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 bradtraversy/coding-with-ai-course-resources --skill cleanupgit clone --depth 1 https://github.com/bradtraversy/coding-with-ai-course-resourcesWrote 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/bradtraversy/coding-with-ai-course-resources/cleanup)<a href="https://agentmods.dev/skills/bradtraversy/coding-with-ai-course-resources/cleanup"><img src="https://agentmods.dev/badge/skills/bradtraversy/coding-with-ai-course-resources/cleanup.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 | $0.00016 | $0.00267 |
| Opus 5 | $0.00008 | $0.00133 |
| Sonnet 5 | $0.00003 | $0.00053 |
| Haiku 4.5 | $0.00002 | $0.00027 |
Grade A, and why
cleanup 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 8d 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.
What it actually says
Review the codebase for cleanup tasks:
- Make sure that the history in @context/current-feature.md is in order from oldest to newest
- Find unnecessary console.log statements in src/
- Find unused imports
- Check for stale TODO comments
- Find orphaned/unused files
- Check that context files match actual project state
- Check if the .env.production has the same variables (not always the same value) as the .env. If something is missing, tell me.
- Find
@ts-ignorecomments that might be stale
Mode: $ARGUMENTS
If no argument or argument is "check":
- Only report findings, don't modify anything
- List what WOULD be cleaned up
If the argument is "run" or "fix":
- First, report all findings with numbered items
- Then ask: "Which items would you like me to fix? (enter numbers like 1,3,5 or 'all' or 'none')"
- Wait for user response before making any changes
- Only fix the items the user specifies
- Report what you changed
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.
- 8d ago First seen · 32 lines · 16 tokens per session scan A 17c3dba3f48e
cleanup is a skill published in the GitHub repository bradtraversy/coding-with-ai-course-resources (319 stars, last pushed 5mo ago), licensed MIT. It adds 16 tokens to every session and 267 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 2026-08-30.
Other skills, from other repositories
debug-optimize-lcp
Guides debugging and optimizing Largest Contentful Paint (LCP) using Chrome DevTools MCP tools. Use this skill whenever the user asks about LCP performance, slow page loads, Core Web Vitals optimization, or wants to understand why their page's main content takes too long to appear. Also use when the user mentions…
systematic-debugging
Use when debugging a failing test, build error, or runtime issue that isn't immediately obvious. Guides a 4-phase root cause analysis instead of random fix attempts.
diagnose
Trace from a reproduced symptom to the source code that causes it. Pin the specific file and approximate line, rate confidence in the cause and clarity of the fix independently, and always propose a concrete fix.
repro-admin
Reproduce an EmDash admin UI bug. Attach a container, start the demo dev server, drive the admin with agent-browser using the dev-bypass session, and capture the reproduction as screenshots plus a replayable transcript.
log-error-digest
Analyze log files to troubleshoot errors, identify peak error periods, and produce error clustering, frequency statistics, and time distribution reports. Supports JSON, syslog, and Nginx formats with automatic detection. Use when a user uploads a .log file and asks to analyze errors, find patterns, debug issues, or…
byted-util-volcengine-detect-retry
An orchestration workflow for Volcengine Cloud Detect, a service that checks websites or network endpoints from test locations.