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client-health-dashboard

client-health-dashboard

github.com/onewave-aiMarketing & growth393259 installsUpdated 1w ago
Repository

About client-health-dashboard

A client-health reporting skill for teams managing customer relationships. It combines available business data into weighted scores, risk rankings, and actionable recommendations in `client-health-report.md`.

Key features

  1. 01Collect data from available sources
  2. 02Calculate weighted client health scores
  3. 03Assign RAG status and trend
  4. 04Rank risks and suggest actions
  5. 05Note missing data without fabrication

Use cases

  1. 01Reviewing health across a client portfolio
  2. 02Prioritizing accounts that need attention
  3. 03Assessing expansion potential for healthy clients

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/onewave-ai/claude-skills --skill client-health-dashboard

After installing, type/client-health-dashboardin Claude Code to confirm it loaded.

Bundled files

References and scripts installed along with SKILL.md.

references/
data-sources.md
output-format.md
risk-and-recommendations.md
scoring-model.md

Details

From the project README

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.

Contents

  • 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

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.

Comments

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