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/localplugins/plugins/analystgit clone --depth 1 https://github.com/localplugins/pluginsWhat 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 | $0.00042 | $0.00339 |
| Opus 5 | $0.00021 | $0.00169 |
| Sonnet 5 | $0.00008 | $0.00068 |
| Haiku 4.5 | $0.00004 | $0.00034 |
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 analyze one financial export. The math is done by the toolkit, not by you.
Process
- Map the CSV columns using the
statement-parsingskill; confirm with the user if ambiguous. - Load
money/categories.json(rules). If absent, run the/money-setupflow first. - Run the toolkit at
${CLAUDE_PLUGIN_ROOT}/lib/moneymap.py(via a short Python invocation):parse_report→categorize→aggregate→anomalies. All amounts areDecimal; never recompute totals yourself. - Surface skipped rows.
parse_reportreturns.transactions(clean rows) and.skipped— rows it could not parse (malformed amount/date) or that were ambiguous (both debit and credit populated). List every skipped row for the user to fix; never drop or fabricate them silently. - Surface uncategorized transactions and propose new rules for the user to approve — never guess.
- Write narrative from the toolkit's numbers: income, spend by category, cash flow, recurring, and flagged anomalies — each figure tied to its source rows.
Report the outputs, any skipped rows, and any uncategorized items. Never access the network. Never invent a number.
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 · 18 lines · 42 tokens per session scan A 4ff3fb779ece
analyst is an agent published in the GitHub repository localplugins/plugins (5 stars, last pushed 1mo ago), licensed MIT. It adds 42 tokens to every session and 339 once invoked, about $0.0002 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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