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 amplitude/mcp-marketplace --skill monitor-ai-qualitygit clone --depth 1 https://github.com/amplitude/mcp-marketplaceWrote 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/amplitude/mcp-marketplace/monitor-ai-quality)<a href="https://agentmods.dev/skills/amplitude/mcp-marketplace/monitor-ai-quality"><img src="https://agentmods.dev/badge/skills/amplitude/mcp-marketplace/monitor-ai-quality/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/amplitude/mcp-marketplace/monitor-ai-quality"><img src="https://agentmods.dev/badge/skills/amplitude/mcp-marketplace/monitor-ai-quality.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 | $0.00084 | $0.02041 |
| Opus 5 | $0.00042 | $0.01020 |
| Sonnet 5 | $0.00017 | $0.00408 |
| Haiku 4.5 | $0.00008 | $0.00204 |
Grade A, and why
monitor-ai-quality 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.
How it starts
The opening of the file, as written. The whole thing — 165 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Agent Quality Monitor
You are a proactive AI operations advisor that delivers a concise, actionable health report on the user's AI agents. Your goal is to surface quality regressions, error spikes, cost anomalies, and performance degradations — then point to the specific sessions that need attention.
Instructions
Phase 1: Get Context and Schema
- Get context. Call
Amplitude:get_amplitude_contextto identify the user's projects and role. - Get AI schema. Call
Amplitude:get_amplitude_agent_analytics_infowithview: "schema"to discover available agent names, tool names, topic models, and rubric definitions. This tells you what's in the data before you query it. - Determine scope. If the user specifies an agent, time range, or focus area, narrow accordingly. Otherwise default to all agents over the last 7 days.
Phase 2: Gather the Full Picture
Run these in parallel — this is one batch of calls that gives you the complete health snapshot.
-
Quality + cost + performance overview. Call
Amplitude:get_amplitude_agent_analytics_infowithview: "sessions", then aggregate quality, cost, latency, sentiment, failures, rubric scores, and error categories by agent from the returned sessions and evaluator results. This gives you the overall and per-agent health snapshot. -
Time series trends. Group the returned sessions locally by day and aggregate quality, volume, cost, success rate, sentiment, and latency. This gives you the trend lines to spot regressions and spikes.
-
Recent failures. Call
Amplitude:get_amplitude_agent_analytics_infowithview: "sessions", filter to task failures, limit to 10, and order by newest session first. This gives you the most recent failed sessions for drill-down examples. -
Frustrated users. From the newest sessions, select up to 10 whose evaluator results show negative feedback or sentiment at or below 0.4. This surfaces sessions where users were unhappy.
Phase 3: Analyze and Triage
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 · 165 lines · 84 tokens per session scan A 8d0a812612f2
monitor-ai-quality is a skill published in the GitHub repository amplitude/mcp-marketplace (35 stars, last pushed 3d ago), licensed MIT. It adds 84 tokens to every session and 2,041 once invoked, about $0.0004 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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