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 user-cohort-forensicsgit 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/user-cohort-forensics)<a href="https://agentmods.dev/skills/amplitude/mcp-marketplace/user-cohort-forensics"><img src="https://agentmods.dev/badge/skills/amplitude/mcp-marketplace/user-cohort-forensics/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/user-cohort-forensics"><img src="https://agentmods.dev/badge/skills/amplitude/mcp-marketplace/user-cohort-forensics.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 | $0.00060 | $0.00535 |
| Opus 5 | $0.00030 | $0.00267 |
| Sonnet 5 | $0.00012 | $0.00107 |
| Haiku 4.5 | $0.00006 | $0.00053 |
Grade A, and why
user-cohort-forensics 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.
What it actually says
User & Cohort Forensics
The arcs
Single user deep-dive: get_amp_user_data include: 'id' (resolve the
user: email, user ID, device ID, or amplitude ID) → include: 'profile'
(lifecycle, acquisition, usage stats) → include: 'timeline' (session-aware
event history). include: 'both' gets profile + timeline in one call when
you know you'll need both. Summarize journeys; don't dump raw events.
Population analysis (batched): build the user set first
(query_amplitude_data with a user-ID group_by to rank by volume, or
use_amplitude_cohorts action: 'find' for a filtered set) →
get_amp_user_data include: 'timeline' in parallel batches — up to 10
identifiers per call, 10–20 calls in flight is normal for population
analysis; hundreds of calls total is fine. Keep each call narrow (event
types, window) so responses stay small.
Cohort spot-check: prefer existing cohorts — use_amplitude_cohorts
action: 'list' or action: 'get' before building ad
hoc definitions. action: 'membership' verifies specific users. Check a
member's timeline for the exact markers (purchase, typing, checkout events)
rather than trusting the cohort definition blindly.
Email → ID resolution: get_amp_user_data include: 'id' per email;
uploaded email lists are resolved in batches of ≤10 identifiers per call.
For bulk exports, batch and note the retry pattern on individual failures.
Parameterization notes
include: 'timeline': always bound the window (last 30 days by default) and pass event-type filters when you know what you're looking for — unfiltered timelines are large and slow.- Rate-limit failures come back flagged
retryablewithretryAfterMs— back off instead of churning. - User identity: a user can match multiple IDs (device, user, email). Say which identity you resolved and flag ambiguous matches instead of picking one silently.
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 · 44 lines · 60 tokens per session scan A 799a7b2c6ff8
user-cohort-forensics is a skill published in the GitHub repository amplitude/mcp-marketplace (35 stars, last pushed 3d ago), licensed MIT. It adds 60 tokens to every session and 535 once invoked, about $0.0003 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.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…
next-partial-prefetching-adoption
Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…