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/JansenAnalytics/claudexWrote 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/jansenanalytics/claudex/rag)<a href="https://agentmods.dev/commands/jansenanalytics/claudex/rag"><img src="https://agentmods.dev/badge/commands/jansenanalytics/claudex/rag/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/jansenanalytics/claudex/rag"><img src="https://agentmods.dev/badge/commands/jansenanalytics/claudex/rag.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 | $0.00014 | $0.00355 |
| Opus 5.5 | $0.00006 | $0.00142 |
| Sonnet 5.5 | $0.00003 | $0.00071 |
| Haiku 4.5 | $0.00001 | $0.00036 |
Grade A, and why
rag scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Semantic search across Claudex's memory store, session transcripts, and cross-agent context. Requires OPENAI_API_KEY (used for embeddings).
- Run
node --experimental-sqlite ~/.claude-agent/scripts/memory-search.cjs --search "$ARGUMENTS" --limit 10. If it errors on a missing OPENAI_API_KEY, say so plainly — that's the fix, not a bug. - If zero results, retry once without any
--source/--agentfilters. Still nothing → suggestnode --experimental-sqlite ~/.claude-agent/scripts/memory-search.cjs --index --incrementalto refresh the index, then re-run. - Present the top hits, Telegram-friendly, one bullet each:
[memory|session|cross-agent]source tag + score (e.g.0.82)- 1-2 line snippet of the matched content
- file/line ref in
code(e.g.memory/2026-05-12.md:14)
- End with: "Drill into a hit?" — offer to open any one and read it in full.
Filter options to mention if useful (don't run unprompted): --source memory|session|cross-agent to scope, --agent kite|argus for a specific sister agent's context.
Output: bullets only, no tables, no preamble. Lead with a one-line verdict (✅ N hits / ⚠️ weak matches / ❌ nothing).
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 · 20 lines · 14 tokens per session scan A 9fa4f2688c85
rag is a command published in the GitHub repository JansenAnalytics/claudex (5 stars, last pushed 3mo ago), licensed MIT. It adds 14 tokens to every session and 355 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 2026-08-31.
Other commands, from other repositories
build-corpus
Use when the user invokes /goal-flight build-corpus to build or refresh the private Goal Flight dispatch-context corpus.
retrieval
Search the user's Ryu memory and knowledge Spaces (RAG).
context
Cost-routing + semantic-recall management. Sync the workspace into Pinecone, query it, or inspect the routing decision for a given task.
index
Index files or directories into the RAG memory system using Memvid.
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.