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/experiment-designer)<a href="https://agentmods.dev/rules/mohitagw15856/pm-claude-skills/experiment-designer"><img src="https://agentmods.dev/badge/rules/mohitagw15856/pm-claude-skills/experiment-designer/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/experiment-designer"><img src="https://agentmods.dev/badge/rules/mohitagw15856/pm-claude-skills/experiment-designer.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 | $0.00075 | $0.00876 |
| Opus 5 | $0.00037 | $0.00438 |
| Sonnet 5 | $0.00015 | $0.00175 |
| Haiku 4.5 | $0.00007 | $0.00088 |
Grade A, and why
experiment-designer 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.
How it starts
The opening of the file, as written. The whole thing — 79 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Experiment Designer Skill
Produce rigorous experiment designs from product hypotheses, and interpret results with statistical and practical significance — so you can defend every decision to a sceptical engineering lead or data scientist.
Required Inputs
Ask the user for these if not provided: For experiment design:
- Hypothesis (what change, what metric, what expected movement)
- Current baseline metric value
- Minimum detectable effect (MDE) — the smallest lift worth caring about
- Available daily sample size
For results interpretation:
- Control and variant results (raw numbers or percentages)
- P-value or confidence interval
- Run duration (days)
- Any anomalies observed during the test
Two-Phase Process
Phase 1: Experiment Design
- Restate hypothesis as: "If we [change], we expect [metric] to [move by X%] because [reason]"
- Define control and variant clearly
- Select primary metric (one only) and secondary guardrail metrics (2-3 max)
- Calculate required sample size from MDE and baseline
- Estimate run time in days
- Set pre-defined success criteria before the test runs — no moving goalposts
- Flag design risks: novelty effects, seasonal confounds, multiple testing issues, network effects, sample ratio mismatch
Phase 2: Results Interpretation
- Assess statistical significance (p < 0.05 threshold)
- Assess practical significance: was the lift meaningful for the business, not just real?
- Interpret confidence intervals
- Investigate confounding factors
- Recommend: Ship / Iterate / Kill / Run follow-up test
- Validate — Confirm the test ran for the full planned duration. Flag if it was stopped early (peeking problem). Confirm sample ratio mismatch did not occur.
Output Structure
[Design or Results header based on phase]
Hypothesis: "If we [change], we expect [metric] to [move by X%] because [reason]"
Primary metric: [One metric only] Guardrail metrics: [2-3 max] Required sample size: [n per variant] Estimated run time: [days] Pre-defined success threshold: [specific number] Design risk flags: [any concerns]
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 · 79 lines · 75 tokens per session scan A 562cda638334
experiment-designer is a cursor rule published in the GitHub repository mohitagw15856/pm-claude-skills (1,357 stars, last pushed yesterday), licensed MIT. It adds 75 tokens to every session and 876 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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