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/tyrchen/rust-lib-template/researchnpx skills add tyrchen/rust-lib-template --skill researchgit clone --depth 1 https://github.com/tyrchen/rust-lib-templateWhat 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 | $0.00139 | $0.02935 |
| Opus 5 | $0.00069 | $0.01468 |
| Sonnet 5 | $0.00028 | $0.00587 |
| Haiku 4.5 | $0.00014 | $0.00294 |
Grade A, and why
research 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 — 212 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research
Capture prior art with rigour: vendor the upstream code, read it deeply, write a memo that future you (and the spec / impl skills) can rely on. Memos are load-bearing; they pin assumptions before code is written so spec drift and rework do not happen later.
When this fires
- "do deep research on
<repo>" / "study how<repo>works" - "submodule
<urls>to./vendors" / "vendor<repo>for reference" - "before we design X, look into how
<crate>does it" - "spike on
<assumption>" — a single-question, time-boxed memo - The user pastes GitHub URLs and asks Codex to learn from them
- The spec or impl skill needs prior-art before proceeding and there is no memo yet
If ./docs/research/ already contains a relevant memo, read it first and decide whether to update it instead of writing a new one. Do not duplicate.
What to produce
For each topic, exactly one memo at ./docs/research/<kind>-<slug>.md plus an updated ./docs/index.md (or wherever the project's AGENTS.md says research lives). Three memo kinds, picked by intent:
spike-<slug>.md— a single, sharp, time-boxed question ("doesArcSwap<Arc<dyn T>>compose?", "islinkmereliable on macOS arm64 release+LTO?"). Validates one assumption with a runnable artefact. ≤ 2 pages.study-<slug>.md— a deep-dive into one or more vendored repos ("howtokio-rs/tracingresolves dispatcher per call site", "howdefmtinterns log strings", "comparing howprost/quick-protobuf/buffahandle unknown fields"). 3–10 pages, cites file paths and line numbers.survey-<slug>.md— pure web / docs research where vendoring is not warranted ("latestaxummiddleware patterns", "current state of Rust async cancellation"). Cite the latest stable version of each source, link to upstream docs / blog posts / RFCs, and note the date — surveys go stale faster than spikes or studies.
Always pick the narrowest kind that fits; specificity beats breadth.
Diagram expectation
What ships with it
1 file 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 · 212 lines · 139 tokens per session scan A 1dac4873ed52
research is a skill published in the GitHub repository tyrchen/rust-lib-template (51 stars, last pushed 3mo ago), licensed MIT. It adds 139 tokens to every session and 2,935 once invoked, about $0.0007 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.
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…