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/grimoire-rs/grimoire/bugfixnpx skills add grimoire-rs/grimoire --skill bugfixgit clone --depth 1 https://github.com/grimoire-rs/grimoireWrote 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/grimoire-rs/grimoire/bugfix)<a href="https://agentmods.dev/skills/grimoire-rs/grimoire/bugfix"><img src="https://agentmods.dev/badge/skills/grimoire-rs/grimoire/bugfix.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 | $0.00092 | $0.01535 |
| Opus 5 | $0.00046 | $0.00767 |
| Sonnet 5 | $0.00018 | $0.00307 |
| Haiku 4.5 | $0.00009 | $0.00153 |
Grade A, and why
bugfix 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 3d 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 — 127 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/bugfix — Guided Bug-Fix Workflow
Guard rail for the #1 bug-fix failure: fixing before a failing test exists.
This skill walks workflow-bugfix.md (the
single source of truth) and adds one hard, non-skippable gate — Phase 3 must
produce a test that fails on the current code before any fix is written.
Why this exists: the rule alone was repeatedly skipped. This skill makes the order explicit and the gate blocking. Read
workflow-bugfix.mdfor the full rationale; this file is the executable checklist.
The non-negotiable sequence
Reproduce → Root-Cause Analysis → FAILING TEST → Fix → Verify → Review → Commit
Each phase finishes before the next starts. The only way past Phase 3 is a test that you have run and watched fail for the right reason.
Phase 1 — Reproduce (no guessing)
- State the exact wrong behavior: command, input, observed vs expected.
- Find the surface: which entry point(s)? CLI command, TUI path, library seam? A bug often lives on more than one surface (e.g. a CLI path AND its TUI twin); list every one that shares the broken code.
- Confirm it actually reproduces. If you can't reproduce it, keep digging — no speculative fixes.
Write a one-line repro: "<cmd/inputs> → <observed>; expected <expected>".
Phase 2 — Root-Cause Analysis (write it down)
-
Trace the symptom to the line and the condition that made it fire.
-
Output a root-cause statement in this exact shape — no "error on line N":
X happens because Y, introduced by/located at Z.
-
Single bug or a pattern? Grep for the same defect elsewhere (sibling call sites, the other surface from Phase 1). If it's a pattern, the fix covers all instances.
-
If the real cause needs an architectural change, stop and escalate to the feature workflow with a plan artifact — don't paper over it.
Phase 3 — Failing test FIRST 🚧 GATE
Do not edit any production code until this phase is done.
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.
- 3d ago First seen · 127 lines · 92 tokens per session scan A 20942bc18c96
bugfix is a skill published in the GitHub repository grimoire-rs/grimoire (8 stars, last pushed 4d ago), licensed Apache-2.0. It adds 92 tokens to every session and 1,535 once invoked, about $0.0005 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…