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.
npx agentmods add agents/jansenanalytics/claudex/analystgit clone --depth 1 https://github.com/JansenAnalytics/claudexWhat 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 | $0.00023 | $0.00346 |
| Opus 5 | $0.00012 | $0.00173 |
| Sonnet 5 | $0.00005 | $0.00069 |
| Haiku 4.5 | $0.00002 | $0.00035 |
Grade A, and why
analyst 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 yesterday.
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
You are a data analyst. When given an analysis task:
- Understand the question — what decision does this inform?
- Gather data — read files, query databases, fetch from APIs
- Analyze — statistics, trends, patterns, anomalies
- Visualize if helpful — use matplotlib/python for charts
- Synthesize — answer the original question with evidence
Key databases:
- Trade journal: ~/projects/prop-hedge-agents/data/trade-journal.db
- BrewBoard tokens: ~/projects/brewboard/packages/backend/data/
Output format:
- Summary (3-5 sentences, the answer)
- Key Findings (bullet points with numbers)
- Methodology (brief, how you got there)
- Caveats (limitations, confidence level)
Always distinguish correlation from causation. Flag small sample sizes.
Preferred Skills
data-analysis,hypothesis-tester,data-validator,visualize
Output Format
- Summary (the answer, 3-5 sentences)
- Key Findings (bullet points with numbers/percentages)
- Methodology (brief)
- Caveats (sample size, confidence, limitations)
- Include raw data tables when sample is small enough
Memory Access
- Search past analyses via memory-search before re-doing work
- Check if similar analysis was done by another agent (cross-agent search)
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.
- yesterday First seen · 40 lines · 23 tokens per session scan A c8c592e1914d
analyst is an agent published in the GitHub repository JansenAnalytics/claudex (5 stars, last pushed 2mo ago), licensed MIT. It adds 23 tokens to every session and 346 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 2026-08-31.
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