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/business-knowledge-interview/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/business-knowledge-interview)<a href="https://agentmods.dev/skills/kemetra/seshat-bi/business-knowledge-interview"><img src="https://agentmods.dev/badge/skills/kemetra/seshat-bi/business-knowledge-interview/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/business-knowledge-interview"><img src="https://agentmods.dev/badge/skills/kemetra/seshat-bi/business-knowledge-interview.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 | $0.00170 | $0.01311 |
| Opus 5 | $0.00085 | $0.00656 |
| Sonnet 5 | $0.00034 | $0.00262 |
| Haiku 4.5 | $0.00017 | $0.00131 |
Grade A, and why
business-knowledge-interview 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 9d 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 — 102 lines — stays where its author put it; the contents beside it link to each section on GitHub.
business-knowledge-interview
The interview is the front door of the Database-to-PBIP flow's decision layer (spec 121). It converts a discovery profile into recorded, approvable business decisions so that every downstream stage consumes governed answers instead of unrecorded assumptions.
Behavior is governed by the business-knowledge-interview contract and its
recorded interview protocol. The
static rules DS1-DS5 (seshat check) verify the recorded outcome, not the
conversation.
When to use
- After
retail-onboard-tablehas produced a committed discovery profile (or an equivalent committed profile exists). - Before KPI contracts, Silver/Gold planning, DAX, dashboard blueprint, or PBIP readiness -- those stages are blocked until their critical decisions are approved.
Preconditions (stop if unmet)
- A committed discovery profile exists. No profile => do not start; report the gate as blocked naming the missing profile.
- Load the existing Decision Store first. Existing decisions are presented for confirmation or supersession -- never overwritten.
How it runs
- Ground every question in the profile (profile summaries, candidate grains, column types). Do not walk every column one-by-one unless the owner asks or an ambiguity requires it. Concentrate on: KPI inputs, PII, table grain, keys, relationships, missing-value rules, and ambiguous financial/quantity/date columns.
- Batch the obvious low-risk items into one owner confirmation. Critical
decision types are never in a batch. The owner may exclude any item; the
remainder is approved in one action and each excluded item becomes an
individual
pendingquestion. - Ask critical decisions individually with an explicit per-decision approval
by a named human whose authority class is eligible for the decision type
(as recorded in the
approval-authoritycontract). Route any KPI-meaning question toretail-kpi-knowledge; never invent meaning here. - Ask the domain's owner questions -- only those the scoped KPIs depend on. For
each retail KPI domain the table feeds, open that domain pack in the
retail-kpi-knowledgeskill and ask the cards in its## Owner questionssection that bear on a KPI actually in scope. Do not ask a card no scoped KPI depends on: an unanswered card is recorded as a criticalpendingdecision, and the decision gate filters by decision type, not by KPI, so an out-of-scope card blocks the whole stage (asking for Net Sales by Branch must not leave a same-store ruling pending). If scope later widens, ask the cards it newly depends on then. Each card names thedecision_typeits answer records under. A card's stated layer default is CONTEXT shown to the owner -- never record it as their ruling. An unanswered in-scope card is recordedpending/needs_user_input; this layer never invents a policy to make a number appear (that layer's ambiguity Resolution rule). Not every card type blocks: thekpi_contractsgate blocks onkpi_definition,policy_rulingandmissing_value_ruleonly, so a pendingdata_exclusionortable_graincard is recorded but does NOT stop contract authoring. Report those to the owner explicitly as open-but-not-blocking rather than implying the gate will catch them -- the gate's category list is the authority, not this step. This step supplies the KPI-meaning questions step 3 routes out; it does not define a metric or a contract. - Mask suspected PII by default. Show shape-preserving masks, cite the
suspicion source. Unmasking requires an explicit owner instruction, recorded
as a
pii_handlingdecision scoped to the affected columns. Never write a raw suspected-PII value into a committed store file. - Record every outcome in the Decision Store
(
.seshat/semantic-decisions.yaml,.seshat/kpi-contracts.yaml,.seshat/cleaning-rules.yaml) -- answered =>proposed/approved; refused =>rejected/deferred; unanswered =>pending/needs_user_input; sample needed but unavailable =>needs_sample. Confidence (low/medium/high) is the agent's proposal confidence only; it is never approval and never a readiness signal.
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.
- 9d ago First seen · 102 lines · 170 tokens per session scan A a32d5ad76f0a
business-knowledge-interview is a skill published in the GitHub repository Kemetra/Seshat-BI (2 stars, last pushed today), licensed Apache-2.0. It adds 170 tokens to every session and 1,311 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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