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/Kemetra/Seshat-BIWrote 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/kemetra/seshat-bi/status)<a href="https://agentmods.dev/commands/kemetra/seshat-bi/status"><img src="https://agentmods.dev/badge/commands/kemetra/seshat-bi/status/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/kemetra/seshat-bi/status"><img src="https://agentmods.dev/badge/commands/kemetra/seshat-bi/status.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 | $0.00007 | $0.00109 |
| Opus 5.5 | $0.00003 | $0.00044 |
| Sonnet 5.5 | $0.00001 | $0.00022 |
| Haiku 4.5 | $0.00001 | $0.00011 |
Grade A, and why
status 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
Run the installed seshat status helper if available (use --format json when
summarizing programmatically) and report each table's recorded stage exactly as
returned. Never invent a stage, upgrade a warning, or emit a numeric
readiness/confidence score; an empty table list is a truthful answer, not an
error. If seshat is unavailable, explain that the Python package seshat-bi
must be installed.
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 · 11 lines · 7 tokens per session scan A 75a120bf573a
status is a command published in the GitHub repository Kemetra/Seshat-BI (2 stars, last pushed 6d ago), licensed Apache-2.0. It adds 7 tokens to every session and 109 once invoked, about $0.0000 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 2026-09-03.
Other commands, from other repositories
publish
Upload a finished migration into an AIDP workspace (dry run by default).
update
Refresh data and findings for existing research using swarm orchestration.
customer-persona-builder
Jobs-to-be-Done (JTBD) customer persona development with priority matrix, buying journey mapping, and decision criteria analysis. Creates actionable buyer personas for product and marketing decisions.
checklist
Generate a custom checklist for the current feature based on user requirements.
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.
specify
Create or update the feature specification from a natural language feature description.