Seshat-BI: Skill for Claude Code

.claude/skills/report-intent-interview/SKILL.md

report-intent-interview is a skill for Claude Code from Kemetra/Seshat-BI. It costs 192 tokens per session (1,425 once invoked), scanned A, original, Apache-2.0.

A guided interview that turns a request for a dashboard report into a written statement of its audience, purpose, decisions, questions, metrics, and review schedule. A dashboard is a visual report used to monitor information and support decisions.

In plain words
What is it for?
Use it before designing a dashboard to clarify who will use it, what decisions it supports, which questions it must answer, and how success will be reviewed.
Why use it?
It prevents later dashboard work from being based on an unstated guess about what the report should achieve.

Skill for Claude Code

Written for Claude Code: installed under .claude/. Also seen: reads .claude/ paths.

This is Kemetra/Seshat-BI's own configuration. It tells Claude Code how to work on Seshat-BI itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything Seshat-BI configures →

Reuse

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.

Copy the file
curl -O https://raw.githubusercontent.com/Kemetra/Seshat-BI/main/.claude/skills/report-intent-interview/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/Kemetra/Seshat-BI

Made for: Claude Code.

Wrote 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.

agentmods badge for report-intent-interview

README.md
[![agentmods](https://agentmods.dev/badge/skills/kemetra/seshat-bi/report-intent-interview/github.svg)](https://agentmods.dev/skills/kemetra/seshat-bi/report-intent-interview)
Your own site
<a href="https://agentmods.dev/skills/kemetra/seshat-bi/report-intent-interview"><img src="https://agentmods.dev/badge/skills/kemetra/seshat-bi/report-intent-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.

agentmods 80×15 button for report-intent-interview

Your own site · 80×15
<a href="https://agentmods.dev/skills/kemetra/seshat-bi/report-intent-interview"><img src="https://agentmods.dev/badge/skills/kemetra/seshat-bi/report-intent-interview.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 192 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,425 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00192 $0.01425
Opus 5 $0.00096 $0.00713
Sonnet 5 $0.00038 $0.00285
Haiku 4.5 $0.00019 $0.00143

Measured 8d ago against content hash ea24ab021cde, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

report-intent-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 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.

.claude/skills/report-intent-interview/SKILL.md · 113 lines

How it starts

The opening of the file, as written. The whole thing — 113 lines — stays where its author put it; the contents beside it link to each section on GitHub.

report-intent-interview

The Report Intent Interview is the missing front end of the dashboard intelligence journey (spec 123, US1). It converts a conversational report request into a committed, reviewable report-intent.yaml so every downstream artifact (blueprint, composition, preview, audit, PBIR) traces back to a clear, approved report purpose instead of an unrecorded assumption.

This skill mirrors .claude/skills/business-knowledge-interview/SKILL.md structurally (load-existing-first, batch/critical question grouping, PII masking, record + STOP, never self-grant) but does NOT extend it: the required_inputs and focus differ. Behavior is governed by the contract contracts/interview/report-intent-interview.yaml; the shape of the produced artifact is templates/report-intent.yaml. The static rule DL9 (seshat check) verifies the recorded artifact's SHAPE, not the conversation.

When to use

  • Someone describes a report they need in plain language ("I need a weekly report for branch managers to spot underperforming branches").
  • Before dashboard-design, the dashboard coordinator, page blueprints, visual specs, or report composition -- those stages trace their business_question back to a question declared here (FR-002a) and are blocked until this intent's own report_intent_approval decision is approved.

Preconditions (stop if unmet)

  • Approved metric contracts exist for the subject area (readiness.status: pass). No approved contract for a metric the request needs => record a gap, route upstream to metric-contract definition, and leave the intent blocked on that metric (FR-004) -- never define the metric here.
  • The semantic model is ready (semantic_model_ready: pass). Not ready => stop, name the missing readiness, and do not proceed to design.
  • Load the existing Report Intent (if any) first. An existing intent is presented for confirmation or supersession -- never silently overwritten.

How it runs

  1. Ground every question in the request and the approved contracts. Focus on: audience, purpose, supported decision, review cadence, primary business questions, metric roles by name (outcome/driver/guardrail), comparisons, dimensions and filters, expected actions and exceptions, pages/drill paths, mobile/accessibility/language/RTL needs, and exclusions/non-goals.
  2. Refuse to commit a vague request. "Build an executive dashboard" gets focused disambiguating questions; the intent is NOT committed until audience, purpose, and at least one primary business_questions entry are resolved (US1 AC#4).
  3. Batch the obvious low-risk items into one owner confirmation (e.g. comparison phrasing, filter defaults). 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 pending question.
  4. Ask critical decisions individually. The intent's own approval (report_intent_approval) is a critical decision type, authority class report_owner (contracts/knowledge/approval-authority.yaml); it requires an explicit, per-decision approval from a named human -- the agent never self-grants it (Principle V).
  5. Reference metrics by name only. Every outcome_metrics / driver_metrics / guardrail_metrics entry names an approved metric contract (name + store_ref + status_required: pass) -- the same triple shape as dashboard-page-blueprint.yaml's required_metric_contracts. There is no formula/DAX field. A metric with no approved contract is a gap: record it, route it upstream, and leave the intent blocked on that metric -- never invent the metric here.
  6. Mask suspected PII by default. Any PII-adjacent dimension/filter follows the shipped masking behavior; unmasking requires an explicit owner instruction, recorded as a pii_handling decision.
  7. Record the artifact and the decision. Write mappings/<subject-area>/design/report-intent.yaml per templates/report-intent.yaml, and record the report_intent_approval decision in the project Decision Store once a named human of authority class report_owner approves. Unanswered items stay pending / needs_user_input; a metric gap stays blocked with a matching blocking_reasons entry.

Read the full file on GitHub · 113 lines

Changes

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.

  1. 8d ago First seen · 113 lines · 192 tokens per session scan A ea24ab021cde

Subscribe to this mod's changes

report-intent-interview is a skill published in the GitHub repository Kemetra/Seshat-BI (2 stars, last pushed 6d ago), licensed Apache-2.0. It adds 192 tokens to every session and 1,425 once invoked, about $0.0010 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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