Client Health Dashboard

Organization

OneWave-AI

client-health-dashboard

Generates a comprehensive client health overview across all accounts. Reads CRM data, support tickets, usage metrics, billing, and engagement logs. Calculates health scores, trend direction, and RAG status per client. Outputs a sorted risk report with recommended actions.

Overview

Publisher	OneWave-AI
Repository	`claude-skills`
Skill name	`client-health-dashboard`
Stars	293
Forks	49
Bundled files	4
License	MIT
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.
4 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by OneWave-AI on GitHub. Read the source before you install it.

Installation

Install the Client Health 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.
Enable the skill in any chat where you want it available.

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/OneWave-AI/claude-skills.git /tmp/claude-skills
mkdir -p .claude/skills
cp -r /tmp/claude-skills/client-health-dashboard .claude/skills/client-health-dashboard

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

Use it in TypingMind

Enable Client Health 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 Client Health 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 Client Health 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.

Client Health Dashboard

Generate a data-driven client health report: pull data from every available source, compute a weighted health score per client, and produce a prioritized risk report (client-health-report.md) sorted by risk with RAG status and actionable recommendations.

references/data-sources.md -- what to pull from CRM, support, usage, billing, and communication channels
references/scoring-model.md -- dimensions, weights, scoring rules, composite formula, RAG thresholds, trend logic
references/risk-and-recommendations.md -- risk factor triggers, per-dimension recommendation menus, expansion assessment
references/output-format.md -- exact report structure, formatting rules, and missing-data handling

Workflow

Collect data from every available source. Handle failures gracefully: log what was unavailable and proceed with partial data. Never fabricate data. See references/data-sources.md for the full source list and the fields to extract per client.
Score each client. Rate the five dimensions 0-100, apply weights, and compute the composite score. Assign RAG status and trend direction. See references/scoring-model.md.
Analyze risk and generate recommendations. Flag critical and warning risk factors, produce 2-4 specific recommendations targeting each client's weakest dimensions, and assess expansion potential for healthy accounts. See references/risk-and-recommendations.md.
Generate the report. Write client-health-report.md following the exact structure and formatting rules. Handle missing data by scoring neutral (50) and noting gaps. See references/output-format.md.
Validate before finalizing:
- Verify RAG assignments match score ranges.
- Confirm section ordering and within-section sorting.
- Confirm every client appears exactly once.
- Confirm each client has 2-4 specific, actionable recommendations.
- Attribute each data point to its source.
- Mark data gaps explicitly; never invent data that was not retrieved.

Interaction

If the user specifies particular clients, filter the report to those only.
If the user specifies a data source, prioritize it.
If the user provides CSV/Excel files, parse them as a primary source.
If the user requests a format variation, adapt accordingly.
Confirm the output path before writing.
If no data sources are accessible, explain what is needed and what to provide.

Constraints

Never fabricate or hallucinate data; report only what was retrieved, attributed to its source.
Never include credentials, API keys, or PII beyond business contact info.
Keep health scores mathematically correct per the weighting formula.
Keep recommendations specific and actionable, not generic.
Keep the report self-contained, professional, and direct.
Do not use emojis anywhere in the report or any output.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Client Health Dashboard AI skill do?

Why use Client Health Dashboard on TypingMind?

Because you install it once and use it with any model. Client Health 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 Client Health Dashboard in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/OneWave-AI/claude-skills/tree/main/client-health-dashboard. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Client Health 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 Client Health Dashboard?

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

Is the Client Health Dashboard AI skill free?

Yes. It is published on GitHub by OneWave-AI under the MIT license. 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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Client Health Dashboard

Overview