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 rag-design-docgit 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/rag-design-doc)<a href="https://agentmods.dev/skills/mohitagw15856/pm-claude-skills/rag-design-doc"><img src="https://agentmods.dev/badge/skills/mohitagw15856/pm-claude-skills/rag-design-doc/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/rag-design-doc"><img src="https://agentmods.dev/badge/skills/mohitagw15856/pm-claude-skills/rag-design-doc.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 | $0.00092 | $0.01042 |
| Opus 5.5 | $0.00037 | $0.00417 |
| Sonnet 5.5 | $0.00018 | $0.00208 |
| Haiku 4.5 | $0.00009 | $0.00104 |
Grade A, and why
rag-design-doc 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 — 78 lines — stays where its author put it; the contents beside it link to each section on GitHub.
RAG Design Doc Skill
Most RAG systems fail not at generation but at retrieval — the model answers confidently from the wrong chunks. This skill forces the decisions that actually determine quality (chunking, retrieval, reranking, grounding) and pairs each with how you'll evaluate it, so "it hallucinates sometimes" becomes a diagnosable, fixable pipeline.
Required Inputs
Ask for these only if they aren't already provided:
- Corpus — what's being retrieved over (docs, tickets, code, tables), size, and update frequency.
- Queries — the kinds of questions users ask, and how precise/recall-sensitive they are.
- Grounding requirement — must answers cite sources? Is "I don't know" acceptable (it should be)?
- Constraints — latency budget, cost, privacy/tenancy (per-customer isolation?), and freshness needs.
Output Format
RAG Design: [system]
1. Goal & non-goals — what questions it answers well, and what it explicitly won't do.
2. Ingestion & chunking
- Source connectors and refresh strategy (full re-index vs. incremental).
- Chunking: strategy (fixed, recursive, semantic, structure-aware), size + overlap, and what metadata travels with each chunk (source, section, timestamp, permissions). Chunking is the highest-leverage choice — justify it.
3. Embeddings & index — embedding model + dimension, vector store, and the index/filter strategy (incl. metadata filters and per-tenant isolation).
4. Retrieval — top-k, hybrid (dense + keyword/BM25) vs. pure vector, metadata pre-filtering, and query transformation (rewriting, decomposition, HyDE) if used.
5. Reranking — whether a cross-encoder/reranker narrows the candidate set before generation, and the final context budget.
6. Generation — the prompt template, how retrieved context is formatted, the instruction to answer only from context and say "I don't know" otherwise, and how citations are produced and verified.
7. Evaluation — retrieval metrics (recall@k, MRR) separately from answer quality (faithfulness/groundedness, correctness). Pair with an ai-eval-plan.
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 · 78 lines · 92 tokens per session scan A 54d60db3e549
rag-design-doc is a skill published in the GitHub repository mohitagw15856/pm-claude-skills (1,449 stars, last pushed today), licensed MIT. It adds 92 tokens to every session and 1,042 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.
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