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 skills/jansenanalytics/claudex/data-analysisnpx skills add JansenAnalytics/claudex --skill data-analysisgit 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 | $0.00027 | $0.00379 |
| Opus 5 | $0.00014 | $0.00189 |
| Sonnet 5 | $0.00005 | $0.00076 |
| Haiku 4.5 | $0.00003 | $0.00038 |
Grade A, and why
data-analysis 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
Data Analysis
SQLite
# Query a database
sqlite3 DB_PATH "SELECT * FROM table LIMIT 10;"
# Schema inspection
sqlite3 DB_PATH ".tables"
sqlite3 DB_PATH ".schema table_name"
# Export to CSV
sqlite3 -header -csv DB_PATH "SELECT * FROM table;" > output.csv
Key Databases
- Trade journal: ${PROP_HEDGE_AGENTS_HOME:-$HOME/projects/prop-hedge-agents}/data/trade-journal.db
- Tables: trades, lessons, market_contexts, agent_memory
- 34+ trades with structured lessons
JSON Processing
# Pretty print
cat file.json | python3 -m json.tool
# Extract with jq (if installed) or python
cat file.json | python3 -c "import json,sys; d=json.load(sys.stdin); print(d['key'])"
CSV Processing
# Quick stats with python
python3 -c "
import csv, statistics
with open('file.csv') as f:
reader = csv.DictReader(f)
data = list(reader)
print(f'Rows: {len(data)}')
print(f'Columns: {list(data[0].keys())}')
"
Visualization (if matplotlib available)
import matplotlib.pyplot as plt
# Create charts and save to file
plt.savefig('/tmp/chart.png', dpi=150, bbox_inches='tight')
Always summarize findings in plain language, not just raw numbers.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 59 lines · 27 tokens per session scan A ff437ac42a9a
data-analysis is a skill published in the GitHub repository JansenAnalytics/claudex (5 stars, last pushed 2mo ago), licensed MIT. It adds 27 tokens to every session and 379 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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