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 agentmods add skills/ssbun/csl-agent-kit/bug-fixnpx skills add SSBun/csl-agent-kit --skill bug-fixgit clone --depth 1 https://github.com/SSBun/csl-agent-kitWrote 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/ssbun/csl-agent-kit/bug-fix)<a href="https://agentmods.dev/skills/ssbun/csl-agent-kit/bug-fix"><img src="https://agentmods.dev/badge/skills/ssbun/csl-agent-kit/bug-fix.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 | $0.00066 | $0.00762 |
| Opus 5 | $0.00033 | $0.00381 |
| Sonnet 5 | $0.00013 | $0.00152 |
| Haiku 4.5 | $0.00007 | $0.00076 |
Grade A, and why
bug-fix 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 4d 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 — 70 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Bug Fix
Use a tight reproduce -> diagnose -> fix -> verify loop. Prefer the smallest command that proves the failure and the smallest change that fixes it.
Workflow
-
Capture the failure
- Read the user report, logs, failing test name, branch state, and recent diff.
- If no command is known, inspect project docs, package manifests, CI config, or existing test scripts.
- State the concrete symptom before changing code.
-
Reproduce narrowly
- Run the smallest relevant test or command first.
- For runtime bugs, re-run the original crash, CLI command, page action, log-producing step, or CI step when possible.
- If the full suite is the only known command, run it once, then narrow from the output.
- Preserve the important error lines, assertion, stack trace, exit code, and failing file.
- If the bug has no failing test and a focused regression test or reproduction script is practical, create it before changing production code.
-
Localize the cause
- Compare failing behavior against nearby tests, recent changes, and existing patterns.
- Inspect production code and tests together; do not assume the test is wrong.
- Form one working hypothesis at a time and verify it with code or command output.
-
Fix minimally
- Change only files needed for the failure.
- Prefer existing helpers, local conventions, and simple control flow.
- Do not rewrite unrelated code while debugging.
-
Verify the fix
- Re-run the original failure path first: failing test, crash command, page action, CI step, or log-producing command.
- Re-run the narrow failing command if it differs from the original path.
- Run the adjacent or broader test command that could catch regressions.
- If no automated regression test was practical, run a smoke check and record the remaining risk.
-
Report evidence
- Summarize the root cause, changed files, and verification commands with pass/fail results.
- If a command cannot run, say exactly why and what risk remains.
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.
- 4d ago First seen · 70 lines · 66 tokens per session scan A 0dcd33ef52a7
bug-fix is a skill published in the GitHub repository SSBun/csl-agent-kit (10 stars, last pushed 5d ago), licensed MIT. It adds 66 tokens to every session and 762 once invoked, about $0.0003 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
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…