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/manhvann/codexkit/cooknpx skills add manhvann/codexkit --skill cookgit clone --depth 1 https://github.com/manhvann/codexkitWhat 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 | $0.00019 | $0.01254 |
| Opus 5 | $0.00010 | $0.00627 |
| Sonnet 5 | $0.00004 | $0.00251 |
| Haiku 4.5 | $0.00002 | $0.00125 |
Grade A, and why
ck:cook 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 — 116 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Cook - Smart Feature Implementation
End-to-end implementation with automatic workflow detection.
Principles: YAGNI, KISS, DRY | Token efficiency | Concise reports
Usage
$cook <natural language task OR plan path>
IMPORTANT: If no flag is provided, the skill will use the interactive mode by default for the workflow.
Optional flags to select the workflow mode:
--interactive: Full workflow with user input (default)--fast: Skip research, scout→plan→code--parallel: Multi-agent execution--no-test: Skip testing step--auto: Auto-approve all steps
Example:
$cook "Add user authentication to the app" --fast
$cook path/to/plan.md --auto
Smart Intent Detection
| Input Pattern | Detected Mode | Behavior |
|---|---|---|
Path to plan.md or phase-*.md |
code | Execute existing plan |
| Contains "fast", "quick" | fast | Skip research, scout→plan→code |
| Contains "trust me", "auto" | auto | Auto-approve all steps |
| Lists 3+ features OR "parallel" | parallel | Multi-agent execution |
| Contains "no test", "skip test" | no-test | Skip testing step |
| Default | interactive | Full workflow with user input |
See references/intent-detection.md for detection logic.
Workflow Overview
[Intent Detection] → [Research?] → [Review] → [Plan] → [Review] → [Implement] → [Review] → [Test?] → [Review] → [Finalize]
Default (non-auto): Stops at [Review] gates for human approval before each major step.
Auto mode (--auto): Skips human review gates, implements all phases continuously.
Task tracking: Use the available task-tracking tools when the runtime exposes them. If task tools are unavailable, keep an explicit checklist in the response and plan files instead.
| Mode | Research | Testing | Review Gates | Phase Progression |
|---|---|---|---|---|
| interactive | ✓ | ✓ | User approval at each step | One at a time |
| auto | ✓ | ✓ | Auto if score≥9.5 | All at once (no stops) |
| fast | ✗ | ✓ | User approval at each step | One at a time |
| parallel | Optional | ✓ | User approval at each step | Parallel groups |
| no-test | ✓ | ✗ | User approval at each step | One at a time |
| code | ✗ | ✓ | User approval at each step | Per plan |
What ships with it
5 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 · 116 lines · 19 tokens per session scan A f05d6bce6560
ck:cook is a skill published in the GitHub repository manhvann/codexkit (88 stars, last pushed 4d ago), licensed MIT. It adds 19 tokens to every session and 1,254 once invoked, about $0.0001 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…