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/codagent-ai/agent-skills/specnpx skills add Codagent-AI/agent-skills --skill specgit clone --depth 1 https://github.com/Codagent-AI/agent-skillsWhat 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 | $0.00036 | $0.00541 |
| Opus 5 | $0.00018 | $0.00270 |
| Sonnet 5 | $0.00007 | $0.00108 |
| Haiku 4.5 | $0.00004 | $0.00054 |
Grade A, and why
spec 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 — 63 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Spec
Turn the proposal's capabilities into testable behavioral requirements through collaborative discovery. Specifications define observable behavior, not architecture or implementation.
Do not write a capability's spec until you have presented its proposed requirements and scenarios and the user has approved them.
Process
- Read the proposal and related existing specifications. Use the proposal's capability names to determine the new or modified spec files. For modified capabilities, locate the current requirement blocks before drafting the delta.
- Work through capabilities one at a time. Use
codagent:ask-questionsto resolve material behavior, boundaries, errors, and edge cases. Ask only questions that affect observable behavior or scope, recommend a default when useful, and do not use a generic approval question as discovery. - Present the complete proposed requirements and scenarios for that capability, including consequential assumptions or defaults inferred from context.
- After approval, write the spec file using the format below. Continue until each proposal capability has a corresponding spec.
If behavior depends on an unresolved architectural choice, specify as much as is currently knowable and
add <!-- deferred-to-design: <reason> --> to the affected scenario. The design phase will complete or
revise it. Do not ask architecture or implementation questions during specification.
Report the relative paths created. Do not invoke another lifecycle skill.
OpenSpec format
- Use one file per capability:
specs/<capability>/spec.md. - Write normative requirements with SHALL or MUST.
- Use
### Requirement: <name>and at least one#### Scenario: <name>per requirement. - Scenarios describe observable WHEN/THEN behavior. Exactly four hashes on scenario headings are required by the parser.
- Avoid scenarios about file contents, configuration structure, or skill text unless those are the actual public contract.
Use delta sections as applicable:
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 · 63 lines · 36 tokens per session scan A 52bf65f8e40a
spec is a skill published in the GitHub repository Codagent-AI/agent-skills (30 stars, last pushed 1mo ago), licensed MIT. It adds 36 tokens to every session and 541 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
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…