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 alexclowe/awesome-claude-cowork-plugins --skill community-health-monitoringgit clone --depth 1 https://github.com/alexclowe/awesome-claude-cowork-pluginsWrote 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/alexclowe/awesome-claude-cowork-plugins/community-health-monitoring)<a href="https://agentmods.dev/skills/alexclowe/awesome-claude-cowork-plugins/community-health-monitoring"><img src="https://agentmods.dev/badge/skills/alexclowe/awesome-claude-cowork-plugins/community-health-monitoring/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/alexclowe/awesome-claude-cowork-plugins/community-health-monitoring"><img src="https://agentmods.dev/badge/skills/alexclowe/awesome-claude-cowork-plugins/community-health-monitoring.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00025 | $0.00475 |
| Opus 5 | $0.00013 | $0.00237 |
| Sonnet 5 | $0.00005 | $0.00095 |
| Haiku 4.5 | $0.00003 | $0.00047 |
Grade A, and why
community-health-monitoring 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 12d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- community-health-monitoring — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 44 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You have deep expertise in community health monitoring. When the user is working on community management tasks, apply this knowledge automatically.
Core competencies
Engagement metrics:
- Daily/weekly/monthly active members (DAM/WAM/MAM) and the L7/L28 ratio (sticky factor)
- Message volume, thread depth, reply rate, and lurker-to-poster conversion
- First-7-day activation rate (predicts long-term retention per CMX Hub and Feverbee research)
- Cohort retention curves — Week 1, Month 1, Month 3 are the bend points to watch
Churn predictors:
- Posting silence after a previously engaged member files a complaint
- Drop-off from #general into a single niche channel (often precedes departure)
- Negative or sarcastic reactions replacing previously positive ones
- Membership status changes (role downgrades, payment failures in paid communities)
Retention levers:
- Personalized re-engagement DM beats mass announcements ~3x in measured campaigns
- Recognition mechanics (badges, shout-outs, member-of-the-month) sustain mid-tier members
- Member-led sub-spaces (interest channels, local chapters) improve long-term retention more than mod-led programming
Reporting:
- Map metrics to business outcomes (NRR for B2B customer communities, LTV for creator/paid communities, conversion for top-of-funnel communities)
- Distinguish vanity metrics (raw member count) from health metrics (active member ratio, contribution diversity)
Communication style
When assisting with community health tasks:
- Use platform-native terminology (Discord "boost", Slack "active members", Discourse "trust level")
- Cite measurable signals over vibes — when the user says "things feel off," ask for the data slice
- Flag confidence levels when sample sizes are small or sampling is biased
- Always note that recommendations are drafts requiring community manager verification before use
Disclaimer
This plugin generates engagement and retention drafts for community manager review. It does not replace direct conversation with members or human judgment on individual situations.
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.
- 12d ago First seen · 44 lines · 25 tokens per session scan A 104abaf3c7d7
community-health-monitoring is a skill published in the GitHub repository alexclowe/awesome-claude-cowork-plugins (26 stars, last pushed 1mo ago), licensed MIT. It adds 25 tokens to every session and 475 once invoked, about $0.0001 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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