Borrowing it
Nothing to install: this file belongs to TakaGoto/rag-learning-academy. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/TakaGoto/rag-learning-academy/main/.claude/skills/fix/SKILL.mdgit clone --depth 1 https://github.com/TakaGoto/rag-learning-academyWrote 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/takagoto/rag-learning-academy/fix)<a href="https://agentmods.dev/skills/takagoto/rag-learning-academy/fix"><img src="https://agentmods.dev/badge/skills/takagoto/rag-learning-academy/fix/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/takagoto/rag-learning-academy/fix"><img src="https://agentmods.dev/badge/skills/takagoto/rag-learning-academy/fix.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 | $0.00019 | $0.00640 |
| Opus 5.5 | $0.00008 | $0.00256 |
| Sonnet 5.5 | $0.00004 | $0.00128 |
| Haiku 4.5 | $0.00002 | $0.00064 |
Grade A, and why
fix 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.
How it starts
The opening of the file, as written. The whole thing — 73 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Fix: Fast-Track RAG Diagnosis
Scope: This skill diagnoses and fixes an existing pipeline with no teaching. For guided debugging that explains common failure modes, use
/debug-rag.
For learners who already have a RAG pipeline and just need help fixing it. No curriculum, no lessons — straight to diagnosis and solutions.
Language awareness: See
.claude/LANGUAGE_AWARENESS.md.
Step 1: Get the Symptom
Ask one question: "What's going wrong?"
Common symptoms and what to probe for:
| Symptom | Follow-up |
|---|---|
| "Bad retrieval results" | Ask for a sample query + what it returns vs what it should return |
| "Hallucinating" | Ask if context is being passed, and what the prompt looks like |
| "Slow" | Ask about data size, embedding model, vector DB, and whether they're batching |
| "No results" | Ask if data is indexed, and check the collection exists |
| "Works sometimes" | Ask for a working query and a failing query — compare them |
Step 2: Read Their Code
Ask the learner to point you to their pipeline code. Read the files in src/ or wherever they indicate. Look at:
- Chunking — strategy, size, overlap
- Embedding — model, dimensions, normalization
- Storage — vector DB config, distance metric
- Retrieval — top-k, filters, search type
- Generation — prompt template, context injection, grounding instructions
Step 3: Diagnose
Identify the likely root cause. Present it clearly:
Diagnosis: [one-line summary]
Root cause: [what's actually happening]
Evidence: [what in their code/output points to this]
If you're not certain, rank the top 2-3 most likely causes and explain how to verify each.
Step 4: Fix It
Provide the specific code change needed. Show a before/after diff if possible. Explain why the fix works in 1-2 sentences — enough to understand, not a lecture.
If the fix requires multiple changes, prioritize: "Fix this first, then we'll check if the other issues resolve."
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 · 73 lines · 19 tokens per session scan A e0df507e58bf
fix is a skill published in the GitHub repository TakaGoto/rag-learning-academy (24 stars, last pushed 6mo ago), licensed MIT. It adds 19 tokens to every session and 640 once invoked, about $0.0001 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 2026-08-30.
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