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 investigate-ai-sessiongit 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/investigate-ai-session)<a href="https://agentmods.dev/skills/amplitude/mcp-marketplace/investigate-ai-session"><img src="https://agentmods.dev/badge/skills/amplitude/mcp-marketplace/investigate-ai-session/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/investigate-ai-session"><img src="https://agentmods.dev/badge/skills/amplitude/mcp-marketplace/investigate-ai-session.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 | $0.00075 | $0.02315 |
| Opus 5 | $0.00037 | $0.01157 |
| Sonnet 5 | $0.00015 | $0.00463 |
| Haiku 4.5 | $0.00007 | $0.00231 |
Grade A, and why
investigate-ai-session 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 7d 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 Session Investigator
You investigate specific AI agent sessions or failure patterns to determine root causes. You operate at the session and span level — reading conversations, tracing execution, and connecting failures to their origins. This is the "why" skill that follows the "what" from /monitor-ai-quality.
Instructions
Step 1: Determine Investigation Scope
The user will provide one of:
- A specific session ID → go directly to Step 2
- A failure pattern (e.g., "Chart Agent timeouts", "tool errors in the last day") → go to Step 1b
- A user complaint (e.g., "user X said the agent didn't work") → go to Step 1c
- A vague signal (e.g., "something's off with the agents") → redirect to
/monitor-ai-qualityfirst, then come back with specific findings
Step 1b: Find Sessions Matching a Pattern
Call Amplitude:get_amplitude_agent_analytics_info with view: "schema" to discover valid agent names, tool names, and evaluator fields. Then call it with view: "sessions" and supported filters:
- Agent failures:
agentNames: ["<agent>"],hasTaskFailure: true - Tool errors:
toolNames: ["<tool>"],hasTaskFailure: true - Technical failures:
hasTechnicalFailure: true - Low quality: fetch recent sessions, then select evaluator quality scores at or below 0.4
- Frustrated users:
hasNegativeFeedback: true, or select evaluator sentiment scores at or below 0.4 - Expensive sessions:
minCostUsd: <threshold> - Slow sessions:
minDurationMs: <threshold> - Specific topic: fetch recent sessions, then select matching evaluator topic classifications locally
Use responseFormat: "concise", limit: 20, and sort by "-session_start" to get recent examples. Select the 3-5 most representative sessions for deep investigation.
Step 1c: Find a Specific User's Sessions
Call Amplitude:get_amplitude_agent_analytics_info with view: "sessions" and the supported user identifier filter to find their sessions. If they reported a specific timeframe, add the date filters. Pick the session(s) that match the complaint.
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.
- 7d ago Changed · +2 lines 1a7bdca2f8af
- 12d ago First seen · 163 lines · 75 tokens per session scan A a67de8dbddef
investigate-ai-session is a skill published in the GitHub repository amplitude/mcp-marketplace (35 stars, last pushed 4d ago), licensed MIT. It adds 75 tokens to every session and 2,315 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.
Other skills, from other repositories
debug-optimize-lcp
Guides debugging and optimizing Largest Contentful Paint (LCP) using Chrome DevTools MCP tools. Use this skill whenever the user asks about LCP performance, slow page loads, Core Web Vitals optimization, or wants to understand why their page's main content takes too long to appear. Also use when the user mentions…
systematic-debugging
Use when debugging a failing test, build error, or runtime issue that isn't immediately obvious. Guides a 4-phase root cause analysis instead of random fix attempts.
diagnose
Trace from a reproduced symptom to the source code that causes it. Pin the specific file and approximate line, rate confidence in the cause and clarity of the fix independently, and always propose a concrete fix.
repro-admin
Reproduce an EmDash admin UI bug. Attach a container, start the demo dev server, drive the admin with agent-browser using the dev-bypass session, and capture the reproduction as screenshots plus a replayable transcript.
log-error-digest
Analyze log files to troubleshoot errors, identify peak error periods, and produce error clustering, frequency statistics, and time distribution reports. Supports JSON, syslog, and Nginx formats with automatic detection. Use when a user uploads a .log file and asks to analyze errors, find patterns, debug issues, or…
byted-util-volcengine-detect-retry
An orchestration workflow for Volcengine Cloud Detect, a service that checks websites or network endpoints from test locations.