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 skills/franklee16/academic-research-skills/python-panel-datanpx skills add franklee16/academic-research-skills --skill python-panel-datagit clone --depth 1 https://github.com/franklee16/academic-research-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/franklee16/academic-research-skills/python-panel-data)<a href="https://agentmods.dev/skills/franklee16/academic-research-skills/python-panel-data"><img src="https://agentmods.dev/badge/skills/franklee16/academic-research-skills/python-panel-data.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 | $0.00017 | $0.00769 |
| Opus 5 | $0.00009 | $0.00385 |
| Sonnet 5 | $0.00003 | $0.00154 |
| Haiku 4.5 | $0.00002 | $0.00077 |
Grade A, and why
python-panel-data 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 3d 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.
The source is not reproduced here
No licence file
A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.
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.
- 3d ago First seen · 136 lines · 17 tokens per session scan A 2e84f3ab3bb4
python-panel-data is a skill published in the GitHub repository franklee16/academic-research-skills (213 stars, last pushed 4mo ago), with no licence file. It adds 17 tokens to every session and 769 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.
Other skills, from other repositories
python-panel-data
Panel data analysis with Python using linearmodels and pandas.
python-panel-data
Panel data analysis with Python using linearmodels and pandas.
geopandas
Use when performing vector spatial data analysis in Python — reading/writing shapefiles, spatial joins, overlay operations, choropleth maps. GeoPandas: extends pandas DataFrames with geometry columns for Pythonic spatial analysis.
panel-data
Panel data econometrics with Python linearmodels; covers pooled OLS, fixed/random effects, Hausman test, clustered SE, Arellano-Bond GMM, and regression tables.
dali-dynamic-mode
DALI imperative dynamic mode (nvidia.dali.experimental.dynamic, ndd): use when working on ndd code or migrating pipelines; skip pipeline-only tasks.
datachain-core
Use ONLY for abstract DataChain SDK questions — API usage, method signatures, or code patterns — when no specific dataset or bucket is referenced. If the request mentions creating, saving, listing, exploring datasets or buckets, use datachain-knowledge instead.