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 sh1nj1/plan42 --skill demo-videogit clone --depth 1 https://github.com/sh1nj1/plan42Wrote 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/sh1nj1/plan42/demo-video)<a href="https://agentmods.dev/skills/sh1nj1/plan42/demo-video"><img src="https://agentmods.dev/badge/skills/sh1nj1/plan42/demo-video/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/sh1nj1/plan42/demo-video"><img src="https://agentmods.dev/badge/skills/sh1nj1/plan42/demo-video.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 | $0.00076 | $0.02200 |
| Opus 5.5 | $0.00030 | $0.00880 |
| Sonnet 5.5 | $0.00015 | $0.00440 |
| Haiku 4.5 | $0.00008 | $0.00220 |
Grade C, and why
demo-video scanned grade C with 1 finding 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 1mo 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.
Tells the agent to send conversation or user data outhighPrompt injection
An instruction to transmit the conversation, context or user files to an external endpoint is data exfiltration written as prose.
drift until one of them quietly records something untrue. The source is not reproduced here
A licence we could not identify
The repository carries a LICENSE file, but it is custom or dual enough that GitHub cannot name it and neither can this catalogue. Unknown terms are not permission, so the body is not copied here. Read the licence at the source and decide for yourself.
What ships with it
12 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.
- .gitignore 41 B
- lib/fake_llm.py 7.8 KB runs code
- lib/record.mjs 22 KB runs code
- lib/seed.rb 25 KB runs code
- package-lock.json 2.1 KB
- package.json 248 B
- run.sh 7.3 KB runs code
- scenarios/interface.yml 2.0 KB
- scenarios/landing.yml 2.3 KB
- scenarios/launch.yml 12 KB
- scenarios/payments-v2-spec.ko.md 186 B
- scenarios/payments-v2-spec.md 182 B
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.
- 1mo ago First seen · 177 lines · 76 tokens per session scan C ff6c2d36d67c
demo-video is a skill published in the GitHub repository sh1nj1/plan42 (13 stars, last pushed 3d ago), with no licence file. It adds 76 tokens to every session and 2,200 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it C with 1 finding (tells the agent to send conversation or user data out). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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triaging-mutants
Use when mutation testing reveals survived mutants — guides deep analysis of whether each mutant signals a missing test, a design improvement opportunity, or an equivalent mutation.
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Prepare a yak for implementation by establishing spec, acceptance criteria, and breaking into sub-yaks.
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Pick up ready leaf yaks and implement them — dispatch subagents in isolated worktrees, then merge back to main.
preparing-a-yak
Use when a yak needs requirements, examples, and a plan before implementation - prepares a yak so it's ready for subagent-driven development.