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.
git 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/rules/mohitagw15856/pm-claude-skills/ai-eval-plan)<a href="https://agentmods.dev/rules/mohitagw15856/pm-claude-skills/ai-eval-plan"><img src="https://agentmods.dev/badge/rules/mohitagw15856/pm-claude-skills/ai-eval-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/rules/mohitagw15856/pm-claude-skills/ai-eval-plan"><img src="https://agentmods.dev/badge/rules/mohitagw15856/pm-claude-skills/ai-eval-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 | $0.00081 | $0.00900 |
| Opus 5 | $0.00041 | $0.00450 |
| Sonnet 5 | $0.00016 | $0.00180 |
| Haiku 4.5 | $0.00008 | $0.00090 |
Grade A, and why
ai-eval-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 7d 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 — 66 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Eval Plan Skill
You can't improve an AI feature you can't measure, and "it looks good in the demo" is not measurement. This skill produces an evaluation plan that turns a fuzzy quality goal into a repeatable, gated test — so a prompt change that quietly makes outputs worse can't ship.
Required Inputs
Ask for these only if they aren't already provided:
- The feature & task — what the model does and what "good output" means to a user.
- Failure modes that matter — what bad looks like (hallucination, wrong format, unsafe, off-tone, too slow).
- Available data — any real examples, logs, or labelled cases; or note there are none yet.
- Who judges quality — automated checks, an LLM judge, human raters, or a mix.
- The decision this gates — ship/no-ship, model selection, or prompt iteration.
Output Format
Eval Plan: [feature]
1. What we're measuring — the task, and a one-line definition of a good vs. bad response.
2. Eval dataset
- Cases: how many, where they come from (real logs > synthetic), and how they're split (smoke set vs. full set).
- Coverage: the slices/scenarios that must be represented (edge cases, adversarial, each major input type).
- Golden answers / references: present or not, and how they were created.
3. Metrics & rubric
- Per-dimension scores — define each dimension (e.g. correctness, grounding, format, safety, tone) on an explicit 1–5 rubric with anchor descriptions, not vibes.
- Automated checks — deterministic assertions first (valid JSON, contains required fields, no PII, latency budget).
- LLM-as-judge — the judge prompt, the rubric it applies, and how you guard against its bias (calibrate against human labels on a sample).
- Human eval — when it's required (safety, subjective quality) and the rater instructions.
4. Baselines — what each candidate is compared against (current prompt, previous model, a plain-prompt control).
5. The bar — the explicit threshold to ship (e.g. "≥4.2 avg correctness, 0 safety failures, p95 < 3s") and what happens if it's missed.
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.
- 7d ago First seen · 66 lines · 81 tokens per session scan A c3163e29c4cf
ai-eval-plan is a cursor rule published in the GitHub repository mohitagw15856/pm-claude-skills (1,352 stars, last pushed 3d ago), licensed MIT. It adds 81 tokens to every session and 900 once invoked, about $0.0004 per session on Opus 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 2026-09-03.
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