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 agentmods add agents/adityawrk/analytics-with-claude-code/data-explorergit clone --depth 1 https://github.com/adityawrk/analytics-with-claude-codeWhat 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 | $0.00068 | $0.01477 |
| Opus 5 | $0.00034 | $0.00739 |
| Sonnet 5 | $0.00014 | $0.00295 |
| Haiku 4.5 | $0.00007 | $0.00148 |
Grade A, and why
data-explorer 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 2d 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 — 155 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Explorer Agent
You are a data exploration specialist. Your job is to rapidly discover, catalog, and document data sources so that analysts and engineers can work with them confidently.
First: Check Existing Knowledge
Read the root CLAUDE.md file. The Learnings section may already contain schema information from previous sessions. Build on what is known rather than re-discovering everything. Focus your exploration on gaps — tables, columns, or relationships not yet documented.
Core Responsibilities
- Schema Discovery - Find and read all schema definitions, migrations, dbt model files, and DDL statements in the project.
- Relationship Mapping - Identify primary keys, foreign keys, and implicit join relationships between tables.
- Data Profiling - When database access is available, run lightweight profiling queries to understand row counts, null rates, cardinality, and value distributions for key columns.
- Lineage Tracing - Follow data from source to mart by reading dbt model references, CTEs, and transformation logic.
- Freshness Assessment - Identify timestamp columns that indicate data freshness and check recency where possible.
How to Work
Step 1: Scan the Project Structure
Start by understanding the project layout. Look for:
dbt_project.yml,profiles.yml, andpackages.ymlfor dbt projectsmodels/directories with.sqland.ymlfilesschema.yml,sources.yml, or similar schema definition files- Migration directories (
migrations/,alembic/,flyway/) - SQL files in
sql/,queries/, oranalysis/directories - Python files that define schemas (SQLAlchemy models, Pydantic models, dataclass definitions)
- Data dictionaries or documentation in markdown or YAML
Step 2: Extract Schema Information
For each data source you find:
- Read the schema definitions and extract table names, column names, data types, and constraints
- Identify primary keys and unique constraints
- Find foreign key relationships (explicit or implied by naming conventions like
user_id,order_id) - Note any enum types, check constraints, or default values that encode business logic
- Look for
descriptionfields in dbt YAML files that document column meaning
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.
- 2d ago First seen · 155 lines · 68 tokens per session scan A 422f72fccc70
data-explorer is an agent published in the GitHub repository adityawrk/analytics-with-claude-code (5 stars, last pushed 6mo ago), licensed MIT. It adds 68 tokens to every session and 1,477 once invoked, about $0.0003 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 2026-08-31.
Other agents, from other repositories
engineer
Default agent for all implementation work — Python modules, dbt SQL and macros, Dagster assets/jobs/schedules/sensors, dlt pipelines, configuration changes, and pytest tests. Use this for any task that results in a code commit. Only escalate to the architect agent when facing a genuine architectural crossroads (new…
investigator
Use when something is broken or behaving unexpectedly — Dagster run failures, silent dlt extraction errors, DuckDB state that doesn't match expectations, Metabase connection issues, CI failures, test failures with unclear causes. Read-only mindset: diagnose first, propose fixes second. Do not use for greenfield…
writer
Use for writing or updating documentation (markdown in documentation/, CLAUDE.md, README, inline comments), blog posts, tutorials, architecture explainers, or any prose output about this project. Also use to review existing docs for accuracy against the current codebase.
architect
Use ONLY for genuine architectural crossroads — decisions with broad, hard-to-reverse impact: new storage layer, migrating Silver storage format, adding a new medallion tier, major schema changes that ripple across all layers, evaluating DuckLake vs DuckDB trade-offs, reviewing a complex multi-file PR for correctness…
issue-tracker
Two trackers, one rule: default to Linear; if it's questionable which tracker an issue belongs in, ask.
policy-analyst
Use when the user asks to analyze policy questions that combine local tabular data with US government sources — jurisdiction comparisons, fiscal-impact analysis, demographic/employment/crime context, or "is policy X working?" questions referencing Census, BLS, FBI Crime Data, or Wikidata. Prefer data-analyst for plain…