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 kyc-escalationgit 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/kyc-escalation)<a href="https://agentmods.dev/skills/mohitagw15856/pm-claude-skills/kyc-escalation"><img src="https://agentmods.dev/badge/skills/mohitagw15856/pm-claude-skills/kyc-escalation/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/kyc-escalation"><img src="https://agentmods.dev/badge/skills/mohitagw15856/pm-claude-skills/kyc-escalation.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 | $0.00093 | $0.01348 |
| Opus 5.5 | $0.00037 | $0.00539 |
| Sonnet 5.5 | $0.00019 | $0.00270 |
| Haiku 4.5 | $0.00009 | $0.00135 |
Grade A, and why
kyc-escalation 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 — 79 lines — stays where its author put it; the contents beside it link to each section on GitHub.
KYC Escalation Skill
This skill helps compliance teams document internal escalations well: facts separated from inference, red flags mapped to a taxonomy, and a recommendation the MLRO or reporting decision-maker can act on. It documents and escalates — it does not decide, and it does not draft regulatory reports.
Boundaries — apply these without exception. Never draft a SAR/STR or its narrative — that is the designated reporting officer's regulated act; this memo is the internal input to that decision. Never advise on structuring transactions, avoiding detection, or evading monitoring, for any party. Never include tipping-off risk material — the memo is internal-only; do not draft customer-facing language about the investigation.
What This Skill Produces
- A factual, time-stamped trigger description
- A profile-vs-activity mismatch analysis (expected vs observed)
- Red flags mapped to a taxonomy, each with its supporting fact
- An "information still needed" list with sources
- A recommendation: clear / enhanced due diligence / exit consideration / refer to reporting decision-maker — with rationale
Required Inputs
Ask for what's missing; never fabricate transaction details — mark gaps [not in file]:
- Trigger — the alert, transaction(s), or observation, with dates, amounts, counterparties
- Customer profile — KYC file basics: stated occupation/business, expected activity, source of funds/wealth, tenure, risk rating
- Activity history — recent pattern for context
- Prior alerts or escalations on this customer
Escalation Framework
1. Trigger description — facts only. What was observed, when, in what amounts, involving whom. Time-stamp everything. No adjectives, no inference — "three cash deposits of 9,400–9,800 on consecutive days", not "obvious structuring".
2. Profile vs activity mismatch. Two columns: what the KYC file says to expect (business type, turnover, geographies, counterparties, channels) vs what was observed. The mismatch — or its absence — is the analytical core. An alert consistent with a well-documented profile may support "clear"; activity inconsistent with the file is what escalates.
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 · 79 lines · 93 tokens per session scan A b31bef23160d
kyc-escalation is a skill published in the GitHub repository mohitagw15856/pm-claude-skills (1,449 stars, last pushed today), licensed MIT. It adds 93 tokens to every session and 1,348 once invoked, about $0.0004 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.
Other skills, from other repositories
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…