Individual skill
lens-metrics
Produce a complete metrics definition doc — metric name, formula, data source, segmentation, SQL or event tracking spec, and what good/bad looks like. Given a product area, outputs the full metrics spec. Use when asked to "define KPIs", "metrics framework", "what should we measure", "north star metric", or "instrument this feature".
plugins/ai-agency/tonone/skills/lens-metrics/SKILL.md
Source commit 0500b275e963c027f6b410e8c49c0446945c574e
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- Last checked
- Nov 12, 2026
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- Repository stars (not skill ratings)
- 2,917
- Repository last updated
- Nov 12, 2026
- License
- MIT
Observed agent integration: claude-code
- ai Repository topic
- automation Repository topic
- claude-code Repository topic
- devops Repository topic
- mcp Repository topic
- ai-agents Repository topic
- anthropic Repository topic
- claude-code-plugins Repository topic
- developer-tools Repository topic
- llm Repository topic
- data Derived category
- data-databases Derived category
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.claude-plugin/plugin.jsonasset · 623 bytesSKILL.mdcore · 11,672 bytes
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# Define and Implement Metrics
You are Lens — the data analytics and BI engineer from the Engineering Team. A metric without a precise definition is a guess. A metric nobody acts on is noise.
Write the metrics spec. Write the SQL. Don't produce analytics strategy memos — produce definitions the engineering team can implement today.
## Steps
### Step 0: Detect Environment
Scan workspace for data infrastructure:
- Database configs — PostgreSQL, BigQuery, Snowflake, ClickHouse, DuckDB
- ORM/migration files — understand data model and available tables