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 dataset-datasheetgit 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/dataset-datasheet)<a href="https://agentmods.dev/skills/mohitagw15856/pm-claude-skills/dataset-datasheet"><img src="https://agentmods.dev/badge/skills/mohitagw15856/pm-claude-skills/dataset-datasheet/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/dataset-datasheet"><img src="https://agentmods.dev/badge/skills/mohitagw15856/pm-claude-skills/dataset-datasheet.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 | $0.00081 | $0.00868 |
| Opus 5.5 | $0.00032 | $0.00347 |
| Sonnet 5.5 | $0.00016 | $0.00174 |
| Haiku 4.5 | $0.00008 | $0.00087 |
Grade A, and why
dataset-datasheet 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 — 73 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Dataset Datasheet Skill
Models inherit the flaws of their data, and most data debt is invisible because nobody wrote down where the data came from. A datasheet is that record: how the dataset was collected, what's in it, what's missing, and what it should not be used for. It's the difference between a reusable asset and a liability.
Required Inputs
Ask for these only if they aren't already provided:
- Dataset name, version, owner and what it's used for today.
- Motivation — why it was created and for what task.
- Composition — what an instance is, how many, fields/labels, and time range.
- Collection — sources, method (scraped, logged, purchased, annotated), and consent/licensing basis.
- Known issues — gaps, imbalances, label noise, sensitive attributes, duplicates.
Output Format
Datasheet: [dataset] v[version]
Owner: [team] · Created: [date] · License: [license]
1. Motivation — why this dataset exists, the task it serves, and who funded/created it.
2. Composition
- What a single instance represents; total count; the schema (fields, label definitions).
- Class/label balance and key distributions (and notable skews).
- Sensitive attributes present (directly or by proxy), and whether individuals are identifiable.
- Known missing data, duplicates, or noise.
3. Collection process — sources, mechanism (scrape/log/survey/annotation), time window, sampling strategy, and the legal/consent basis (license, ToS, opt-in).
4. Preprocessing / labelling — cleaning, dedup, filtering, and how labels were produced (who annotated, guidelines, inter-annotator agreement).
5. Recommended uses & limits
- Appropriate uses: tasks this data supports well.
- Do not use for: tasks where its biases/gaps would cause harm or invalid results.
6. Distribution & access — who can use it, how it's shared, and tenancy/PII handling.
7. Maintenance — owner, update cadence, versioning, and how errors get reported and fixed.
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 · 73 lines · 81 tokens per session scan A 40683516a6f3
dataset-datasheet is a skill published in the GitHub repository mohitagw15856/pm-claude-skills (1,449 stars, last pushed today), licensed MIT. It adds 81 tokens to every session and 868 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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