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-experimentsgit 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-experiments)<a href="https://agentmods.dev/skills/amplitude/mcp-marketplace/monitor-experiments"><img src="https://agentmods.dev/badge/skills/amplitude/mcp-marketplace/monitor-experiments/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-experiments"><img src="https://agentmods.dev/badge/skills/amplitude/mcp-marketplace/monitor-experiments.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 | $0.00069 | $0.03206 |
| Opus 5 | $0.00034 | $0.01603 |
| Sonnet 5 | $0.00014 | $0.00641 |
| Haiku 4.5 | $0.00007 | $0.00321 |
Grade A, and why
monitor-experiments 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 9d 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.
This is a copy
91% identical to monitor-experiments-consolidated — 24 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 306 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Experiment Monitor & Report Generator
Scan active and recently completed experiments, surface what needs attention, and report on the ones that matter.
This is a monitoring skill — keep output accessible to non-experts. Avoid statistical jargon (no p-values, no power analysis). For deep-dive analysis of a specific experiment, use the analyze-experiments skill instead.
CRITICAL: Managing Response Sizes
get_experiments: 3-5 IDs max per call. Filter usingsearchresults BEFORE fetching.query_experimentresponses are large. Extract onlysummaryobjects and validity flags. Ignoretimeseries,xValues, bulk arrays.- Metric name resolution:
searchdoes NOT match metric IDs inqueries. Search withentityTypes: ["METRIC"], emptyqueries,limitPerQuery: 50, scoped to project. Match IDs from results.
Report Structure
The report has two parts:
- Summary & Actions (top) — One table + action items. Someone should be able to read just this and know the full picture.
- Details (bottom) — Deep-dives on experiments that need attention, briefs on recently decided, one-liners for monitoring experiments, and a needs-setup list.
Do NOT duplicate information between the summary table and the details. The summary table is the single source of truth for the portfolio state. Details expand on specific experiments.
Instructions
Step 1: Context & Discovery
- Call
Amplitude:get_amplitude_context. If multiple projects, ask which to monitor. - Search for experiments:
Amplitude:search({
entityTypes: ["EXPERIMENT"],
appIds: [projectId],
queries: [],
sortOrder: "lastModified",
sortDirection: "DESC",
limitPerQuery: 50
})
- Filtering rules — include experiments that are:
- Running and not stale: Any experiment in a running state that is NOT marked as stale. Stale experiments have gone idle and should be excluded.
- Recently decided: Completed experiments that have a decision recorded AND were modified within the last 14 days. These are worth reviewing to confirm the decision or share learnings.
- Exclude: Drafts, disabled experiments, and stale experiments.
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.
- 9d ago First seen · 306 lines · 69 tokens per session scan A 3b521a53a27b
monitor-experiments is a skill published in the GitHub repository amplitude/mcp-marketplace (35 stars, last pushed today), licensed MIT. It adds 69 tokens to every session and 3,206 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 91% identical to monitor-experiments-consolidated, differing in 24 lines, and is treated as a copy.
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