metrics

metrics is a skill for Claude Code from ai-analyst-lab/ai-analyst-plugin. It costs 94 tokens per session (1,229 once invoked), scanned A, original, MIT.

A skill for looking up metric definitions in the active dataset's metric dictionary. A metric is a defined way of measuring something, such as conversion or retention.

In plain words
What is it for?
Use it to list metrics, search for a metric, view its full definition, or check how it is calculated before analyzing data.
Why use it?
It prevents analysis from using an undefined or inconsistent meaning for a metric.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the ai-analyst-plus plugin — 44 skills, 1 command, 13 agents shipped together

Good fit Use it to list metrics, search for a metric, view its full definition, or check how it is calculated before analyzing data.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/ai-analyst-lab/ai-analyst-plugin/metrics
Install

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.

Any agent
npx skills add ai-analyst-lab/ai-analyst-plugin --skill metrics
Clone the repo
git clone --depth 1 https://github.com/ai-analyst-lab/ai-analyst-plugin

Made for: Claude Code.

Or install ai-analyst-plus, the plugin that ships this one along with the rest of its 44 skills, 1 command, 13 agents.

Wrote 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.

agentmods badge for metrics

README.md
[![agentmods](https://agentmods.dev/badge/skills/ai-analyst-lab/ai-analyst-plugin/metrics.svg)](https://agentmods.dev/skills/ai-analyst-lab/ai-analyst-plugin/metrics)
Your own site
<a href="https://agentmods.dev/skills/ai-analyst-lab/ai-analyst-plugin/metrics"><img src="https://agentmods.dev/badge/skills/ai-analyst-lab/ai-analyst-plugin/metrics.svg" alt="Measured on agentmods" height="20"></a>
Per session 94 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,229 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00094 $0.01229
Opus 5 $0.00047 $0.00615
Sonnet 5 $0.00019 $0.00246
Haiku 4.5 $0.00009 $0.00123

Measured 8d ago against content hash 69d75ebfa53e, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

metrics 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 8d 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.

ai-analyst-plus/skills/metrics/SKILL.md · 98 lines

How it starts

The opening of the file, as written. The whole thing — 98 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Skill: Metrics

Purpose

Browse, search, and display metric definitions from the active dataset's metric dictionary. Provides quick access to how metrics are defined, computed, and validated.

When to Use

  • User says /metrics or "show me the metrics" or "what metrics do we track?"
  • During analysis, to confirm a metric's definition before computing it
  • When writing a metric spec, to check for existing definitions

Deference rule: metric-shaped meaning questions ("what does ARR mean here?", "how is churn defined?") come here first; if the metric is not in the dictionary and the term looks organizational (a product, team, or general glossary term), hand off to the business skill before suggesting metric-spec.

Invocation

/metrics — list all metrics for the active dataset /metrics {id} — show full spec for a specific metric /metrics category={cat} — filter by category (e.g., monetization) /metrics search={term} — search metric names and descriptions

Instructions

Step 1: Load Metric Dictionary

  1. Read .knowledge/active.yaml to identify the active dataset.
  2. Read .knowledge/datasets/{active}/metrics/index.yaml for the metric list.
  3. If no metrics directory exists: "No metric dictionary for this dataset. Use the metric-spec skill to define metrics."

Step 2: Execute Command

List all (/metrics):

  • Display as a table: id, name, category, direction, validation_status
  • Group by category
  • Show total count
  • If dictionary is empty AND user mentions specific analysis context (e.g., "revenue analysis", "conversion analysis"):
    • Read .knowledge/datasets/{active}/schema.md to explore available tables/columns
    • Suggest 3-5 relevant metrics the user could define for their analysis context
    • Include suggested SQL formulas for each
    • Reference the metric-spec skill for formalization

Show specific (/metrics {id}):

  • Read .knowledge/datasets/{active}/metrics/{id}.yaml
  • Display: name, category, owner, full definition (formula, unit, direction, granularity), source tables, dimensions, guardrails, typical range, validation status
  • If metric not found:
    • Suggest closest match from index (fuzzy string match on name)
    • Fallback search strategy: Search working/, outputs/, and .knowledge/analyses/ for recent usage of the metric name
    • If found in recent work, extract the formula/definition used and offer to formalize it
    • If not found anywhere, suggest defining it via metric-spec skill

Read the full file on GitHub · 98 lines

Changes

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.

  1. 8d ago First seen · 98 lines · 94 tokens per session scan A 69d75ebfa53e

Subscribe to this mod's changes

metrics is a skill published in the GitHub repository ai-analyst-lab/ai-analyst-plugin (32 stars, last pushed 12d ago), licensed MIT. It adds 94 tokens to every session and 1,229 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.

Related

Other skills, from other repositories

systematic-debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.

obra/superpowers · 21 tokens

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…

microsoft/ai-agents-for-beginners · 200 tokens

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…

vercel/next.js · 95 tokens

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…

vercel/next.js · 170 tokens

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…

vercel/next.js · 103 tokens

chronicle

Analyze Copilot session history for standup reports, usage tips, session search, and session reindexing. Use when the user asks for a standup, daily summary, usage tips, workflow recommendations, wants to search or find past sessions by keyword/file/PR, wants to reindex their session store, or asks about deleting…

microsoft/vscode · 72 tokens