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/dcramer/agents/odienpx skills add dcramer/agents --skill odiegit clone --depth 1 https://github.com/dcramer/agentsWhat 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 | $0.00078 | $0.02157 |
| Opus 5 | $0.00039 | $0.01078 |
| Sonnet 5 | $0.00016 | $0.00431 |
| Haiku 4.5 | $0.00008 | $0.00216 |
Grade A, and why
odie 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 2d 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 — 134 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analyze recurring failure evidence and produce a hard-rule plan: which patterns can be enforced mechanically, which existing tools should enforce them, where the checks should run, and which patterns must remain human judgment.
Contract
- Run when there is recurring or high-signal evidence from transcripts, commit history, Sentry issues, GitHub issues or PRs, CI failures, reviewer feedback, Garfield findings, or an explicit request to codify a repeated failure pattern.
- Treat the source repository as the active repo being improved.
- Prefer evidence that is cross-checked across at least two sources, such as transcript findings plus bugfix commits, or Sentry events plus follow-up PRs.
- Discover existing tooling before recommending new tooling.
- Recommend only hard, mechanically enforceable rules with a clear pass/fail signal.
- Prefer strengthening existing adequate tools over adding duplicate tools; prefer repo-standard or maintained rule frameworks over one-off scripts.
- Prefer existing generalized runners such as
lint,check, ortest. If a separate command is justified, use one aggregate tool runner such aslint:ast-grep, not finding-specific runners such aslint:comments. - Before recommending a custom script, first try to express the exact signal with an existing repo tool, maintained linter rule, structural-search rule, schema check, generated-artifact diff check, or deterministic test helper.
- Recommend custom scripts only for stable repo-specific invariants that cannot be expressed by those frameworks without overmatching and have small valid/invalid fixtures.
- Do not implement checks, install dependencies, edit CI, or change configs unless the user explicitly asks for implementation.
- Do not propose checks for one-off, judgment-heavy, product-requirement, or intentionally accepted tradeoff findings.
- Run a secondary verification pass before final output to remove candidates that still require true human judgment.
- If current tool capabilities, install commands, or migration paths matter and are not already present in the repo, verify them from official docs when possible and cite the source in the handoff.
What ships with it
4 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.
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.
- 2d ago First seen · 134 lines · 78 tokens per session scan A 4ebe43cf00f6
odie is a skill published in the GitHub repository dcramer/agents (54 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 78 tokens to every session and 2,157 once invoked, about $0.0004 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.
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.
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…
agent-host-chat-contributions
Build and review cross-cutting agent-host chat behavior through lifecycle contributions. Use when adding turn lifecycle side effects, prompt or context injection, restored-history transformation, protocol-action observation, or when reviewing changes that add code to AgentSideEffects or AgentService.
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.