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/audit-content/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/audit-content)<a href="https://agentmods.dev/skills/takagoto/rag-learning-academy/audit-content"><img src="https://agentmods.dev/badge/skills/takagoto/rag-learning-academy/audit-content/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/audit-content"><img src="https://agentmods.dev/badge/skills/takagoto/rag-learning-academy/audit-content.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 | $0.00016 | $0.00992 |
| Opus 5 | $0.00008 | $0.00496 |
| Sonnet 5 | $0.00003 | $0.00198 |
| Haiku 4.5 | $0.00002 | $0.00099 |
Grade A, and why
audit-content 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 10d 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 — 116 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Audit Content: Keep the Academy Current
Purpose: Systematically check all academy materials for outdated information — deprecated models, changed APIs, superseded techniques, and stale references. Produces an actionable update report.
Step 1: Determine Audit Scope
If the user specifies a scope (e.g., /audit-content embeddings or /audit-content module 3), audit only that area. Otherwise, run a full audit across all content.
Full audit covers:
- Curriculum modules (
.claude/docs/curriculum/) - Agent definitions (
.claude/agents/) - Reference docs (
.claude/docs/reference/) - Code rules (
.claude/rules/) - Sample data (
data/raw/) - Templates (
.claude/docs/templates/)
Step 2: Check Model References
Scan all content files for embedding and LLM model references. Verify each against current state:
| Model Reference | What to Check |
|---|---|
text-embedding-3-small |
Still recommended? Check OpenAI docs and MTEB leaderboard |
all-MiniLM-L6-v2 |
Still a good free default? Check Sentence Transformers releases |
nomic-embed-text |
Still maintained and competitive? |
bge-base-en-v1.5 |
Check if newer BGE versions exist |
| Claude model IDs | Verify against current Anthropic model IDs |
| GPT model IDs | Verify against current OpenAI model IDs |
Use WebSearch to check the MTEB leaderboard and model provider documentation for current recommendations.
Step 3: Check Library and Framework References
Scan for library references and verify versions and API patterns:
| Library | What to Check |
|---|---|
langchain |
Import patterns — should use langchain-core, langchain-community, or langgraph |
chromadb |
API compatibility — check for breaking changes |
ragas |
Current version and metric names |
sentence-transformers |
Current version and API |
llama-index |
Package rename or API changes |
pydantic |
v1 vs v2 patterns |
Use WebSearch to check changelogs and migration guides.
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.
- 10d ago First seen · 116 lines · 16 tokens per session scan A 8031f1e7c763
audit-content is a skill published in the GitHub repository TakaGoto/rag-learning-academy (18 stars, last pushed 5mo ago), licensed MIT. It adds 16 tokens to every session and 992 once invoked, about $0.0001 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 2026-08-30.
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