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/powerbi-review)<a href="https://agentmods.dev/commands/kemetra/seshat-bi/powerbi-review"><img src="https://agentmods.dev/badge/commands/kemetra/seshat-bi/powerbi-review/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/powerbi-review"><img src="https://agentmods.dev/badge/commands/kemetra/seshat-bi/powerbi-review.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 | $0.00014 | $0.00206 |
| Opus 5 | $0.00007 | $0.00103 |
| Sonnet 5 | $0.00003 | $0.00041 |
| Haiku 4.5 | $0.00001 | $0.00021 |
Grade A, and why
powerbi-review 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 8d 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
Load the powerbi-workflows skill and follow its review route. Route a
screenshot or built-report review to its visual QA guidance, a page-blueprint
check to the installed seshat pbir-validate-blueprint helper when available,
a pre-Desktop field-resolution check to seshat pbir-validate-bindings,
hand-authored TMDL to seshat tmdl-doc-comment-lint (which checks ONE rule --
a /// block must attach to a declaration -- and is NOT a TMDL syntax
validator, so a pass does not mean the model loads), and a semantic or metric
question to bi-dax-knowledge / retail-kpi-knowledge. Findings are advisory
evidence for a named human; a clean review never grants approval or a readiness
pass, and neither helper verifies that Desktop can load the model.
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.
- 8d ago First seen · 15 lines · 14 tokens per session scan A 90b2b2fc23d9
powerbi-review is a command published in the GitHub repository Kemetra/Seshat-BI (2 stars, last pushed yesterday), licensed Apache-2.0. It adds 14 tokens to every session and 206 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 2026-09-03.
Other commands, from other repositories
gsd:code-review
Review source files changed during a phase for bugs, security issues, and code quality problems.
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.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.