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 G1Joshi/Agent-Skills --skill pandasgit clone --depth 1 https://github.com/G1Joshi/Agent-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/g1joshi/agent-skills/pandas)<a href="https://agentmods.dev/skills/g1joshi/agent-skills/pandas"><img src="https://agentmods.dev/badge/skills/g1joshi/agent-skills/pandas/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/g1joshi/agent-skills/pandas"><img src="https://agentmods.dev/badge/skills/g1joshi/agent-skills/pandas.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 | $0.00016 | $0.00302 |
| Opus 5 | $0.00008 | $0.00151 |
| Sonnet 5 | $0.00003 | $0.00060 |
| Haiku 4.5 | $0.00002 | $0.00030 |
Grade A, and why
pandas 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 10d 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
Pandas
Pandas is the Excel of Python. v3.0 (2025/2026) enforces Copy-on-Write (CoW), finally fixing the SettingWithCopyWarning confusion.
When to Use
- Data Cleaning: Loading CSV/Excel/SQL and cleaning it.
- Time Series: Unmatched datetime indexing capabilities.
- Small/Medium Data: Features that fit in RAM.
Core Concepts
DataFrame / Series
2D tables and 1D arrays.
Copy-on-Write (CoW)
Views are always views, copies are always copies. Modifying a view triggers a copy only if necessary.
PyArrow Backend
Using Arrow memory format for speed and string handling (dtype="string[pyarrow]").
Best Practices (2025)
Do:
- Use PyArrow Strings:
pd.options.future.infer_string = True(Default in 3.0). - Use
.query(): For cleaner filtering syntax. - Migrate to CoW: Ensure your code doesn't rely on side-effects of views.
Don't:
- Don't iterate rows: Use vectorization (
df['a'] + df['b']).
References
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.
- 10d ago First seen · 45 lines · 16 tokens per session scan A 86163d97b32d
pandas is a skill published in the GitHub repository G1Joshi/Agent-Skills (12 stars, last pushed 7mo ago), licensed MIT. It adds 16 tokens to every session and 302 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 2026-08-30.
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