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 taxonomygit 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/taxonomy)<a href="https://agentmods.dev/skills/amplitude/mcp-marketplace/taxonomy"><img src="https://agentmods.dev/badge/skills/amplitude/mcp-marketplace/taxonomy/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/taxonomy"><img src="https://agentmods.dev/badge/skills/amplitude/mcp-marketplace/taxonomy.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector pass
SkillSpector: 1 finding, up to low
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- low Privilege Escalation · line 192 Skill requests more permissions than appear necessary for its stated functionality. Review if elevated access is justified.Fix: Request only the minimum permissions required. Document why each permission is needed. Remove broad permissions like '*' or 'all'.
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 | $0.00080 | $0.05689 |
| Opus 5 | $0.00040 | $0.02844 |
| Sonnet 5 | $0.00016 | $0.01138 |
| Haiku 4.5 | $0.00008 | $0.00569 |
Grade A, and why
taxonomy 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 6d 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 — 463 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Taxonomy Generation & Data Auditing
When to Use
- User asks to create or review a tracking plan or event taxonomy
- User wants to validate event/property naming conventions
- User needs to audit data quality (duplicates, stale events, missing metadata)
- User asks about funnel design or event relationships
- Agent is generating event names or property names and needs to follow standards
- User wants to understand or improve their taxonomy governance
- User asks about reducing event volume or type counts
- User asks about deprecation, blocking, deleting, or hiding events
- Any agent needs a "source of truth" for taxonomy best practices before recommending events
- User asks about AI readiness, AI Controls, or improving AI feature accuracy
Layer 1: Foundational Concepts
Core Philosophy
Six principles govern all taxonomy work:
- Evidence-first. Never fabricate. Every finding must be grounded in tool-retrieved data. If something cannot be verified, say so explicitly.
- Scan aggressively. Propose confidently. Confirm before writing. Paginate autonomously through the full taxonomy. Form a prioritized, opinionated view of what needs fixing — then present it. Never call a write tool without explicit user confirmation.
- Be opinionated, not neutral. Generic requests ("audit my taxonomy") are an invitation to lead. Use the scoring framework, recommend the highest-impact action first, and explain why. Don't present a menu of equal options.
- Surface critical issues proactively. If you find something important while working on an adjacent task, raise it. Don't silently ignore a PII violation because the user only asked about naming conventions.
- Questions extract institutional knowledge. Ask about business intent and real-world meaning, not Amplitude mechanics. One focused question at a time. The goal is to surface knowledge that lives in people's heads.
- Explain before acting. Before calling any write tool, present exact proposed changes — including before/after state — and wait for explicit confirmation.
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.
- 6d ago Changed · -50 lines 7d138d67f2d6
- 11d ago First seen · 513 lines · 80 tokens per session scan A a832d2fd527f
taxonomy is a skill published in the GitHub repository amplitude/mcp-marketplace (35 stars, last pushed 2d ago), licensed MIT. It adds 80 tokens to every session and 5,689 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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