Unvibecode RAG Review
FinanceFlash/unvibecode
Review RAG designs or accessible implementations for extraction failures, deterministic facts, evidence routing, source versioning, code retrieval, and memory boundaries.
Review an existing Retrieval-Augmented Generation system and find why it underperforms.
Install RAG Architecture Review
$ npx skills add mohitagw15856/pm-claude-skills --skill rag-architecture-review -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install mohitagw15856/pm-claude-skills rag-architecture-review --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/mohitagw15856/pm-claude-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/rag-architecture-review .claude/skills/rag-architecture-review && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "rag-architecture-review" agent skill from https://github.com/mohitagw15856/pm-claude-skills/tree/main/skills/rag-architecture-review into .claude/skills/rag-architecture-review/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "rag-architecture-review", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/mohitagw15856/pm-claude-skills/tree/main/skills/rag-architecture-reviewType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add mohitagw15856/pm-claude-skills --skill rag-architecture-review -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install mohitagw15856/pm-claude-skills rag-architecture-review --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mohitagw15856/pm-claude-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/rag-architecture-review .agents/skills/rag-architecture-review && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "rag-architecture-review" agent skill from https://github.com/mohitagw15856/pm-claude-skills/tree/main/skills/rag-architecture-review into .agents/skills/rag-architecture-review/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "rag-architecture-review", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add mohitagw15856/pm-claude-skills --skill rag-architecture-review -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install mohitagw15856/pm-claude-skills rag-architecture-review --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mohitagw15856/pm-claude-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/rag-architecture-review .cursor/skills/rag-architecture-review && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "rag-architecture-review" agent skill from https://github.com/mohitagw15856/pm-claude-skills/tree/main/skills/rag-architecture-review into .cursor/skills/rag-architecture-review/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "rag-architecture-review", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/mohitagw15856/pm-claude-skills.git --path skills/rag-architecture-review--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add mohitagw15856/pm-claude-skills --skill rag-architecture-review -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install mohitagw15856/pm-claude-skills rag-architecture-review --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mohitagw15856/pm-claude-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/rag-architecture-review .gemini/skills/rag-architecture-review && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "rag-architecture-review" agent skill from https://github.com/mohitagw15856/pm-claude-skills/tree/main/skills/rag-architecture-review into .gemini/skills/rag-architecture-review/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "rag-architecture-review", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install mohitagw15856/pm-claude-skills rag-architecture-reviewInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add mohitagw15856/pm-claude-skills --skill rag-architecture-review -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/mohitagw15856/pm-claude-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/rag-architecture-review .github/skills/rag-architecture-review && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "rag-architecture-review" agent skill from https://github.com/mohitagw15856/pm-claude-skills/tree/main/skills/rag-architecture-review into .github/skills/rag-architecture-review/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "rag-architecture-review", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add mohitagw15856/pm-claude-skills --skill rag-architecture-review -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install mohitagw15856/pm-claude-skills rag-architecture-review --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mohitagw15856/pm-claude-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/rag-architecture-review .opencode/skills/rag-architecture-review && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "rag-architecture-review" agent skill from https://github.com/mohitagw15856/pm-claude-skills/tree/main/skills/rag-architecture-review into .opencode/skills/rag-architecture-review/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "rag-architecture-review", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
rag-architecture-review- GitHub stars
- 1.4k
- Token cost
- ~1.1k tokens
- SKILL.md length
- 554 words
- Files
- 1
- Skills in repo
- 1,348
- Repo updated
- First seen
- Licence
- MIT
At a glance
Review an existing Retrieval-Augmented Generation system and find why it underperforms.
- Asked to review
- SKILL.md covers Working from a brief, Required Inputs, Output Format and Quality Checks, plus 3 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Audit a RAG pipeline
What it does
RAG Architecture Review is an agent skill from mohitagw15856/pm-claude-skills. Review an existing Retrieval-Augmented Generation system and find why it underperforms. Use when asked to review or audit a RAG pipeline, diagnose wrong/ungrounded answers from a 'chat with your docs' feature, or improve an already-built knowledge assistant. Produces a staged review — ingestion, chunking, retrieval, reranking, generation, evaluation — with prioritised findings, root causes, and concrete fixes.
Its SKILL.md is about 1.1k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in AI & LLM Engineering, covering Retrieval-augmented generation and Software architecture. The repository describes itself as: 1255 professional Agent Skills for Claude, ChatGPT, Gemini, Cursor & Codex — PRDs, postmortems, leases, medical bills, layoffs, go-bags, new countries. Plain markdown, MIT, in… The licence is MIT.
When your agent uses it
- Asked to review
- Audit a RAG pipeline
- Diagnose wrong/ungrounded answers from a chat with your docs feature
- Improve an already-built knowledge assistant
Example prompts
- “chat with your docs”
- “/rag-architecture-review”
What it can do on your machine
Read from SKILL.md and the folder at commit 1cbf1f0. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
RAG Architecture Review loads about 1.1k tokens when it runs. Until then it costs about 109 tokens; SKILL.md has 554 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from mohitagw15856/pm-claude-skills at commit 1cbf1f0, republished under its MIT licence (© mohitagw15856). 554 words, ~1,119 tokens.
Download SKILL.mdSave it as
.claude/skills/rag-architecture-review/SKILL.md (or your agent's skills folder).- name
- rag-architecture-review
- description
- Review an existing Retrieval-Augmented Generation system and find why it underperforms. Use when asked to review or audit a RAG pipeline, diagnose wrong/ungrounded answers from a 'chat with your docs' feature, or improve an already-built knowledge assistant. Produces a staged review — ingestion, chunking, retrieval, reranking, generation, evaluation — with prioritised findings, root causes, and concrete fixes.
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" |
3. Diagnosis: symptom → stage — map each reported bad answer to the stage that caused it, so fixes target the real cause (a confident-but-wrong answer is usually retrieval, not the LLM).
4. Prioritised fix plan — ordered by impact-to-effort, with the one change likely to move quality most first.
5. Evaluation gap — whether retrieval quality (recall@k, MRR) is measured separately from answer quality
(faithfulness, correctness); if not, that's finding #1 — you can't fix what you can't isolate. Pair with an
ai-eval-plan.
Show full SKILL.md (202 more words)Show less
Quality Checks
- Every reported symptom is traced to a specific stage, not blamed on "the model"
- Retrieval quality and answer quality are evaluated separately (or that gap is finding #1)
- Findings are severity-ranked and the fix plan is ordered by impact, not by stage order
- Hybrid retrieval and reranking are assessed for queries with exact terms/IDs
- Grounding instruction and "I don't know" behaviour are checked in the generation stage
- Per-tenant / permission isolation is verified in retrieval, not just the UI
Anti-Patterns
- Do not recommend fine-tuning the model when the failure is in retrieval — fix what's retrieved first
- Do not review only the generation prompt — most RAG quality is won or lost before the LLM sees anything
- Do not present findings without severity and priority — a flat list doesn't tell the team what to do Monday
- Do not assume the corpus is fine — stale or badly-structured source data caps every downstream stage
- Do not skip the eval gap — without separated metrics, every fix is a guess
Based On
Retrieval-Augmented Generation practice — staged diagnosis, separated retrieval/answer evaluation, hybrid retrieval, and grounded generation.
Example Trigger Phrases
- "Audit a RAG pipeline."
- "Diagnose wrong/ungrounded answers from a 'chat with your docs' feature."
- "Improve an already-built knowledge assistant."
© mohitagw15856, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in skills/rag-architecture-review of mohitagw15856/pm-claude-skills.
Open the folder on GitHubat commit 1cbf1f0
Compare with similar skills
RAG Architecture Review next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| RAG Architecture Review this skillmohitagw15856/pm-claude-skills | 1.4k | — | ~1.1k | Automated safety check: Pass | MIT | |
| Unvibecode RAG ReviewFinanceFlash/unvibecode | 228 | — | ~766 | Automated safety check: Pass | Apache-2.0 | |
| Chroma Vector DatabaseOrchestra-Research/AI-Research-SKILLs | 13k | 7 repos | ~2.3k | Automated safety check: Pass | MIT | |
| Senior Prompt Engineermaslennikov-ig/claude-code-orchestrator-kit | 260 | 3 repos | ~1.4k | Automated safety check: Pass | Custom licence | |
| MCP Local RAGshinpr/mcp-local-rag | 412 | — | ~4.4k | Automated safety check: Pass | MIT | |
| Ms Agent Framework RAGshuyu-labs/WebCode | 278 | — | ~1.1k | Automated safety check: Pass | Custom licence |
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maslennikov-ig/claude-code-orchestrator-kit
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Related
Categories
Questions about RAG Architecture Review
What does RAG Architecture Review do?
Review an existing Retrieval-Augmented Generation system and find why it underperforms. RAG Architecture Review is an agent skill from mohitagw15856/pm-claude-skills. Review an existing Retrieval-Augmented Generation system and find why it underperforms.
When should I use RAG Architecture Review?
RAG Architecture Review fits situations like: asked to review; audit a RAG pipeline; diagnose wrong/ungrounded answers from a chat with your docs feature; improve an already-built knowledge assistant.
How do I install RAG Architecture Review in Claude Code?
Run `npx skills add mohitagw15856/pm-claude-skills --skill rag-architecture-review -a claude-code`. Or copy the skill folder (skills/rag-architecture-review in mohitagw15856/pm-claude-skills) into .claude/skills/rag-architecture-review in your project. Claude Code loads it when a task matches its description.
How do I install RAG Architecture Review in Codex?
Run `npx skills add mohitagw15856/pm-claude-skills --skill rag-architecture-review -a codex`. Or copy the skill folder (skills/rag-architecture-review in mohitagw15856/pm-claude-skills) into .agents/skills/rag-architecture-review in your project. Codex loads it when a task matches its description.
Can I use RAG Architecture Review in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add mohitagw15856/pm-claude-skills --skill rag-architecture-review -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/rag-architecture-review, .gemini/skills/rag-architecture-review, .github/skills/rag-architecture-review and .opencode/skills/rag-architecture-review in your project.
What does RAG Architecture Review need to run?
SKILL.md names no scripts, command-line tools or credentials: RAG Architecture Review is instructions for the agent only.
Does RAG Architecture Review access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is RAG Architecture Review safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does RAG Architecture Review use?
RAG Architecture Review is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does RAG Architecture Review use?
About 1.1k tokens (SKILL.md is roughly 4.5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to RAG Architecture Review?
Skills that share tags, products or a category with RAG Architecture Review: Unvibecode RAG Review (FinanceFlash/unvibecode, 228 stars), Chroma Vector Database (Orchestra-Research/AI-Research-SKILLs, 13k stars), Senior Prompt Engineer (maslennikov-ig/claude-code-orchestrator-kit, 260 stars) and MCP Local RAG (shinpr/mcp-local-rag, 412 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains RAG Architecture Review?
mohitagw15856 (a GitHub user) maintains it in mohitagw15856/pm-claude-skills, which has 1,434 GitHub stars. The repository holds 1,348 skills in this directory. The repository was last updated on October 9, 2026.
Source: mohitagw15856/pm-claude-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.