterns for safe code migrations and upgrades.
loom-debugging
Systematic diagnosis and resolution of software bugs, test failures, data quality issues, and performance problems.