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
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/mohitagw15856/pm-claude-skillsnpx agentmods add rules/mohitagw15856/pm-claude-skills/pricing-sensitivity-modelWrote 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/pricing-sensitivity-model)<a href="https://agentmods.dev/rules/mohitagw15856/pm-claude-skills/pricing-sensitivity-model"><img src="https://agentmods.dev/badge/rules/mohitagw15856/pm-claude-skills/pricing-sensitivity-model/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/pricing-sensitivity-model"><img src="https://agentmods.dev/badge/rules/mohitagw15856/pm-claude-skills/pricing-sensitivity-model.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 | $0.00109 | $0.00942 |
| Opus 5 | $0.00055 | $0.00471 |
| Sonnet 5 | $0.00022 | $0.00188 |
| Haiku 4.5 | $0.00011 | $0.00094 |
Grade A, and why
pricing-sensitivity-model 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 6d 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 — 51 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Pricing Sensitivity Model (Van Westendorp)
The Van Westendorp Price Sensitivity Meter is fifty years old and still the fastest honest answer to "what should this cost?" — but most readouts are someone squinting at where four lines seem to cross. This skill computes the crossings: cumulative curves built from the actual responses, intersections found by linear interpolation, non-monotone respondents dropped and counted.
Required Inputs
- Survey responses — per respondent, the four classic answers as prices: too cheap (quality suspect), cheap (a bargain), expensive (getting dear), too expensive (out of the question). 20+ valid responses for a stable read; the script warns below that and refuses below 5.
- Segment splits (optional) — the tool doesn't segment; run it per segment and compare, which is usually where the real finding is.
If the survey hasn't run yet, produce the four questions verbatim and the screener instead, then stop — don't invent responses.
Output Format
- The four points — OPP (optimal price point: too-cheap × too-expensive crossing), IPP (indifference: cheap × expensive), and the acceptable range PMC–PME. Each with one sentence of meaning, not just the acronym.
- Data hygiene — valid n, dropped non-monotone count (a high drop rate is itself a finding: respondents didn't understand the category or the questions).
- The recommendation — a price inside the range with reasoning; note that OPP minimises purchase resistance, which is not the same as maximising revenue — premium positions price above OPP deliberately.
- The caveat — VW measures perception, not demand; pair with a real willingness-to-pay test before betting the pricing page on it.
Programmatic Helper
This skill ships scripts/van_westendorp.py — zero dependencies (stdlib zip+XML):
python3 scripts/van_westendorp.py analyze pricing.xlsx --responses-file survey.json
# survey.json: [{"too_cheap":5,"cheap":9,"expensive":18,"too_expensive":30}, …]
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.
- 6d ago First seen · 51 lines · 109 tokens per session scan A fee3f0ba8737
pricing-sensitivity-model is a cursor rule published in the GitHub repository mohitagw15856/pm-claude-skills (1,345 stars, last pushed 2d ago), licensed MIT. It adds 109 tokens to every session and 942 once invoked, about $0.0005 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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