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/0xfurai/claude-code-subagents/pandas-expertgit clone --depth 1 https://github.com/0xfurai/claude-code-subagentsWrote 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.
[](https://agentmods.dev/agents/0xfurai/claude-code-subagents/pandas-expert)<a href="https://agentmods.dev/agents/0xfurai/claude-code-subagents/pandas-expert"><img src="https://agentmods.dev/badge/agents/0xfurai/claude-code-subagents/pandas-expert.svg" alt="Measured on agentmods" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00017 | $0.00383 |
| Opus 5 | $0.00009 | $0.00192 |
| Sonnet 5 | $0.00003 | $0.00077 |
| Haiku 4.5 | $0.00002 | $0.00038 |
Grade A, and why
pandas-expert 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.
What it actually says
Focus Areas
- DataFrame creation and manipulation
- Series operations and transformations
- Indexing and selecting data
- Grouping and aggregating data
- Merging, joining, and concatenating DataFrames
- Handling missing data effectively
- Applying functions across DataFrames
- Data input/output with various formats
- Time series analysis capabilities
- Conditional selection and filtering
Approach
- Utilize vectorized operations for efficiency
- Keep data types consistent and optimized
- Use chaining methods for readability
- Leverage
apply()andmap()for custom transformations - Maintain DataFrame index integrity
- Optimize memory usage with data type adjustments
- Employ
query()for complex filtering - Document code with concise comments
- Use
pandasbuilt-in plotting for quick visual insights - Always use version-controlled scripts for replicability
Quality Checklist
- Ensure no operations alter original data unintentionally
- Validate DataFrames' shapes after operations
- Check for the presence of missing values post-transformation
- Confirm data types after manipulations
- Efficient use of memory and processing resources
- Correct index alignment post-merges/joins
- Consistent naming conventions for clarity
- Proper testing of data input/output processes
- Ensure accurate grouping and aggregation results
- Verify performance with sample datasets
Output
- Clean, well-structured DataFrames ready for analysis
- Efficient data manipulation scripts
- Comprehensive summary statistics
- Clear and interpretable data visualizations
- Accurate time series forecasts and analysis
- Flexible data processing pipelines
- Documented notebooks and scripts for reproducibility
- Performant data transformation functions
- Effective missing data strategies implemented
- Insightful exploratory data analysis results
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 · 59 lines · 17 tokens per session scan A da9fe2fe9b0d
pandas-expert is an agent published in the GitHub repository 0xfurai/claude-code-subagents (996 stars, last pushed 10mo ago), licensed MIT. It adds 17 tokens to every session and 383 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 2026-09-03.
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