Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add OneWave-AI/claude-skills --skill client-health-dashboardgit clone --depth 1 https://github.com/OneWave-AI/claude-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/onewave-ai/claude-skills/client-health-dashboard)<a href="https://agentmods.dev/skills/onewave-ai/claude-skills/client-health-dashboard"><img src="https://agentmods.dev/badge/skills/onewave-ai/claude-skills/client-health-dashboard/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/onewave-ai/claude-skills/client-health-dashboard"><img src="https://agentmods.dev/badge/skills/onewave-ai/claude-skills/client-health-dashboard.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00054 | $0.00894 |
| Opus 5 | $0.00027 | $0.00447 |
| Sonnet 5 | $0.00011 | $0.00179 |
| Haiku 4.5 | $0.00005 | $0.00089 |
Grade A, and why
client-health-dashboard scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 13d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 50 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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 channelsreferences/scoring-model.md-- dimensions, weights, scoring rules, composite formula, RAG thresholds, trend logicreferences/risk-and-recommendations.md-- risk factor triggers, per-dimension recommendation menus, expansion assessmentreferences/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.mdfor 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.mdfollowing the exact structure and formatting rules. Handle missing data by scoring neutral (50) and noting gaps. Seereferences/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.
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 13d ago First seen · 50 lines · 54 tokens per session scan A 659a7b34ac92
client-health-dashboard is a skill published in the GitHub repository OneWave-AI/claude-skills (291 stars, last pushed 1mo ago), licensed MIT. It adds 54 tokens to every session and 894 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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