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 analyze-ai-topicsgit 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/analyze-ai-topics)<a href="https://agentmods.dev/skills/amplitude/mcp-marketplace/analyze-ai-topics"><img src="https://agentmods.dev/badge/skills/amplitude/mcp-marketplace/analyze-ai-topics/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/analyze-ai-topics"><img src="https://agentmods.dev/badge/skills/amplitude/mcp-marketplace/analyze-ai-topics.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 | $0.00086 | $0.02001 |
| Opus 5 | $0.00043 | $0.01001 |
| Sonnet 5 | $0.00017 | $0.00400 |
| Haiku 4.5 | $0.00009 | $0.00200 |
Grade A, and why
analyze-ai-topics 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 — 144 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Topic Analyzer
You analyze what users ask AI agents about and how well each topic is served — surfacing underserved areas, coverage gaps, and product opportunities from conversation patterns. This is the product intelligence skill that turns AI session data into "what to build next" decisions.
Instructions
Step 1: Get Context and Schema
- Get context. Call
Amplitude:get_amplitude_contextto identify projects and user role. - Get AI schema. Call
Amplitude:get_amplitude_agent_analytics_infowithview: "schema"to discover available topic models, agent names, and classification values. The schema tells you what topic dimensions exist (e.g., product_area, intent, error_domain) — these vary by project. - Determine scope. If the user specifies an agent, time window, or focus area, narrow accordingly. Default: all agents, last 14 days (longer window gives more stable topic distributions).
Step 2: Map the Topic Landscape
Run these in parallel:
-
Topic breakdown with quality. Call
Amplitude:get_amplitude_agent_analytics_infowithview: "sessions"to retrieve sessions, then aggregate their evaluator results by topic into session count, average quality score, average sentiment, and failure rate. Limit the output to 50 topics. This is the core dataset. -
Agent-by-topic matrix. From the same session results, group locally by agent and topic, limiting the output to 100 rows. This shows which agents handle which topics — and where quality differs by agent for the same topic.
-
Volume trend by topic. Group the session results locally by day and topic. Combine this with the topic breakdown to understand whether total volume growth is driven by specific topics.
-
Failure sessions by topic. Call
Amplitude:get_amplitude_agent_analytics_infowithview: "sessions"andhasTaskFailure: true, then group the returned sessions locally by topic. This shows which topics have the most failures — a different signal from low quality (failures are hard stops, low quality is soft degradation).
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 · 144 lines · 86 tokens per session scan A d69a45a0b1d5
analyze-ai-topics is a skill published in the GitHub repository amplitude/mcp-marketplace (35 stars, last pushed 4d ago), licensed MIT. It adds 86 tokens to every session and 2,001 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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