Kai data dashboard

kai-data-dashboard - Data To Dashboard Handoff

Kai root note: knowledge/, harness/, and scripts/ paths in this skill live in the Kai install, not the user's project. Resolve them against the first ancestor directory of this SKILL.md that contains a knowledge/ folder (the Kai plugin root, ~/.claude/kai, or the kai-cmo-harness repo). MARKETING.md, memory/, and any output files live in the current project. If a referenced scripts/ command is not available in this install, say so, skip it, and continue with the file-based guidance — never fabricate its output.

Turn existing Kai artifacts into a dashboard spec, data contract, or lightweight static dashboard. This skill is a companion surface. It should not replace analytics setup, audit analysis, or outbound strategy.

Use /kai-analytics when the user needs a tracking plan or attribution model. Use /kai-html-presentation when the user needs a client-ready audit deck. Use this skill when the data already exists and needs to become a dashboard-ready operator surface.

Phase 0: Identify The Source

Accept these inputs:

• workspace/sdr-operator/<package-slug>/
• Any folder with kai-data.json, audit-data.json, _data-sources.md, or _data-gaps.md
• CSV exports from CRM, ESP, sequencer, ads, analytics, or sales tools
• Markdown reports with source-backed findings
• User-provided metrics and targets

If there is no source folder or file, ask for it. Do not fabricate sample data unless the output is explicitly labeled internal_demo.

Phase 1: Load Provenance

Before designing the dashboard:

1. Read _data-sources.md, _data-gaps.md, and available JSON/CSV files.
2. Declare data mode: sales_external, onboarding_connected, user_provided, or internal_demo.
3. List unsupported fields as gaps.
4. Do not add numbers that are not present in the source.

For audit folders, run:

python scripts/quality_gates/audit_provenance_lint.py <source-folder> --audit-dir

Phase 2: Choose Dashboard Type

Pick the dashboard type from the source and request:

Type	Best Fit	Primary View
sdr_operator_room	SDR package, lead ledger, reply data	Pipeline state, source quality, next actions
marketing_ops	Campaign, content, SEO, ad, lifecycle data	Channel performance and bottlenecks
executive_scorecard	Monthly/weekly report	KPIs, decisions, risks, next steps
audit_delivery	Audit folder	Findings, scorecards, fixes, data gaps
connector_health	API sync or integration data	Source freshness, failures, missing credentials

Default to sdr_operator_room when the source is from /kai-sdr-operator.

Phase 3: Produce Dashboard Artifacts

Write output to:

<source-folder>/dashboard/

Required files:

dashboard-spec.md
metrics-dictionary.md
data-contract.json
source-map.md
data-gaps.md

Optional file when the user asks for a usable static artifact:

index.html

Do not build a full frontend app unless the user asks for implementation. For app builds, hand the spec to the relevant frontend skill or repository code.

Dashboard Spec Requirements

Each dashboard spec must include:

• Audience: executive, operator, SDR, marketer, client, founder, or analyst.
• Jobs to be done.
• Metric definitions with exact formulas.
• Data source per metric.
• Refresh cadence and freshness warning.
• Widgets, filters, drilldowns, empty states, and error states.
• Alert thresholds with source or hypothesis label.
• Permissions and sensitive-data handling.
• Handoff notes for frontend, BI, or static HTML build.

For SDR dashboards, include:

• Status counts by sourced, enriched, approved_for_copy, queued, sent, replied, meeting_booked, disqualified, suppressed, and blocked.
• Source quality table.
• Fit score distribution.
• Next-action queue.
• Reply triage categories.
• Suppression, bounce, opt-out, and complaint warnings.
• Data gaps that block live outreach.

Static HTML Rules

If writing index.html:

• Keep it single-file unless the user requests app integration.
• Use tables for dense operator data.
• Use restrained styling, readable status colors, and responsive layouts.
• Keep critical numbers visible as text, not only canvas or images.
• Include a source footer or source drawer.
• Include empty states for missing metrics.
• Do not hide gaps. Show them as a first-class panel.

Quality Gates

Before handoff:

1. Confirm every number has a source, retrieval date, or internal_demo label.
2. Confirm every metric has a formula or definition.
3. Confirm _data-gaps.md or data-gaps.md is represented.
4. Confirm no placeholder text remains.
5. Confirm sensitive fields are either excluded, masked, or explicitly approved.
6. If HTML is produced, check desktop and mobile readability.

Output Summary

Final response should include:

• Dashboard folder path.
• Dashboard type.
• Files produced.
• Data gaps.
• Whether a static HTML dashboard was built or only specified.