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/decision-tree-solverWrote 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/decision-tree-solver)<a href="https://agentmods.dev/rules/mohitagw15856/pm-claude-skills/decision-tree-solver"><img src="https://agentmods.dev/badge/rules/mohitagw15856/pm-claude-skills/decision-tree-solver/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/decision-tree-solver"><img src="https://agentmods.dev/badge/rules/mohitagw15856/pm-claude-skills/decision-tree-solver.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 | $0.00115 | $0.01448 |
| Opus 5 | $0.00057 | $0.00724 |
| Sonnet 5 | $0.00023 | $0.00290 |
| Haiku 4.5 | $0.00012 | $0.00145 |
Grade A, and why
decision-tree-solver 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 4d 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.
Decision Tree Solver
"Should we settle or go to trial" is not answered by instinct or by whoever argues longest — it is three numbers and a probability, and most people have never actually multiplied them. This skill extracts the tree hiding inside a messy decision (the choices, the chances, the payoffs, the costs of playing), computes the rollback with the bundled script, and — the part instinct can never do — finds the break-even: the probability at which the recommendation flips. Because "trial is worth it if you win 60% of the time" is an opinion, but "the answer flips at 90% — are you more than 90% sure?" is a decision made tractable. The script is deterministic, stdlib-only, and shows its arithmetic.
What This Skill Produces
- The extracted tree — decisions, chance nodes with probabilities, outcomes with values, and the costs of each path, pulled from the situation as described
- The rollback — expected value at every node, the best choice named at every fork, from the script
- The break-even scan — for each two-way uncertainty, the probability at which the top-level choice flips, which is the number that makes probability arguments productive
- The robustness read — whether the answer survives the probabilities being argued about, or hinges on a number nobody can defend
- The leaves-out list — what expected value cannot see here: risk appetite, one-shot vs repeated, the unquantified costs
Required Inputs
Ask for these if not provided:
- The choices — the real options on the table, including the do-nothing one
- The uncertainties — what could happen under each choice, and the requester's honest probability for each (pushing back on false precision is part of the job)
- The payoffs and costs — the money (or a stated proxy) at each end point, and what each path costs to walk: fees, time priced honestly, deposits
- The stakes context — one-shot or repeatable, and whether the worst branch is survivable — because expected value is the right tool for repeatable bets and needs a caveat for ruinous one-shots
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.
- 4d ago First seen · 77 lines · 115 tokens per session scan A 269a889a8e6a
decision-tree-solver is a cursor rule published in the GitHub repository mohitagw15856/pm-claude-skills (1,345 stars, last pushed 2d ago), licensed MIT. It adds 115 tokens to every session and 1,448 once invoked, about $0.0006 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 2026-09-05.
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