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/warpdotdev/common-skills/skill-doctornpx skills add warpdotdev/common-skills --skill skill-doctorgit clone --depth 1 https://github.com/warpdotdev/common-skillsWrote 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/warpdotdev/common-skills/skill-doctor)<a href="https://agentmods.dev/skills/warpdotdev/common-skills/skill-doctor"><img src="https://agentmods.dev/badge/skills/warpdotdev/common-skills/skill-doctor.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 | $0.00055 | $0.02206 |
| Opus 5 | $0.00028 | $0.01103 |
| Sonnet 5 | $0.00011 | $0.00441 |
| Haiku 4.5 | $0.00006 | $0.00221 |
Grade A, and why
skill-doctor 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 6d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- skill-doctor — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 172 lines — stays where its author put it; the contents beside it link to each section on GitHub.
skill-doctor
Grade the user's agent setup by scoring recent local agent conversations, then propose concrete skill edits and render one shareable report page.
The report can cover conversations in the current repository, conversations in selected projects, or all local conversations. It can evaluate project skills alone or project and global skills together.
Everything runs locally. Never upload transcripts, session files, or any excerpt of them anywhere. The only shareable artifact is the report the user chooses to post.
Let SKILL_ROOT be the directory containing this SKILL.md.
Step 0: Start the run
Verify the executing harness
Read $SKILL_ROOT/references/supported-harnesses.md and identify the harness executing this skill from the runtime context. If it is unsupported or cannot be identified confidently, follow the reference's stop behavior. Do not create a report directory or read conversation history.
Ask which conversations to grade
First check whether the current directory is inside a git repository:
git rev-parse --show-toplevel
Use the harness's user-question tool when available.
When a current repository is available, ask “Which conversations should I grade?” with:
- Conversations in this repository — recommended.
- All conversations.
- Choose projects to analyze.
When there is no current repository, ask the same question with:
- All conversations — recommended.
- Choose projects to analyze.
If the user chooses projects, ask for one or more project paths. Expand and validate every path as a git repository before continuing. The run produces one combined report across those projects.
Ask which skills to evaluate
Then ask “Which skills should I evaluate?” with:
- Project skills + global skills — recommended.
- Project skills only.
For an all-conversations run, “Project skills” means skills from local git repositories inferred from the conversations' working directories. After these answers, proceed immediately.
What ships with it
11 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.
- assets/pierre-diffs.js 1130 KB runs code
- assets/warp-pixel-icon.svg 4.3 KB
- references/skill-improvements.md 1.9 KB
- references/supported-harnesses.md 2.4 KB
- scorers/code-quality.md 4.7 KB
- scorers/efficiency.md 3.3 KB
- scripts/collect_sessions.py 61 KB runs code
- scripts/render_report.py 26 KB runs code
- scripts/test_collect_sessions.py 24 KB runs code
- scripts/test_render_report.py 8.7 KB runs code
- scripts/warp_decoder.py 11 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.
- 6d ago First seen · 172 lines · 55 tokens per session scan A 2cd9eb4d0a70
skill-doctor is a skill published in the GitHub repository warpdotdev/common-skills (537 stars, last pushed 3d ago), licensed MIT. It adds 55 tokens to every session and 2,206 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 2026-08-30.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
chronicle
Analyze Copilot session history for standup reports, usage tips, session search, and session reindexing. Use when the user asks for a standup, daily summary, usage tips, workflow recommendations, wants to search or find past sessions by keyword/file/PR, wants to reindex their session store, or asks about deleting…
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…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…