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/audit)<a href="https://agentmods.dev/commands/jansenanalytics/claudex/audit"><img src="https://agentmods.dev/badge/commands/jansenanalytics/claudex/audit/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/audit"><img src="https://agentmods.dev/badge/commands/jansenanalytics/claudex/audit.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 | $0.00295 |
| Opus 5.5 | $0.00006 | $0.00118 |
| Sonnet 5.5 | $0.00003 | $0.00059 |
| Haiku 4.5 | $0.00002 | $0.00030 |
Grade A, and why
audit 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
Produce a single composite audit report of Claudex's own state. Three sections, no fluff.
- System health — run
bash ~/.claude-agent/scripts/status-claudex.sh --full(or equivalent) for tmux/systemd/process state, plus disk/memory/uptime. Read recent~/.claude-agent/logs/watchdog.logentries. - Memory & RAG —
node --experimental-sqlite ~/.claude-agent/scripts/memory-search.cjs --statsfor chunk counts, provider, last reindex time. Note any embedding-provider mismatch warnings. - Skill catalog — if
~/.claude-agent/scripts/skill-audit.shexists, run it. Otherwise grep~/.claude-agent/.claude/skills/*/SKILL.mdfor: count by category, count with external_deps, count by maturity, and any skill missing required frontmatter.
Output format: one Telegram-friendly message, bullets only, max 25 lines. Lead each section with a one-line verdict (✅/⚠️/❌). End with a "Action items" section listing concrete things to fix — empty list if all clean.
Don't pad. Don't congratulate. If everything's fine, the report should be short.
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 · 15 lines · 16 tokens per session scan A 0840ec45f098
audit is a command published in the GitHub repository JansenAnalytics/claudex (5 stars, last pushed 3mo ago), licensed MIT. It adds 16 tokens to every session and 295 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
minutes-ideas
Surface recent voice memos and ideas captured from any device. Use when the user asks "what ideas did I have?", "what were my recent memos?", "what did I record while walking?", or wants to recall a captured thought.
learn
Force claude-smart to extract learnings from this session now.
memory-store
Store an insight, decision, or pattern to memory.
analyze-context
USE WHEN you want to analyze project context before starting work on a task. Calls context + recall, then synthesizes goals, decisions, gotchas, and relevant memories into a pre-task brief.
cc-memory
Configure persistent memory that survives across sessions using a layered approach: split rule files for always-loaded context, auto-memory for organic learning, and optional MCP-backed long-term memory for large codebases.
lians-recall
Recall current (non-stale) facts from Lians memory, optionally as-of a past date.