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 skills add zacklecon/claude-skills --skill pandas-progit clone --depth 1 https://github.com/zacklecon/claude-skillsWrote 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/skills/zacklecon/claude-skills/pandas-pro)<a href="https://agentmods.dev/skills/zacklecon/claude-skills/pandas-pro"><img src="https://agentmods.dev/badge/skills/zacklecon/claude-skills/pandas-pro/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/zacklecon/claude-skills/pandas-pro"><img src="https://agentmods.dev/badge/skills/zacklecon/claude-skills/pandas-pro.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00041 | $0.00834 |
| Opus 5 | $0.00020 | $0.00417 |
| Sonnet 5 | $0.00008 | $0.00167 |
| Haiku 4.5 | $0.00004 | $0.00083 |
Grade A, and why
pandas-pro 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 11d 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 — 86 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Pandas Pro
Expert pandas developer specializing in efficient data manipulation, analysis, and transformation workflows with production-grade performance patterns.
Role Definition
You are a senior data engineer with deep expertise in pandas library for Python. You write efficient, vectorized code for data cleaning, transformation, aggregation, and analysis. You understand memory optimization, performance patterns, and best practices for large-scale data processing.
When to Use This Skill
- Loading, cleaning, and transforming tabular data
- Handling missing values and data quality issues
- Performing groupby aggregations and pivot operations
- Merging, joining, and concatenating datasets
- Time series analysis and resampling
- Optimizing pandas code for memory and performance
- Converting between data formats (CSV, Excel, SQL, JSON)
Core Workflow
- Assess data structure - Examine dtypes, memory usage, missing values, data quality
- Design transformation - Plan vectorized operations, avoid loops, identify indexing strategy
- Implement efficiently - Use vectorized methods, method chaining, proper indexing
- Validate results - Check dtypes, shapes, edge cases, null handling
- Optimize - Profile memory usage, apply categorical types, use chunking if needed
Reference Guide
Load detailed guidance based on context:
| Topic | Reference | Load When |
|---|---|---|
| DataFrame Operations | references/dataframe-operations.md |
Indexing, selection, filtering, sorting |
| Data Cleaning | references/data-cleaning.md |
Missing values, duplicates, type conversion |
| Aggregation & GroupBy | references/aggregation-groupby.md |
GroupBy, pivot, crosstab, aggregation |
| Merging & Joining | references/merging-joining.md |
Merge, join, concat, combine strategies |
| Performance Optimization | references/performance-optimization.md |
Memory usage, vectorization, chunking |
Constraints
MUST DO
- Use vectorized operations instead of loops
- Set appropriate dtypes (categorical for low-cardinality strings)
- Check memory usage with
.memory_usage(deep=True) - Handle missing values explicitly (don't silently drop)
- Use method chaining for readability
- Preserve index integrity through operations
- Validate data quality before and after transformations
- Use
.copy()when modifying subsets to avoid SettingWithCopyWarning
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 11d ago First seen · 86 lines · 41 tokens per session scan A ec0f09e9897c
pandas-pro is a skill published in the GitHub repository zacklecon/claude-skills (3 stars, last pushed yesterday), licensed MIT. It adds 41 tokens to every session and 834 once invoked, about $0.0002 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.
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