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/poteto/noodle/plannpx skills add poteto/noodle --skill plangit clone --depth 1 https://github.com/poteto/noodleWhat 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 | $0.00061 | $0.01437 |
| Opus 5 | $0.00030 | $0.00718 |
| Sonnet 5 | $0.00012 | $0.00287 |
| Haiku 4.5 | $0.00006 | $0.00144 |
Grade A, and why
plan 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 — 128 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Plan
Produce implementation plans grounded in project principles. Write plans to brain/plans/. Do NOT implement anything — the plan is the deliverable.
Autonomous Session Mode
When this skill runs in a non-interactive Noodle execution session (for example Cook, Oops, or Repair):
- Skip Step 2 (AskUserQuestion) — the scope is fully defined in the initial prompt.
- Skip Step 6's pause — write the plan, commit it, emit
stage_yield(see Step 6), and end the session. Do not wait for human review. - Step 4 (find-skills) — install skills autonomously without confirmation.
All other steps proceed normally.
Use Tasks to track progress. Create a task for each step (TaskCreate), mark each in_progress when starting and completed when done (TaskUpdate). Check TaskList after completing each step.
Step 0 — Triage Complexity
Before running the full planning workflow, assess whether this task actually needs a plan:
Trivially small (1-2 files, obvious approach): Tell the user this task doesn't need a plan and suggest implementing directly without the plan skill. Stop here — do not implement.
Needs planning (proceed to Step 1):
- The change spans 3+ files or introduces new architecture
- There are multiple valid approaches and the user should weigh in
- The task has unclear scope or cross-cutting concerns
- The user explicitly asks for a plan
Step 1 — Load Principles
Read brain/principles.md. Follow every [[wikilink]] and read each linked principle file. These principles govern all plan decisions — cite them by name in the plan overview and phase files.
Do NOT skip this. Do NOT use memorized principle content — always read fresh. The self-check in Step 5b will verify citations exist.
Step 2 — Define Scope and Constraints
Use AskUserQuestion to resolve ambiguity before exploring the codebase:
- What is in scope vs explicitly out of scope?
- Are there constraints (dependencies, platform requirements, existing patterns to preserve)?
- What does "done" look like?
What ships with it
2 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 · 128 lines · 61 tokens per session scan A f429f980350f
plan is a skill published in the GitHub repository poteto/noodle (267 stars, last pushed 5mo ago), licensed MIT. It adds 61 tokens to every session and 1,437 once invoked, about $0.0003 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.