Kai Data Dashboard

Community

cgallic

kai-data-dashboard

Convert Kai workflow data, CSV exports, audit folders, SDR package outputs, and marketing reports into dashboard-ready specs or lightweight static dashboards. Use when "data dashboard", "operator dashboard", "operator room", "HTML operators room", "sales dashboard", "SDR dashboard", "turn this data into a dashboard", "dashboard handoff", "visualize Kai data", or any request to package sourced marketing, sales, audit, or SDR data for a dashboard or presentation surface.

Overview

Publisher	cgallic
Repository	`kai-cmo-harness`
Skill name	`kai-data-dashboard`
Stars	61
Forks	8
Bundled files	Instructions only
Links	Skill source Repository Homepage

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
Self-contained
Everything the model needs lives in the instructions — no extra files to sync.
Open source
Published by cgallic on GitHub. Read the source before you install it.

Installation

Install the Kai Data Dashboard AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/cgallic/kai-cmo-harness/tree/main/harness/skills-v2/kai-data-dashboard
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/cgallic/kai-cmo-harness/tree/main/harness/skills-v2/kai-data-dashboard

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/cgallic/kai-cmo-harness.git /tmp/kai-cmo-harness
mkdir -p .claude/skills
cp -r /tmp/kai-cmo-harness/harness/skills-v2/kai-data-dashboard .claude/skills/kai-data-dashboard

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Kai Data Dashboard in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Kai Data Dashboard on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Kai Data Dashboard is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

/kai-data-dashboard — Sourced Data As An Operator Surface

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.

Objective

A dashboard handoff package that a frontend, BI team, or single static HTML file can build from without anyone inventing a number: every metric defined by formula, every metric bound to a named source, every unsupported field visible as a gap. The data already exists — this skill packages it into an operator surface.

This is a companion surface. It does not replace analytics setup, audit analysis, or outbound strategy.

Done when

Work type audit-report — floor E3/C4/O1 (harness/eco-floors.yaml). This package is client- or operator-facing and carries quantitative claims, so it inherits the audit floor rather than the internal default.

E3 — a named human approved the exact delivered files, and every number in the spec resolves to a row in source-map.md or the source folder's _data-sources.md.
C4 — the Kai Data Provenance Rule holds. Mode declared, sources cited, gaps written down. For audit source folders, audit_provenance_lint.py passes.
O1 — every alert threshold names the metric it watches and whether the threshold is sourced or a stated hypothesis. A threshold with no label is not finished.

Constraints

A source is required. Accept workspace/sdr-operator/<package-slug>/, any folder holding kai-data.json, audit-data.json, _data-sources.md, or _data-gaps.md, CSV exports from CRM/ESP/sequencer/ads/analytics/sales tools, markdown reports with source-backed findings, or user-provided metrics and targets. If there is no source folder or file, ask for it.
No fabricated sample data unless the output is explicitly labeled internal_demo.
Declare the data mode on the artifacts: sales_external, onboarding_connected, user_provided, or internal_demo.
Read the provenance ledgers before designing anything — _data-sources.md, _data-gaps.md, and the available JSON/CSV files. Do not add numbers that are not present in the source. Unsupported fields are listed as gaps.

For audit source folders, run the blocking lint:

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

Before handoff: every number has a source, retrieval date, or internal_demo label; every metric has a formula or definition; _data-gaps.md / data-gaps.md is represented in the surface; no placeholder text remains; sensitive fields are excluded, masked, or explicitly approved. HTML output is readable at desktop and mobile widths.
Gaps are a first-class panel, never hidden or filled in.
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.
Static HTML rules when index.html is produced: single file unless app integration is requested; tables for dense operator data; restrained styling with readable status colors and responsive layout; critical numbers as text rather than only canvas or images; a source footer or source drawer; empty states for missing metrics.

Context

Need	Load
Provenance rule, modes, source tiers, gap handling	`harness/references/audit-data-provenance.md`
Source-folder ledgers and collected data	`_data-sources.md`, `_data-gaps.md`, `kai-data.json` / `audit-data.json` in the source folder
Tracking plan or attribution model instead	`/kai-analytics`
Client-ready audit deck instead	`/kai-html-presentation`
Durable, white-labeled client product with onboarding wizard and page set	`/kai-client-dashboard` (it calls this skill for the data-contract layer underneath it)

Dashboard type — pick from source and request. Default to sdr_operator_room when the source came from /kai-sdr-operator.

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

Spec contract. Every dashboard spec carries: 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, error states; alert thresholds with source or hypothesis label; permissions and sensitive-data handling; handoff notes for frontend, BI, or static HTML build.

SDR dashboards additionally carry: status counts by sourced, enriched, approved_for_copy, queued, sent, replied, meeting_booked, disqualified, suppressed, 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.

Output goes to <source-folder>/dashboard/: dashboard-spec.md, metrics-dictionary.md, data-contract.json, source-map.md, data-gaps.md, plus index.html when the user asks for a usable static artifact. Report the folder path, dashboard type, files produced, data gaps, and whether HTML was built or only specified.

Escalate when

No source folder or file was given, or the named source contains no retrievable data.
The user wants a metric the source cannot support — that is a gap, and forcing it needs a human decision.
Sensitive fields (PII, contact records, call recordings, revenue detail) would appear on a surface whose permissions are undefined.
The request is a full frontend app build rather than a spec plus optional static file.
Sample data is requested for a surface that will not be labeled internal_demo.

Frequently asked questions

What does the Kai Data Dashboard AI skill do?

Why use Kai Data Dashboard on TypingMind?

Because you install it once and use it with any model. Kai Data Dashboard is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Kai Data Dashboard in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/cgallic/kai-cmo-harness/tree/main/harness/skills-v2/kai-data-dashboard. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Kai Data Dashboard?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Kai Data Dashboard?

As many as you like. As long as a model supports skills, you can use Kai Data Dashboard with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Kai Data Dashboard AI skill free?

It is published on GitHub by cgallic. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

/kai-data-dashboard — Sourced Data As An Operator Surface

Objective

Done when

Constraints

Context

Escalate when

Frequently asked questions

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