lanej/looker
claude/skills/looker/SKILL.md
Semantic search over EasyPost's Looker BI instance via MCP tools (mcp__looker__*). Use when: (1) finding what data exists in Looker ('what dashboard shows carrier revenue?', 'which explore has claims data?'); (2) retrieving BigQuery SQL from existing Looker queries to inspect or reuse; (3) discovering available fields/dimensions/measures for an explore; (4) rebuilding or refreshing the Looker index after the instance changes; (5) fetching a specific saved Look by ID.
$ install --global
skillsauthnpx skillsauth add lanej/dotfiles lookerInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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TrivyContainer and dependency vulnerability scanner
95%
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SemgrepStatic code analysis for vulnerabilities
95%
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mcp-scan (Snyk)Model Context Protocol security validation
95%
Skipped
Snyk (dep)Open source security scanning
50%
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Socket.devSupply chain security analysis
50%
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VirusTotalMulti-engine malware detection
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OWASP Dep-Check
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Last scanned: May 26, 2026, 7:39 AM78.0s1 file scanned
SKILL.md
- name:
- looker
- description:
- Semantic search over EasyPost's Looker BI instance via MCP tools (mcp__looker__*). Use when: (1) finding what data exists in Looker ('what dashboard shows carrier revenue?', 'which explore has claims data?'); (2) retrieving BigQuery SQL from existing Looker queries to inspect or reuse; (3) discovering available fields/dimensions/measures for an explore; (4) rebuilding or refreshing the Looker index after the instance changes; (5) fetching a specific saved Look by ID.
Looker MCP Tool Guide
Semantic search over EasyPost's Looker instance. Search is backed by the local qmd index — no credentials needed. Catalog/auth tools require Looker API credentials (env vars).
Primary Tool: search_all
Use by default. Searches queries, explores, and dashboards in one call.
mcp__looker__search_all(query, limit?, catalog_dir?)
Returns { queries[], explores[], dashboards[] } — each ranked by cosine similarity.
Score interpretation: ≥ 0.65 = strong match. 0.50–0.65 = related but weaker. < 0.50 = likely noise.
Individual Search Tools
Use when you only need one result type:
mcp__looker__search_queries — Returns full generated BigQuery SQL per query. Use when you need actual SQL to inspect or run.
- Result fields:
score,query_id,explore,model,dashboard_titles[],element_titles[],fields[],sources[],generated_sql
mcp__looker__search_explores — Aggregated view per explore. Use for "which explore covers X?" questions.
- Result fields:
score,explore,domain,model,n_queries,bq_tables[]
mcp__looker__search_dashboards — Search by dashboard title and panel content.
- Result fields:
score,dashboard_id,dashboard_title,n_elements,explores[]
mcp__looker__search_looks — Search saved Looks by title, explore, and fields.
- Result fields:
score,look_id,title,explore,bq_tables[]
All search tools accept: query (required), limit (default 5, max 20), catalog_dir (default ~/.looker/catalog).
Fetch a Look by ID
mcp__looker__get_look(id) — Fetch a saved Look by numeric ID (from the Looker URL: /looks/57 → 57). Returns title, folder, query_id, and URL. Requires credentials.
Admin Tools (Credentials Required)
Run in sequence when the index is missing (tool says "No Looker index found") or after the Looker instance has changed (new explores, dashboards added):
mcp__looker__verify_auth— Confirm API connectivity before crawling.mcp__looker__catalog— Crawl the instance; writes JSONL to~/.looker/catalog/. Incremental (checkpoint-aware).- Key params:
folder_id(scope crawl),workers(default 4),out_dir(default~/.looker/catalog)
- Key params:
mcp__looker__enrich— Add BQ table lineage, domain classification, and per-explore markdown for qmd indexing. Annotates with Claude Haiku via Vertex AI.- Defaults:
out_dir→~/workspace/resources/looker-queries/,dict_dir→~/workspace/resources/bigquery/domains/,cache_dir→~/.looker/annotation_cache - Use
no_annotate: trueto skip Claude annotation.
- Defaults:
mcp__looker__index_build— Runsqmd updateto refresh the search index. No credentials needed.
Usage Patterns
"What dashboard shows carrier revenue?"
→ search_all("carrier revenue") → check dashboards + queries
"Find the SQL for shipment volume by state"
→ search_queries("shipment volume by state") → read generated_sql
"Which explore covers claims data?"
→ search_explores("claims") → check bq_tables and domain
"I need to build a query about X — what fields exist?"
→ search_queries for similar queries → inspect fields[] to see available dimensions/measures
Search returns "No Looker results — run looker enrich and qmd update first" error
→ Catalog exists but embeddings are missing. Check the index_build log for "N unique hashes need vectors". If non-zero, run qmd embed via Bash before searching. Full sequence for a stale index: verify_auth → catalog → enrich → index_build → qmd embed (if "N unique hashes need vectors").
Search returns "No Looker index found" error
→ Index doesn't exist. Run: verify_auth → catalog → enrich → index_build → qmd embed
Search returns no results or low scores → The query didn't match. Rephrase or try a different search term. Do NOT assume the index is stale — the tool now distinguishes "missing index" from "no match" explicitly.
Storage Paths
~/.looker/catalog/ # JSONL from catalog crawl
~/.looker/catalog/queries.jsonl
~/.looker/catalog/checkpoint.json
~/.looker/annotation_cache/ # Claude Haiku annotation cache
~/workspace/resources/looker-queries/ # Per-explore markdown (qmd-indexed)
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