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 steph-dove/klaussy-agents --skill plangit clone --depth 1 https://github.com/steph-dove/klaussy-agentsWrote 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/steph-dove/klaussy-agents/plan)<a href="https://agentmods.dev/skills/steph-dove/klaussy-agents/plan"><img src="https://agentmods.dev/badge/skills/steph-dove/klaussy-agents/plan/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/steph-dove/klaussy-agents/plan"><img src="https://agentmods.dev/badge/skills/steph-dove/klaussy-agents/plan.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 | $0.00053 | $0.03388 |
| Opus 5 | $0.00026 | $0.01694 |
| Sonnet 5 | $0.00011 | $0.00678 |
| Haiku 4.5 | $0.00005 | $0.00339 |
Grade A, and why
{{REPO}}-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 9d 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.
This is a copy
91% identical to fastapi-plan — 14 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 175 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are helping plan and implement a task in this repo. Follow these phases in order — do NOT skip Phase 3 (clarifying questions).
Output file: the approved plan is written to plan.md at the repo root. Phase 6 reads it back as the source-of-truth checklist and updates checkboxes as work proceeds, so a fresh session can resume mid-task by re-reading plan.md. The file is gitignored.
Use TodoWrite throughout: create one task per phase up front, mark each in_progress when starting and completed when done. The flow is long-running, and the todo list keeps the user oriented.
Hard cap on sub-agents: This skill spawns up to 2-3 explore agents (Phase 2), 2-3 architects (Phase 4), and 3 reviewers (Phase 7) — at most 9 Agent invocations total across the whole flow. Do NOT exceed that cap. If you find yourself wanting a 10th invocation (retrying a failed agent, spawning a "just one more" specialist), stop and summarize what you have for the user instead. Retries hide failures; extra specialists are scope creep.
Phase 1 — Discovery
Restate the user's request in your own words: what is being built, what problem it solves, what success looks like. Identify constraints, non-goals, and any ticket reference in the task description.
Surface-level ambiguity check — before launching parallel exploration in Phase 2 (which costs 2-3 agent invocations), make sure you can answer all of these:
- Can you name the thing being built in one sentence (a feature, a fix, a refactor)?
- Do you know the user-visible surface it touches (an endpoint, a screen, a command)?
- Is success observable (a behavior change you could write a test for)?
If any answer is "no", ask the user before exploring. Phase 3 covers the deeper "what should error handling do" / "what about edge case X" questions; Phase 1 catches the "do I even know what they want" case so the parallel agents don't waste effort on the wrong target.
Referenced-asset check — block, don't invent. If the task or ticket points at material you need but cannot actually retrieve — a mockup, screenshot, or design file attached to a GitHub/Jira issue; a Figma link; an image, spec, or doc you have no tool to open — do NOT proceed by guessing what it contains. A ticket CLI (gh issue view, glab issue view, and the Jira equivalents) shows an issue's text but does not download its image attachments, and a design you can't see is not a design you can fabricate. Stop and tell the user exactly which assets you're missing and ask them to provide them (paste the image, drop the file into the repo, share the copy/measurements). Never make up UI text, layout, spacing, colors, or copy to fill the gap — a plausible-looking invention is worse than a blocked task, because it looks done. This is a hard block: planning cannot continue past a design the human hasn't given you.
Confirm with the user before continuing.
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.
- 9d ago First seen · 175 lines · 0 tokens per session scan A 6375d00c9138
{{REPO}}-plan is a skill published in the GitHub repository steph-dove/klaussy-agents (16 stars, last pushed 15d ago), licensed MIT. It adds 53 tokens to every session and 3,388 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 91% identical to fastapi-plan, differing in 14 lines, and is treated as a copy.
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