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/latestaiagents/agent-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/commands/latestaiagents/agent-skills/rag-debug)<a href="https://agentmods.dev/commands/latestaiagents/agent-skills/rag-debug"><img src="https://agentmods.dev/badge/commands/latestaiagents/agent-skills/rag-debug/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/commands/latestaiagents/agent-skills/rag-debug"><img src="https://agentmods.dev/badge/commands/latestaiagents/agent-skills/rag-debug.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 | $0.00011 | $0.00407 |
| Opus 5.5 | $0.00004 | $0.00163 |
| Sonnet 5.5 | $0.00002 | $0.00081 |
| Haiku 4.5 | $0.00001 | $0.00041 |
Grade A, and why
rag-debug 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 1mo 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.
What it actually says
/rag-debug
Diagnose and fix RAG pipeline problems with systematic analysis.
What I Need
Tell me:
- What query is producing poor results?
- What answer did you expect vs what you got?
- Which vector store are you using?
How It Works
Step 1: Query Analysis
I'll analyze your query to identify potential issues:
- Query clarity and specificity
- Potential keyword/semantic mismatch
- Query complexity (single vs multi-hop)
Step 2: Retrieval Diagnosis
I'll check retrieval quality:
- Are relevant documents in your index?
- What's the similarity score distribution?
- Are chunks properly sized?
Step 3: Generation Analysis
I'll examine the generation step:
- Is context being used correctly?
- Are there hallucinations?
- Is the prompt template effective?
Step 4: Fix Recommendations
Based on findings, I'll recommend:
- Chunking adjustments
- Retrieval strategy changes
- Prompt improvements
- Hybrid search additions
Debug Checklist
[ ] Query understood correctly?
[ ] Documents indexed?
[ ] Chunks contain answer?
[ ] Top-k retrieving relevant docs?
[ ] Context passed to LLM?
[ ] LLM using context?
[ ] Answer grounded?
Common Issues & Fixes
| Symptom | Likely Cause | Fix |
|---|---|---|
| Wrong docs retrieved | Semantic gap | Add hybrid search |
| Right docs, wrong answer | Poor prompt | Improve system prompt |
| Partial answer | Chunk boundary | Adjust overlap |
| Hallucination | Context ignored | Add grounding check |
| No answer | Missing docs | Check indexing |
Quick Commands
# Test retrieval only
retriever.invoke("your query")
# Check similarity scores
vectorstore.similarity_search_with_score("query", k=10)
# Inspect chunk content
for doc in results: print(doc.page_content[:200])
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.
- 1mo ago First seen · 81 lines · 11 tokens per session scan A 236cc2c74337
rag-debug is a command published in the GitHub repository latestaiagents/agent-skills (5 stars, last pushed 6mo ago), licensed MIT. It adds 11 tokens to every session and 407 once invoked, about $0.0000 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 2026-09-03.
Other commands, from other repositories
fdk-fix
Fix all platform validation errors and lint errors in a Freshworks app. Runs fdk validate, identifies fatal errors and lint issues, applies fixes iteratively, and re-validates until all errors are resolved.
optimizer
Performance optimization with systematic analysis and improvements.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.