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 commands/bankstatemently/plugins/analyzegit clone --depth 1 https://github.com/bankstatemently/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 | $0.00046 | $0.00300 |
| Opus 5 | $0.00023 | $0.00150 |
| Sonnet 5 | $0.00009 | $0.00060 |
| Haiku 4.5 | $0.00005 | $0.00030 |
Grade A, and why
analyze 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
Input: $ARGUMENTS
Analyze
Input: the user's analysis scope — account, product, content hash, date range, or free-text filter. Output: category totals, merchant or counterparty totals, top merchants, and a monthly trend, each row citing content_hash.
Treat the input as the user's analysis scope: account, product, content hash, date range, or free-text filter.
Follow this sequence:
- If the scoped statement has not been categorized, call
categorize_statement. - Call
group_byfor category totals within the scope. - Call
group_byfor merchant or counterparty totals within the scope. - Call
top_nto rank the largest merchants or counterparties within the scope. - Call
time_serieswith monthly buckets for trend figures within the scope. - Present category totals, merchant or counterparty totals, top merchants, and the monthly trend. Cite content_hash for every figure or table row you report.
Example prompt: Categorize my checking account spending last quarter and show top merchants plus the monthly trend.
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 · 28 lines · 46 tokens per session scan A f81609f5b5a2
analyze is a command published in the GitHub repository bankstatemently/plugins (1 stars, last pushed 2d ago), licensed MIT. It adds 46 tokens to every session and 300 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.
Other commands, from other repositories
make
Create structured implementation plan in docs/plans/.
speckit.checklist
Generate a custom checklist for the current feature based on user requirements.
speckit.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.
speckit.specify
Create or update the feature specification from a natural language feature description.
speckit.analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
speckit.implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.