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 commands/fergupa/claude_plugins/explore-datagit clone --depth 1 https://github.com/fergupa/claude_pluginsWhat 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 | $0.00014 | $0.01017 |
| Opus 5 | $0.00007 | $0.00508 |
| Sonnet 5 | $0.00003 | $0.00203 |
| Haiku 4.5 | $0.00001 | $0.00102 |
Grade A, and why
explore-data 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 yesterday.
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 — 116 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/explore-data - Profile and Explore a Dataset
If you see unfamiliar placeholders or need to check which tools are connected, see CONNECTORS.md.
Generate a comprehensive data profile for a table or uploaded file. Understand its shape, quality, and patterns before diving into analysis.
Usage
/explore-data <table_name or file>
Workflow
1. Access the Data
If a data warehouse MCP server is connected:
- Resolve the table name (handle schema prefixes, suggest matches if ambiguous)
- Query table metadata: column names, types, descriptions if available
- Run profiling queries against the live data
If a file is provided (CSV, Excel, Parquet, JSON):
- Read the file and load into a working dataset
- Infer column types from the data
If neither:
- Ask the user to provide a table name (with their warehouse connected) or upload a file
- If they describe a table schema, provide guidance on what profiling queries to run
2. Generate Data Profile
Run the following profiling checks:
Table-level metrics:
- Total row count
- Column count and types breakdown
- Approximate table size (if available from metadata)
- Date range coverage (min/max of date columns)
Column-level metrics for each column:
- Data type (and whether it matches expected type)
- Null count and null rate (%)
- Distinct count and cardinality (distinct / total)
- For numeric columns: min, max, mean, median, stddev, percentiles (p25, p50, p75, p95, p99)
- For string columns: min/max length, most common values (top 10), empty string count
- For date/timestamp columns: min, max, distribution by time period
- For boolean columns: true/false/null distribution
Present the profile as a clean summary table, grouped by column type (dimensions, metrics, dates, IDs).
3. Identify Data Quality Issues
Flag potential problems:
- High null rates: Columns with >5% nulls (warn), >20% nulls (alert)
- Low cardinality surprises: Columns that should be high-cardinality but aren't (e.g., a "user_id" with only 50 distinct values)
- High cardinality surprises: Columns that should be categorical but have too many distinct values
- Suspicious values: Negative amounts where only positive expected, future dates in historical data, obviously placeholder values (e.g., "N/A", "TBD", "test", "999999")
- Duplicate detection: Check if there's a natural key and whether it has duplicates
- Distribution skew: Extremely skewed numeric distributions that could affect averages
- Encoding issues: Mixed case in categorical fields, trailing whitespace, inconsistent formats
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.
- yesterday First seen · 116 lines · 14 tokens per session scan A 8a3b5f17d07e
explore-data is a command published in the GitHub repository fergupa/claude_plugins (2 stars, last pushed 6mo ago), licensed Apache-2.0. It adds 14 tokens to every session and 1,017 once invoked, about $0.0001 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 commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.