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/retail-scaffold/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/retail-scaffold)<a href="https://agentmods.dev/skills/kemetra/seshat-bi/retail-scaffold"><img src="https://agentmods.dev/badge/skills/kemetra/seshat-bi/retail-scaffold/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/retail-scaffold"><img src="https://agentmods.dev/badge/skills/kemetra/seshat-bi/retail-scaffold.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 | $0.00052 | $0.01126 |
| Opus 5 | $0.00026 | $0.00563 |
| Sonnet 5 | $0.00010 | $0.00225 |
| Haiku 4.5 | $0.00005 | $0.00113 |
Grade A, and why
retail-scaffold 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 10d 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 — 92 lines — stays where its author put it; the contents beside it link to each section on GitHub.
retail-scaffold
The authoring front door for a NEW seshat check rule. Adding a rule means wiring the
same five places every time; doing it by hand silently under-governs a rule when a
place is missed (a shipped rule once had no glossary row). This skill runs the
retail scaffold CLI verb, which automates the mechanical part with a strict
write/print split, and interprets its output.
Boundary vs retail-govern (read first)
Both wrap a governance CLI verb; they do opposite jobs:
retail-govern-- INTERPRET existingseshat checkfindings: map a reported rule id (D8,C2,S2,G1, …) to its meaning and fix. It reasons about rules that already exist.retail-scaffold(this skill) -- AUTHOR a NEW rule + verify its wiring. It creates a rule that does not yet exist, or doctors an existing rule's five-place wiring for drift.
Use retail-govern when a check FAILED and you need the fix; use retail-scaffold
when you are ADDING a rule or checking whether one is fully wired.
Scope + non-negotiables
- Invoke-and-interpret only. This skill runs
retail scaffoldand reads its output; it does not hand-edit the five places itself. - The CLI (not this skill) is the source of the five wiring places. They are
declared in
src/seshat/scaffold.py(FIVE_PLACES); this skill points at that authority and never re-enumerates a competing list (anti-fork). - Never invents rule intent (DEC-1). The author supplies the id, the title, and the real check logic. The generated stub yields no findings until the author fills it in; its test stub fails on purpose (honest red).
- Never self-grants a wiring pass (Principle I). Whether a rule is correctly wired is disposed of by the test suite + the gate exit code, not by this skill or the helper.
- Prose + golden records are PRINT-only (Principle V). The helper writes exactly
three targets and PRINTS the rest (golden-regen commands, a suggested glossary row,
the import/
__all__edit) for a human to apply by hand. It never writes the glossary or a golden record.
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.
- 10d ago First seen · 92 lines · 52 tokens per session scan A 5969c1379f5e
retail-scaffold is a skill published in the GitHub repository Kemetra/Seshat-BI (2 stars, last pushed yesterday), licensed Apache-2.0. It adds 52 tokens to every session and 1,126 once invoked, about $0.0003 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 skills, from other repositories
ktx-analytics
Use when answering a question that needs data from a ktx-connected database - investigating, analyzing, "how many", "show me", "what's the breakdown of", finding records by value, exploring tables, comparing periods, explaining metrics, or any data-analysis request. Triggers even when the user does not say…
metabase_ingest
Convert Metabase questions, models, and metrics into ktx Semantic Layer source definitions. Covers result-metadata to KSL column type mapping, FK/PK detection, near-duplicate deduplication, pre-aggregation decomposition, join-graph connectivity, and how to react to priorProvenance from earlier ingest syncs. Load when…
sl_capture
How to capture new reusable patterns into ktx's semantic layer - when a measure, segment, or join belongs in the catalog and how to write it generically so it stays small and useful over time. Loaded by the post-turn memory-agent only. The research agent does not write to the SL.
looker_ingest
Extract durable ktx knowledge and semantic-layer contribution proposals from staged Looker runtime dashboard, Look, and explore JSON. Load for WorkUnits whose raw files are under explores/, dashboards/, or looks/.
metricflow_ingest
Map a MetricFlow semanticmodel or metric into ktx semantic layer sources. Covers the MetricFlow to ktx primitive table, extends: inheritance flattening, metric-type handling (simple / derived / ratio / cumulative / conversion), model: ref('x') resolution, and four worked examples. Load when the turn contains…
lookml_ingest
Map a LookML view/model/explore into ktx semantic layer sources. Covers the LookML to ktx primitive table, provenance tagging, and three worked examples (overlay, standalone from derivedtable, standalone with sqlalwayswhere). Load when the turn contains .lkml content.