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/offer-comparisonWrote 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/offer-comparison)<a href="https://agentmods.dev/rules/mohitagw15856/pm-claude-skills/offer-comparison"><img src="https://agentmods.dev/badge/rules/mohitagw15856/pm-claude-skills/offer-comparison/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/offer-comparison"><img src="https://agentmods.dev/badge/rules/mohitagw15856/pm-claude-skills/offer-comparison.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 | $0.00092 | $0.00941 |
| Opus 5 | $0.00046 | $0.00470 |
| Sonnet 5 | $0.00018 | $0.00188 |
| Haiku 4.5 | $0.00009 | $0.00094 |
Grade A, and why
offer-comparison 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 — 76 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Offer Comparison Skill
Offers are quoted as feelings — "the startup has more upside" — but they resolve to numbers with dates on them. This skill computes the curves: what each offer pays in each of the next four years, where the lines cross, and which lever in the weaker offer would actually move it.
What This Skill Produces
- The comp table — per-year and cumulative totals per offer, from the script
- The crossover analysis — which offer leads when, and what assumption that ranking is hostage to
- The risk translation — private equity restated honestly rather than at face value
- Negotiation levers — ranked by dollar impact per unit of asking-awkwardness
Required Inputs
Ask for these if not provided:
- Per offer: base, bonus %, equity grant value, vest years, cliff months, vest frequency, 401(k) match (% and cap), any promised refreshers
- The user's horizon — expecting to stay 2 years or 4 changes the answer, because cliffs do
- Equity risk view — public RSUs count at face; for private equity, agree a discount with the user (e.g. 50–75% haircut pre-Series B) and pass the discounted number to the script labeled as such
Programmatic Helper
python3 scripts/offer_comparison.py offers.json
cat offers.json | python3 scripts/offer_comparison.py - --json
Input shape in the script docstring. The script computes vesting month-by-month (a 12-month cliff releases the accrued year), bonuses and match annually, and reports the cumulative leader and crossover year. It values equity at exactly the number you give it — the risk adjustment is your input, visible, never a hidden assumption.
Framework: The Judgment Around the Math
- The cliff vs the horizon — an 18-month expected stay makes year-4 equity fiction; compare at the user's actual horizon, not the grant's
- A risky dollar ≠ a salary dollar — never compare private paper to cash 1:1; show the comparison at 2–3 discount levels if the user resists picking one
- Refreshers are policy, not promise — model them only if written down; otherwise mention them as upside outside the table
- Levers, ranked: base (compounds into bonus and match) → equity grant → signing bonus (one-time, easiest yes) → cliff/start-date adjustments
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 · 76 lines · 92 tokens per session scan A c4aa6f61d477
offer-comparison is a cursor rule published in the GitHub repository mohitagw15856/pm-claude-skills (1,352 stars, last pushed 3d ago), licensed MIT. It adds 92 tokens to every session and 941 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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