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 prompt-regression-suitegit 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/prompt-regression-suite)<a href="https://agentmods.dev/skills/mohitagw15856/pm-claude-skills/prompt-regression-suite"><img src="https://agentmods.dev/badge/skills/mohitagw15856/pm-claude-skills/prompt-regression-suite/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/prompt-regression-suite"><img src="https://agentmods.dev/badge/skills/mohitagw15856/pm-claude-skills/prompt-regression-suite.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 | $0.00102 | $0.01224 |
| Opus 5.5 | $0.00041 | $0.00490 |
| Sonnet 5.5 | $0.00020 | $0.00245 |
| Haiku 4.5 | $0.00010 | $0.00122 |
Grade A, and why
prompt-regression-suite 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 — 98 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prompt Regression Suite Skill
Every prompt tweak, model upgrade, and context change is a deploy. This skill designs the suite that runs on each one and answers a single question: did anything that used to work stop working?
What This Skill Produces
- A golden case set: curated inputs with per-case pass criteria
- Scoring methods per case class (exact, rubric-judge, property checks)
- CI gate thresholds — what blocks a merge vs. what warns
- A failure triage protocol — flaky vs. regressed vs. golden-set-wrong
Required Inputs
Ask for (if not already provided):
- The feature and its contract — what the LLM step receives and must produce
- What has broken before (or nearly) — past incidents seed the best cases
- Real traffic examples — 10-20 representative inputs, including ugly ones
- What triggers a run — prompt edits, model bumps, retrieval changes, all of the above?
Building the Golden Set
Compose the set from four deliberate classes — not a random sample:
| Class | Purpose | Share |
|---|---|---|
| Core paths | The 5-10 inputs that represent most real traffic | ~40% |
| Past failures | Every input that caused a bug, complaint, or incident — permanently | ~25% |
| Edge & adversarial | Empty/huge inputs, wrong language, injection attempts, off-topic | ~25% |
| Canaries | Cases pinned to behaviours you never want to change (refusals, format, tone) | ~10% |
Keep it small enough to run on every change (30-80 cases beats 500 nobody runs). Version it in git next to the prompt.
Scoring Per Case
Choose the cheapest check that catches the regression:
- Exact / structural — JSON parses, required fields present, enum values legal. Free and deterministic; use wherever the contract is structural.
- Property checks — output contains/never-contains X, length bounds, citation count. Deterministic proxies for quality.
- LLM-as-judge with a rubric — only where judgement is unavoidable. Pin the judge model + rubric version, score against the baseline output, and spot-check judge agreement with a human on ~20 cases before trusting it.
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 · 98 lines · 102 tokens per session scan A a9a882ee7695
prompt-regression-suite is a skill published in the GitHub repository mohitagw15856/pm-claude-skills (1,449 stars, last pushed today), licensed MIT. It adds 102 tokens to every session and 1,224 once invoked, about $0.0004 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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