PM Skills is a collection of plain-Markdown instructions that teach AI assistants structured methods for handling professional, personal, and life-admin tasks. People use it with Claude, ChatGPT, Gemini, Cursor, Codex, and other supported agents for work such as writing product requirements, reviewing documents, or planning difficult situations.
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 mohitagw15856/pm-claude-skills --skill model-cardgit clone --depth 1 https://github.com/mohitagw15856/pm-claude-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/mohitagw15856/pm-claude-skills/model-card)<a href="https://agentmods.dev/skills/mohitagw15856/pm-claude-skills/model-card"><img src="https://agentmods.dev/badge/skills/mohitagw15856/pm-claude-skills/model-card/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/mohitagw15856/pm-claude-skills/model-card"><img src="https://agentmods.dev/badge/skills/mohitagw15856/pm-claude-skills/model-card.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 | $0.00073 | $0.00915 |
| Opus 5.5 | $0.00029 | $0.00366 |
| Sonnet 5.5 | $0.00015 | $0.00183 |
| Haiku 4.5 | $0.00007 | $0.00092 |
Grade A, and why
model-card 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 — 76 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Model Card Skill
A model card is the README for a model: what it does, what it was trained and evaluated on, where it works, and — most importantly — where it doesn't. It turns an opaque artifact into something a reviewer, a downstream team, or a regulator can actually assess. Write it before launch, not after.
Required Inputs
Ask for these only if they aren't already provided:
- Model name & version, owner team, and date.
- What it does — task type (classification, generation, ranking, extraction…) and the decision it informs.
- Intended use & users — the supported use cases, and explicitly the out-of-scope ones.
- Training data — sources, size, time range, and known gaps (link a
dataset-datasheetif one exists). - Evaluation — datasets, metrics, and results, ideally broken down by subgroup/slice.
- Known limitations & risks — failure modes, bias findings, safety concerns.
Output Format
Model Card: [name] v[version]
Owner: [team] · Date: [date] · Status: [in review / production / deprecated]
1. Overview — one paragraph: what the model does, the decision it serves, and who uses it.
2. Intended Use
- In scope: the use cases this model is validated for.
- Out of scope / do not use for: explicit prohibited or unvalidated uses (this section prevents the most harm).
- Users: who is expected to operate or consume it.
3. Training Data — sources, size, time window, labelling method, and known coverage gaps.
4. Evaluation
- Metrics: the primary metric(s) and why they were chosen for this task.
- Overall results: headline numbers vs. a stated baseline.
- Sliced results: a table of the key metric across important subgroups (geography, language, device, demographic where appropriate) — surface where performance drops, don't hide it behind an average.
| Slice | N | Metric | vs. overall |
|---|
5. Limitations & Failure Modes — concrete situations where it underperforms or should not be trusted.
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 · 76 lines · 73 tokens per session scan A 2eae86ea7cdc
model-card is a skill published in the GitHub repository mohitagw15856/pm-claude-skills (1,449 stars, last pushed today), licensed MIT. It adds 73 tokens to every session and 915 once invoked, about $0.0003 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 2026-10-12.
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