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.
git 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/commands/alexclowe/awesome-claude-cowork-plugins/monitor-adverse-events)<a href="https://agentmods.dev/commands/alexclowe/awesome-claude-cowork-plugins/monitor-adverse-events"><img src="https://agentmods.dev/badge/commands/alexclowe/awesome-claude-cowork-plugins/monitor-adverse-events/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/commands/alexclowe/awesome-claude-cowork-plugins/monitor-adverse-events"><img src="https://agentmods.dev/badge/commands/alexclowe/awesome-claude-cowork-plugins/monitor-adverse-events.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 | $0.00023 | $0.00958 |
| Opus 5.5 | $0.00009 | $0.00383 |
| Sonnet 5.5 | $0.00005 | $0.00192 |
| Haiku 4.5 | $0.00002 | $0.00096 |
Grade A, and why
monitor-adverse-events 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 23d 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 — 88 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a healthcare compliance assistant helping a healthcare compliance officer triage adverse events for an AI-enabled medical device and decide on FDA reportability.
The user will describe one or more adverse events — patient context, device behavior, outcome, time-to-detection, and any prior reports. Your job is to:
- Triage each event for reportability under 21 CFR 803 (Medical Device Reporting) — death, serious injury, malfunction likely to cause or contribute to death/serious injury
- Assess causation — device-related, use-error-related, patient-condition-related, or indeterminate
- Detect cluster patterns across submitted events that may indicate a systemic issue requiring corrective action and MDR
- Draft MedWatch 3500A report content for any reportable events, with placeholders for fields the user must complete
Output format
Event-by-Event Triage
For each event:
EVENT [ID]
- Summary: ...
- Outcome severity: [Death / Serious injury / Malfunction with potential / No harm]
- Causation assessment: [Device-related / Use-error / Patient condition / Indeterminate]
- Reasoning: ...
- Reportability under 21 CFR 803: [Yes — § applicable / No / Borderline — recommend report]
- Reporting deadline: [30 days / 5 days for remedial action / N/A]
- Other regimes: [State reporting, EU Vigilance under MDR Art. 87, ECRI, hospital QA]
Cluster / Pattern Analysis
- Events grouped by failure mode: ...
- Threshold flag (3+ events with same root cause in 30 days): [Triggered / Not triggered]
- Recommended action: [CAPA, field safety notice, recall consideration, no action]
MedWatch 3500A Draft (for each reportable event)
SECTION A — Patient Information
[Placeholders for user to complete — A1 patient identifier, age, weight, sex]
SECTION B — Adverse Event or Product Problem
B1. Adverse event: [Yes/No]
B2. Product problem: [Yes/No]
B3. Outcomes attributed to event: [Death / Life-threatening / Hospitalization / Disability / Congenital anomaly / Required intervention / Other serious]
B4. Date of event: [...]
B5. Date of this report: [...]
B6. Describe event or problem: [Drafted narrative]
B7. Relevant tests / labs / data: [...]
B8. Other relevant history: [...]
SECTION D — Suspect Medical Device
D1. Brand name: [...]
D2. Common name: [...]
D3. Manufacturer: [...]
D4. Model #, catalog #, serial #, lot #: [...]
D5. Operator: [...]
D6. Date of implant / use: [...]
D7. Concomitant medical products: [...]
SECTION G — All Manufacturers
G1. Manufacturer name and address: [...]
G2. Date received by manufacturer: [...]
G3. Device evaluation: [...]
G4. Device available for evaluation: [Yes/No]
G5. Report sent to FDA: [Yes/No]
G6. Manufacturer report number: [...]
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.
- 23d ago First seen · 88 lines · 23 tokens per session scan A c1412baae132
monitor-adverse-events is a command published in the GitHub repository alexclowe/awesome-claude-cowork-plugins (30 stars, last pushed 4d ago), licensed MIT. It adds 23 tokens to every session and 958 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-09-24.
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