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-optimizergit 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-optimizer)<a href="https://agentmods.dev/skills/mohitagw15856/pm-claude-skills/prompt-optimizer"><img src="https://agentmods.dev/badge/skills/mohitagw15856/pm-claude-skills/prompt-optimizer/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-optimizer"><img src="https://agentmods.dev/badge/skills/mohitagw15856/pm-claude-skills/prompt-optimizer.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 | $0.00086 | $0.00944 |
| Opus 5.5 | $0.00034 | $0.00378 |
| Sonnet 5.5 | $0.00017 | $0.00189 |
| Haiku 4.5 | $0.00009 | $0.00094 |
Grade A, and why
prompt-optimizer 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 — 78 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prompt Optimizer Skill
A weak prompt fails in patterned ways — vague task, no output contract, buried instructions, no examples, or asking for judgement with nothing to ground it. This skill diagnoses which failure mode is in play and rewrites the prompt to fix it, then hands you a way to check the fix held — so "it's flaky" becomes a specific, testable change rather than another round of fiddling.
Working from a brief
You'll often get just the prompt and a vague "it's not working". Always deliver a full rewrite anyway — infer the intended task and output from the prompt's wording, state your assumptions, and rewrite. If the failing behaviour wasn't described, infer the most likely failure mode from the prompt's structure and say so. Never hand back only a critique with no rewritten prompt.
Required Inputs
Ask for these only if they aren't already provided (else infer and label):
- The current prompt — the exact text being used.
- What's going wrong — wrong answers, inconsistent format, refusals, too long/short, hallucinated facts.
- The desired output — what a perfect response looks like (a sample is ideal).
- Context — the model/runtime, whether it's one-shot or part of a chain, and any hard constraints (length, JSON, latency).
Output Format
Prompt Diagnosis & Rewrite
1. Diagnosis — the specific failure mode(s), each tied to the line that causes it:
| Symptom | Likely cause | Fix applied |
|---|---|---|
| Inconsistent format | no explicit output contract | added a schema + example |
| Hallucinated details | asked to answer without grounding | added "use only the provided context; say what's unknown" |
| Ignores an instruction | buried mid-paragraph | moved to a numbered rule near the top |
2. Rewritten prompt — the full new prompt in a fenced block, ready to paste. Apply the levers that fit: role + task in the first lines, an explicit output contract (structure/schema + a short example), grounding rules ("answer only from X; if unknown, say so"), constraints stated as rules not prose, and 1–3 few-shot examples when the task needs a demonstrated pattern.
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 · 78 lines · 86 tokens per session scan A 3ca8ebe8a69e
prompt-optimizer is a skill published in the GitHub repository mohitagw15856/pm-claude-skills (1,449 stars, last pushed today), licensed MIT. It adds 86 tokens to every session and 944 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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