Borrowing it
Nothing to install: this file belongs to Kemetra/Seshat-BI. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Kemetra/Seshat-BI/main/.claude/skills/grain-confidence-reviewer/SKILL.mdgit 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/skills/kemetra/seshat-bi/grain-confidence-reviewer)<a href="https://agentmods.dev/skills/kemetra/seshat-bi/grain-confidence-reviewer"><img src="https://agentmods.dev/badge/skills/kemetra/seshat-bi/grain-confidence-reviewer/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/skills/kemetra/seshat-bi/grain-confidence-reviewer"><img src="https://agentmods.dev/badge/skills/kemetra/seshat-bi/grain-confidence-reviewer.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 | $0.00175 | $0.02572 |
| Opus 5 | $0.00088 | $0.01286 |
| Sonnet 5 | $0.00035 | $0.00514 |
| Haiku 4.5 | $0.00017 | $0.00257 |
Grade A, and why
grain-confidence-reviewer 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 12d 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.
How it starts
The opening of the file, as written. The whole thing — 179 lines — stays where its author put it; the contents beside it link to each section on GitHub.
grain-confidence-reviewer
The Mapping Ready gate turns on one load-bearing fact: is the declared grain
actually unique on the data? That fact is already measured (profile.py's
PkProof) and the diff inputs are two committed source-map.yaml versions. This skill
SURFACES both for a reviewer -- a grain-confidence card and a semantic mapping diff --
and STOPS at the human seam. It deepens the shipped mapping gate (roadmap Layer 3); it
adds no new gate, no new Python, no numeric score.
Scope boundary (read first)
- Surfaces and STOPS. It RENDERS evidence (a card, a diff) for a human reviewer; it
does NOT write an approval, does NOT write
Gate status: CLEARED, does NOT editsource-map.yamlto clear a finding, and does NOT pick a new candidate PK or widen a grain on the human's behalf. - "Confidence" is evidence + status + blockers, NEVER a number. No
0.87, no auto high/medium/low label. Grain confidence is reported as exactly ONE of the four readiness statuses (not_started|blocked|warning|pass) with the measured signal cited as evidence and any blockers attached (roadmap rule #9; "No fake confidence", readiness-model.md). - Reuses the measured signal; re-implements nothing. It reads
PkProof(total,distinct_pk,null_pk,is_unique); it never writes its own uniqueness query. - Judgment calls are the human's (Principle V): grain ambiguity, PII publish-safety, business rollups -- surfaced, never auto-resolved.
- Cite Principles IV (mapping gate), V (human seam), VII (generic), VIII (live deferred).
- ASCII only, UTF-8 no BOM.
US1 -- Grain confidence card
Read the measured signal
Read the Candidate-grain/PK numbers from mappings/<table>/source-profile.md; OR, at
the deferred live boundary, re-run src/seshat/profile.py over a READ-ONLY connection
(resolve_dsn + make_psycopg2_runner, the db extra). Reuse PkProof
(total, distinct_pk, null_pk, is_unique) -- do NOT re-implement the uniqueness
query.
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.
- 12d ago First seen · 179 lines · 175 tokens per session scan A 78982a3aaa0c
grain-confidence-reviewer is a skill published in the GitHub repository Kemetra/Seshat-BI (2 stars, last pushed 3d ago), licensed Apache-2.0. It adds 175 tokens to every session and 2,572 once invoked, about $0.0009 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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