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 skills add mimukit/skills --skill plankitgit clone --depth 1 https://github.com/mimukit/skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/mimukit/skills/plankit)<a href="https://agentmods.dev/skills/mimukit/skills/plankit"><img src="https://agentmods.dev/badge/skills/mimukit/skills/plankit/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/mimukit/skills/plankit"><img src="https://agentmods.dev/badge/skills/mimukit/skills/plankit.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What 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 5.1 | $0.00090 | $0.03665 |
| Opus 5.5 | $0.00036 | $0.01466 |
| Sonnet 5.5 | $0.00018 | $0.00733 |
| Haiku 4.5 | $0.00009 | $0.00366 |
Grade A, and why
plankit 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 — 150 lines — stays where its author put it; the contents beside it link to each section on GitHub.
plankit
Turn a rough idea (a feature, a project, a spec, a PRD) into a structured plan document you can act on. plankit is generative: it brainstorms the approach, settles the decisions needed for a coherent draft, and writes a NNNN-plan-<slug>-YYYY-MM-DD.md grounded in the real codebase (not a guess). It is the front of a flow, plankit drafts → grillkit hardens → issuekit files where a project tracks work in GitHub Issues, and plankit drafts → grillkit hardens → implementkit builds where it doesn't. Either way the plan it writes is the exact input the next step expects. plankit plans only: it never writes application code and never creates issues.
When this fires
The user wants to think a piece of work through before building it: "plan this feature", "brainstorm a plan / PRD / spec", "write a plan doc", "help me think through X first", "/plankit". One distinction matters:
- It is not the adversarial interrogator. That's grillkit. plankit asks enough to draft a coherent plan and records the thin spots as open questions; grillkit is what pressure-tests them one decision at a time.
Procedure
1. Capture the idea
Get the concept, the problem it solves, who it's for, and the outcome that means success. The success outcome is the goal behind the request, meaning what the work is for and not only what it builds ("keep the app crash-free", not "fix this crash"), plus a target: the number that shows it worked. If the input is a one-liner, ask a few scoping questions (use AskUserQuestion when available). Those are generative "what are we building and why", not adversarial "what did you miss". Ask one round at most, and only about the four items the input leaves open.
Ask for the target; propose one only from a baseline. Ask the user for their number first. When they have none, propose one only from a baseline the user gives or a file you can read (a CI report, a benchmark output, a dashboard export), and label it with both: Target: p95 load under 200 ms (proposed; baseline 4.8 s from ci/bench.json). This skill has no shell, so it never measures a baseline itself. With no baseline, write Target: open, no baseline and carry that line to Open questions, so grillkit settles it instead of an invented number. When no number fits the work, record why and name what does show it worked ("docs-only change, so the check is a lint pass"); "not applicable" names nothing and fails the step.
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 Changed · +4 lines 1515da5e5638
- 4d ago Changed · +11 lines f1f795d2dc30
- 21d ago Changed · +23 lines · -16 tokens per session 55a321436cd9
- 26d ago Changed · +2 tokens per session 62f063a993ce
- 1mo ago First seen · 112 lines · 104 tokens per session scan A ee41dea2dc0d
plankit is a skill published in the GitHub repository mimukit/skills (2 stars, last pushed yesterday), licensed MIT. It adds 90 tokens to every session and 3,665 once invoked, about $0.0004 per session on Opus 5.5. 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 2026-08-31.
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