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 capital-allocationgit 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/capital-allocation)<a href="https://agentmods.dev/skills/mohitagw15856/pm-claude-skills/capital-allocation"><img src="https://agentmods.dev/badge/skills/mohitagw15856/pm-claude-skills/capital-allocation/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/capital-allocation"><img src="https://agentmods.dev/badge/skills/mohitagw15856/pm-claude-skills/capital-allocation.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 | $0.00910 |
| Opus 5.5 | $0.00034 | $0.00364 |
| Sonnet 5.5 | $0.00017 | $0.00182 |
| Haiku 4.5 | $0.00009 | $0.00091 |
Grade A, and why
capital-allocation 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 — 77 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Capital Allocation Skill
Allocating capital is the core executive job: a fixed pot, more good ideas than money, and the need to say no on the record. This skill scores initiatives by expected return and strategic fit per unit of cost, allocates against the cap (honouring must-funds), and makes the cut line explicit — so funding is a defensible portfolio choice, not the loudest voice in the room.
Required Inputs
Ask for these only if they aren't already provided:
- The cap — the total budget or headcount to allocate, and the period.
- The initiatives — each with its cost, expected return (revenue, savings, or a strategic value), and strategic fit.
- Constraints — anything that must be funded (compliance, keep-the-lights-on) or can't be partially funded.
- The objective — what you're optimising: near-term return, strategic positioning, or a balance.
Output Format
Capital Allocation: [pot], [period]
1. Objective & cap — what you're optimising and the total available.
2. Scored initiatives — a table; score = expected value × strategic fit, normalised per unit cost:
| Initiative | Cost | Expected return | Strategic fit (1–5) | Score / $ | Must-fund? |
|---|
3. The allocation — funded vs. unfunded against the cap, with budget utilisation. Must-funds first, then highest score/$ until the cap binds.
4. The cut line — the marginal initiative that just missed, and what it would take to fund it (the most useful number for the debate).
5. Rationale & trade-offs — why the portfolio is balanced this way, what's deliberately not funded, and the reversibility of each bet.
6. Re-evaluation triggers — what would change the allocation mid-period (a bet pays off early, a must-fund grows).
Programmatic Helper
scripts/capital_allocate.py (stdlib only) does the allocation deterministically — must-funds first, then
by score-per-cost until the cap binds — and reports the cut line:
# items.json: [{"name":"Mobile revamp","cost":300,"expected_return":900,"strategic_fit":5,"must_fund":false}, ...]
python3 scripts/capital_allocate.py items.json --budget 1000
python3 scripts/capital_allocate.py items.json --budget 1000 --json
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 77 lines · 86 tokens per session scan A e39594ec8149
capital-allocation 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 910 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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