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 review-agent-insightsgit 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/review-agent-insights)<a href="https://agentmods.dev/skills/amplitude/mcp-marketplace/review-agent-insights"><img src="https://agentmods.dev/badge/skills/amplitude/mcp-marketplace/review-agent-insights/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/review-agent-insights"><img src="https://agentmods.dev/badge/skills/amplitude/mcp-marketplace/review-agent-insights.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 | $0.00095 | $0.02050 |
| Opus 5 | $0.00048 | $0.01025 |
| Sonnet 5 | $0.00019 | $0.00410 |
| Haiku 4.5 | $0.00010 | $0.00205 |
Grade A, and why
review-agent-insights 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 — 162 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Review Agent Insights
Surface everything Amplitude's AI agents have found recently. Query every available agent type in get_agent_results, validate for staleness, and synthesize into a unified narrative ranked by impact with concrete follow-up actions.
CRITICAL: Tool Reference
Primary tool:
Amplitude:get_agent_results— Retrieve pre-computed analyses from Amplitude's AI agents. Supports multiple agent types (check the tool'sagent_typeenum for the current list). Each agent type is queried separately. All support filtering bycreated_after,created_before,query,agent_params, andlimit.
Supporting tools:
Amplitude:get_amplitude_context/Amplitude:get_amplitude_context— Bootstrap user, org, and project info.Amplitude:use_amp_flagswithaction: "list_deployments"— Check whether fixes have shipped for flagged issues (staleness validation).
Instructions
Step 1: Bootstrap Context (1-2 calls)
- Call
Amplitude:get_amplitude_contextto get the user's org, projects, recent activity, and key dashboards. If multiple projects, ask which to review — or review all if the user wants a broad scan. - Call
Amplitude:get_amplitude_contextfor the target project's settings and AI context.
Determine the review window from the user's request:
- Default: last 7 days (good balance of recency and coverage).
- "What's new today?" → last 1-2 days.
- "Catch me up on this month" → last 14-30 days.
- Always compute the
created_afterISO 8601 timestamp for the review window.
Step 2: Query All Agent Types (parallel)
Check the get_agent_results tool descriptor to discover every available agent_type in the enum. Make one call per agent type, in parallel. For each:
agent_type: the agent type from the enumcreated_after: the review window timestamp from Step 1limit: 10
If the user asked about a specific area (e.g., "onboarding insights"), add a query matching that area to every call. If an agent type supports additional filtering via agent_params (e.g., impact ratings, categories, dashboard IDs), use them to focus results when the user's request suggests a narrower scope — otherwise omit agent_params to get the broadest view.
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 · 162 lines · 95 tokens per session scan A 3c796aa19a1c
review-agent-insights is a skill published in the GitHub repository amplitude/mcp-marketplace (35 stars, last pushed 4d ago), licensed MIT. It adds 95 tokens to every session and 2,050 once invoked, about $0.0005 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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