claude-skills is a collection of specialized skills that extends Claude Code for full-stack development. Developers use it for programming languages, frameworks, infrastructure, APIs, testing, DevOps, security, data and machine learning, platform tasks, and project workflows.
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 Jeffallan/claude-skills --skill pandas-progit clone --depth 1 https://github.com/Jeffallan/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/jeffallan/claude-skills/pandas-pro)<a href="https://agentmods.dev/skills/jeffallan/claude-skills/pandas-pro"><img src="https://agentmods.dev/badge/skills/jeffallan/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/jeffallan/claude-skills/pandas-pro"><img src="https://agentmods.dev/badge/skills/jeffallan/claude-skills/pandas-pro.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector pass
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 | $0.00087 | $0.01447 |
| Opus 5 | $0.00044 | $0.00724 |
| Sonnet 5 | $0.00017 | $0.00289 |
| Haiku 4.5 | $0.00009 | $0.00145 |
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.
Copies of this mod
1 near-identical copy found in the catalogue:
- pandas-pro — 95% identical, 2 lines differ
How it starts
The opening of the file, as written. The whole thing — 181 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.
Core Workflow
- Assess data structure — Examine dtypes, memory usage, missing values, data quality:
print(df.dtypes) print(df.memory_usage(deep=True).sum() / 1e6, "MB") print(df.isna().sum()) print(df.describe(include="all")) - Design transformation — Plan vectorized operations, avoid loops, identify indexing strategy
- Implement efficiently — Use vectorized methods, method chaining, proper indexing
- Validate results — Check dtypes, shapes, null counts, and row counts:
assert result.shape[0] == expected_rows, f"Row count mismatch: {result.shape[0]}" assert result.isna().sum().sum() == 0, "Unexpected nulls after transform" assert set(result.columns) == expected_cols - Optimize — Profile memory, 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 |
Code Patterns
Vectorized Operations (before/after)
# ❌ AVOID: row-by-row iteration
for i, row in df.iterrows():
df.at[i, 'tax'] = row['price'] * 0.2
# ✅ USE: vectorized assignment
df['tax'] = df['price'] * 0.2
Safe Subsetting with .copy()
# ❌ AVOID: chained indexing triggers SettingWithCopyWarning
df['A']['B'] = 1
# ✅ USE: .loc[] with explicit copy when mutating a subset
subset = df.loc[df['status'] == 'active', :].copy()
subset['score'] = subset['score'].fillna(0)
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 · 181 lines · 87 tokens per session scan A 4c026295382b
pandas-pro is a skill published in the GitHub repository Jeffallan/claude-skills (11,401 stars, last pushed 1mo ago), licensed MIT. It adds 87 tokens to every session and 1,447 once invoked, about $0.0004 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 2026-08-30.
Other skills, from other repositories
ruff-bump
Use when upgrading Ruff in one repository or a fleet and evaluating newly applicable rules before committing each change.
pytest-optimizer-00-scan
Use when starting or re-baselining a pytest optimization pass by profiling tests and fixtures without editing the suite.
astropy
Core Python library for astronomy and astrophysics workflows that need Astropy APIs, including units/quantities, coordinates, FITS I/O, tables, time systems, WCS, and cosmology. Use when implementing or debugging astronomical data analysis code with Astropy.
cobrapy
Constraint-based metabolic modeling (COBRA). FBA, FVA, gene knockouts, flux sampling, SBML models, for systems biology and metabolic engineering analysis.
dask
Distributed computing for larger-than-RAM pandas/NumPy workflows. Use when you need to scale existing pandas/NumPy code beyond memory or across clusters. Best for parallel file processing, distributed ML, integration with existing pandas code. For out-of-core analytics on single machine use vaex; for in-memory speed…
matlab
Build, review, migrate, and safely plan MATLAB or GNU Octave numerical workflows, including arrays, tabular/time data, tests, projects, graphics, MAT files, and explicit Python interoperability.