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.
git 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/rules/mohitagw15856/pm-claude-skills/lending-risk-brief)<a href="https://agentmods.dev/rules/mohitagw15856/pm-claude-skills/lending-risk-brief"><img src="https://agentmods.dev/badge/rules/mohitagw15856/pm-claude-skills/lending-risk-brief/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/lending-risk-brief"><img src="https://agentmods.dev/badge/rules/mohitagw15856/pm-claude-skills/lending-risk-brief.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 | $0.00085 | $0.01268 |
| Opus 5 | $0.00043 | $0.00634 |
| Sonnet 5 | $0.00017 | $0.00254 |
| Haiku 4.5 | $0.00009 | $0.00127 |
Grade A, and why
lending-risk-brief 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 8d 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.
Lending Risk Brief Skill
A loan book fails in patterns before it fails in names. This skill writes the portfolio-level brief that makes the patterns visible: where the book is concentrated, which vintages are misbehaving, which way the grades are migrating, what the macro could do to it, and which names need decisions now.
What This Skill Produces
- Concentration analysis: sector, geography, single-name — each against limits
- Vintage performance comparison
- A migration-matrix narrative (not just the matrix)
- Macro-sensitivity scenarios (base / adverse / severe) with named transmission channels
- A top-10 watch-names table
- Recommended actions with owners
Required Inputs
Ask for what's available; compute what the data supports and mark the rest [data gap — request from portfolio systems]:
- Portfolio snapshot — exposures by borrower, sector, geography, grade, origination vintage
- Concentration limits from the risk appetite statement, if set
- Grade migrations this period (upgrades/downgrades by exposure)
- Delinquency/NPL and provision figures, current and prior periods
- Watch-list candidates already known to the team
Portfolio Framework
1. Concentration. Three cuts, each vs its limit (or vs a stated reference norm if no limit exists — and flag the missing limit as a finding): top sector and top-3 sector share; geographic share; single-name — top-10 and top-20 obligor share, largest single exposure vs capital. Concentration that grew via passive drift (runoff elsewhere) deserves the same flag as active growth — say which it was. Correlated concentrations count together (e.g. construction lending + commercial-real-estate collateral is one bet, not two).
2. Vintage performance. Compare cohorts at the same age on-book (delinquency/default at month 12, 24…), not calendar snapshots — a young book always looks clean. A vintage underperforming its age-matched predecessors signals an underwriting-standards question for that origination period; name the period and what changed in criteria then, if known.
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.
- 8d ago First seen · 76 lines · 85 tokens per session scan A 32b19fa238e4
lending-risk-brief is a cursor rule published in the GitHub repository mohitagw15856/pm-claude-skills (1,357 stars, last pushed today), licensed MIT. It adds 85 tokens to every session and 1,268 once invoked, about $0.0004 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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