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/rag-architecture-review)<a href="https://agentmods.dev/rules/mohitagw15856/pm-claude-skills/rag-architecture-review"><img src="https://agentmods.dev/badge/rules/mohitagw15856/pm-claude-skills/rag-architecture-review/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/rag-architecture-review"><img src="https://agentmods.dev/badge/rules/mohitagw15856/pm-claude-skills/rag-architecture-review.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 | $0.00084 | $0.00973 |
| Opus 5 | $0.00042 | $0.00487 |
| Sonnet 5 | $0.00017 | $0.00195 |
| Haiku 4.5 | $0.00008 | $0.00097 |
Grade A, and why
rag-architecture-review 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 — 72 lines — stays where its author put it; the contents beside it link to each section on GitHub.
RAG Architecture Review Skill
A RAG system that "hallucinates sometimes" is almost never one bug — it's a chain where the weakest stage caps
quality, and the symptom (a wrong answer) is far from the cause (a chunk that was never retrieved). This skill
reviews an existing pipeline stage by stage, isolates where quality leaks, and ranks fixes by impact so you
work the biggest lever first. (Designing a new system from scratch? Use rag-design-doc.)
Working from a brief
Given a partial description ("it uses pgvector and sometimes makes things up"), deliver the full staged review anyway — infer the likely setup for each unstated stage, label the inference, and flag what to confirm. Never withhold the review for missing detail; a labelled assumption plus "confirm this" beats a blank.
Required Inputs
Ask for these only if they aren't already provided (else infer and label):
- The current architecture — ingestion, chunking, embedding model, vector store, retrieval (top-k, hybrid?), reranking, and the generation prompt.
- The symptoms — examples of bad answers (wrong, ungrounded, stale, refuses) with the expected answer.
- The corpus — what's retrieved over, its size, structure, and update frequency.
- Constraints — latency, cost, and per-tenant/permission isolation needs.
Output Format
RAG Review: [system]
1. Summary — the headline: where quality is leaking and the top 3 fixes, in priority order.
2. Stage-by-stage findings — for each stage, what's working, what's not, and why:
| Stage | Finding | Severity | Root cause | Fix |
|---|---|---|---|---|
| Chunking | 1500-tok fixed chunks split tables mid-row | High | structure-blind splitting | structure-aware chunking + metadata |
| Retrieval | pure vector, no keyword | High | exact IDs/terms missed | add hybrid (BM25 + dense) |
| Generation | weak grounding instruction | Med | model answers from prior | "answer only from context; else say unknown" |
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 · 72 lines · 84 tokens per session scan A db6fc238a212
rag-architecture-review is a cursor rule published in the GitHub repository mohitagw15856/pm-claude-skills (1,357 stars, last pushed today), licensed MIT. It adds 84 tokens to every session and 973 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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