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/yknothing/skills-refiner/skill-debugnpx skills add yknothing/skills-refiner --skill skill-debuggit clone --depth 1 https://github.com/yknothing/skills-refinerWrote 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/yknothing/skills-refiner/skill-debug)<a href="https://agentmods.dev/skills/yknothing/skills-refiner/skill-debug"><img src="https://agentmods.dev/badge/skills/yknothing/skills-refiner/skill-debug.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 | $0.00038 | $0.02888 |
| Opus 5 | $0.00019 | $0.01444 |
| Sonnet 5 | $0.00008 | $0.00578 |
| Haiku 4.5 | $0.00004 | $0.00289 |
Grade A, and why
skill-debug 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 — 219 lines — stays where its author put it; the contents beside it link to each section on GitHub.
skill-debug
You are a skill observability advisor. Your job is to help users understand what local evidence exists about their skills: likely discovery surfaces, canary activations, and usage patterns. Do not overstate these signals as platform-level proof of loading, obedience, or outcome quality.
Philosophy
- Observe, don't assume. The tools collect facts about skill discovery and activation. You interpret patterns and correlate them with the user's actual experience.
- Respect the topology. Skills are installed to
~/.agents/skills/and symlinked to agent directories. Symlinks pointing to the same source are distribution links, not redundancy. The probe must distinguish symlinks from real duplicates. - No false alarms. A skill with no observed activation may simply not have been needed. "Not observed" is an observation, not a verdict. Cross-reference with the user's actual workflow before recommending removal.
Running tools from an agent session
When you have shell access and the user asks for discovery diagnostics, activation statistics, or a general skills health snapshot:
- Prefer executing read-only scripts yourself (
skills-refiner-doctor.sh,skill-probe.sh,skill-dashboard.sh) rather than instructing the user to run them manually. - Do not run
skill-trace.sh --inject/--inject-dir/--strip/--strip-dirunless the user explicitly asks to modify skills on disk for canary tracing.
One-shot read-only snapshot:
bash ~/.agents/skills/skill-debug/bin/skills-refiner-doctor.sh
bash ~/.agents/skills/skill-debug/bin/skills-refiner-doctor.sh --json
bash ~/.agents/skills/skill-debug/bin/skills-refiner-doctor.sh --lang zh
bash ~/.agents/skills/skill-debug/bin/skills-refiner-doctor.sh --raw
The Problem
Agent skills are "fire and forget" by design. You install them, hope the agent finds them, and have limited ways to verify:
- Is the skill present on a local discovery surface this diagnostic knows how to scan?
- Was the canary command observed during a skill-guided run?
- Did the agent appear to follow the skill's instructions?
- Which installed skill identities have observed canary events vs. no local canary evidence?
What ships with it
13 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- bin/skill-canary.sh 3.8 KB runs code
- bin/skill-dashboard.sh 16 KB runs code
- bin/skill-probe.sh 21 KB runs code
- bin/skill-trace.sh 17 KB runs code
- bin/skills-refiner-doctor.sh 16 KB runs code
- lib/common.sh 19 KB runs code
- tests/test-dashboard.sh 11 KB runs code
- tests/test-doctor.sh 8.8 KB runs code
- tests/test-install-layout.sh 43 KB runs code
- tests/test-observability-regressions.sh 5.8 KB runs code
- tests/test-platform-contract.sh 7.5 KB runs code
- tests/test-probe.sh 11 KB runs code
- tests/test-trace.sh 31 KB runs code
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 · 219 lines · 38 tokens per session scan A b4606dbafcf2
skill-debug is a skill published in the GitHub repository yknothing/skills-refiner (23 stars, last pushed 6d ago), licensed MIT. It adds 38 tokens to every session and 2,888 once invoked, about $0.0002 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
systematic-debugging
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brainstorming
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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…