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 eric861129/SKILLS_All-in-one --skill pandas-progit clone --depth 1 https://github.com/eric861129/SKILLS_All-in-oneWrote 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/eric861129/skills_all-in-one/pandas-pro)<a href="https://agentmods.dev/skills/eric861129/skills_all-in-one/pandas-pro"><img src="https://agentmods.dev/badge/skills/eric861129/skills_all-in-one/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/eric861129/skills_all-in-one/pandas-pro"><img src="https://agentmods.dev/badge/skills/eric861129/skills_all-in-one/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 | $0.00087 | $0.01423 |
| Opus 5 | $0.00044 | $0.00711 |
| Sonnet 5 | $0.00017 | $0.00285 |
| Haiku 4.5 | $0.00009 | $0.00142 |
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 7d 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.
This is a copy
95% identical to pandas-pro — 2 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 179 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.
- 7d ago First seen · 179 lines · 87 tokens per session scan A 528518b4cddf
pandas-pro is a skill published in the GitHub repository eric861129/SKILLS_All-in-one (52 stars, last pushed 4mo ago), licensed MIT. It adds 87 tokens to every session and 1,423 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 95% identical to pandas-pro, differing in 2 lines, and is treated as a copy.
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