agentleFS
OneWave-AIOneWave-AI/claude-skills/client-health-dashboard/SKILL.md

client-health-dashboard

Skill378 starsChanged 52 days agoMITView on GitHub

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.

The file

We split this skill into the rules it gives an agent

12 rules. About 0 are common, 12 are distinctive, and 0 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈12

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Verify RAG assignments match score ranges.

    line 26
  2. Distinctive:

    If the user specifies particular clients, filter the report to those only.

    line 35
  3. Distinctive:

    If the user specifies a data source, prioritize it.

    line 36
  4. Distinctive:

    If the user provides CSV/Excel files, parse them as a primary source.

    line 37
  5. Distinctive:

    If the user requests a format variation, adapt accordingly.

    line 38
  6. Distinctive:

    If no data sources are accessible, explain what is needed and what to provide.

    line 40
  7. Distinctive:

    Never fabricate or hallucinate data.

    line 44
  8. Distinctive:

    Never include credentials, API keys, or PII beyond business contact info.

    line 45
  9. Distinctive:

    Keep health scores mathematically correct per the weighting formula.

    line 46
  10. Distinctive:

    Keep recommendations specific and actionable, not generic.

    line 47
  11. Distinctive:

    Keep the report self-contained, professional, and direct.

    line 48
  12. Distinctive:

    Do not use emojis anywhere in the report or any output.

    line 49

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

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.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.