Scientific Agent Skills is a collection of reusable procedures that give AI agents capabilities for scientific research across areas such as biology, chemistry, medicine, and drug discovery. It is used by researchers and by people building AI scientist workflows with compatible coding agents. The catalogue contains many of the project's skills and supporting instructions.
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 K-Dense-AI/scientific-agent-skills --skill geopandasgit clone --depth 1 https://github.com/K-Dense-AI/scientific-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/k-dense-ai/scientific-agent-skills/geopandas)<a href="https://agentmods.dev/skills/k-dense-ai/scientific-agent-skills/geopandas"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/geopandas/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/k-dense-ai/scientific-agent-skills/geopandas"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/geopandas.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 | $0.00035 | $0.03329 |
| Opus 5 | $0.00017 | $0.01665 |
| Sonnet 5 | $0.00007 | $0.00666 |
| Haiku 4.5 | $0.00003 | $0.00333 |
Grade A, and why
geopandas 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 9d 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:
- geopandas — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 268 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GeoPandas
Use GeoPandas for planar vector data represented as pandas-like GeoSeries and
GeoDataFrame objects. This skill targets stable GeoPandas 1.1.4 (released
2026-06-26), not the unreleased 1.2 documentation.
Reproducible environment
GeoPandas 1.1.4 requires Python 3.10+; its tagged source requires NumPy >=1.24,
pandas >=2.0, Shapely >=2.0, pyproj >=3.5, pyogrio >=0.7.2, and packaging.
This exact Python 3.12 snapshot was smoke-tested on 2026-07-23:
uv venv --python 3.12
uv pip install \
"geopandas==1.1.4" \
"numpy==2.5.1" \
"pandas==3.0.5" \
"shapely==2.1.2" \
"pyproj==3.7.2" \
"pyogrio==0.13.0" \
"pyarrow==25.0.0" \
"packaging==26.2"
Keep optional plotting and PostGIS packages pinned in the project lock as well. Do not mix binary geospatial packages from incompatible package channels.
Safety and privacy contract
- Treat exact coordinates, addresses, parcel boundaries, trajectories, and small-area joins as sensitive. Default reports to counts, categories, coarse extents, and redacted identifiers. Generalize before publication.
- Never automatically load a URL, cloud URI, GDAL
/vsi*path, archive, or geocode an address. Obtain explicit approval, validate provenance and hashes, then stage an unpacked local file in an isolated workspace. - GDAL/OGR drivers, GEOS, PROJ, pyogrio, Shapely, pyproj, and their wheels are a native-code trust boundary. Prefer official wheels/conda-forge, record native versions, restrict drivers, and process untrusted data in a sandbox.
- Do not open macro-enabled office files or nested archives through permissive GDAL drivers. The bundled CLIs use an extension allowlist and reject archives.
- Read only named database secrets such as
GEOPANDAS_POSTGIS_PASSWORD; use a secret manager or scoped environment variable. Never embed a password in a URL or source, print an engine/URL, or dump the environment. - Every derived artifact needs source hashes/versions, CRS, operation parameters, predicate, join cardinality, precision/repair choices, and row-count checks.
What ships with it
13 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.
- references/crs-management.md 8.7 KB
- references/data-io.md 12 KB
- references/data-structures.md 7.4 KB
- references/geometric-operations.md 9.5 KB
- references/spatial-analysis.md 9.6 KB
- references/visualization.md 8.5 KB
- scripts/_common.py 21 KB runs code
- scripts/crs_reprojection_plan.py 7.2 KB runs code
- scripts/export_plan.py 11 KB runs code
- scripts/geometry_validity_report.py 7.3 KB runs code
- scripts/sensitive_coordinates_checklist.py 8.8 KB runs code
- scripts/spatial_join_audit.py 13 KB runs code
- scripts/vector_inventory.py 4.4 KB runs code
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.
- 9d ago Changed · +17 lines 98450a46bf64
- 13d ago First seen · 251 lines · 35 tokens per session scan A 2293810fa81a
geopandas is a skill published in the GitHub repository K-Dense-AI/scientific-agent-skills (44,469 stars, last pushed today), licensed MIT. It adds 35 tokens to every session and 3,329 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 2026-08-30.
Other skills, from other repositories
discovery-toolbox
A routed repertoire of 90 scientific thinking operators for biological research agents - visual reasoning, detectability and information budgets, search reframing, causal identification, competing explanations, observation and selection processes, pipeline artifact diagnosis, effort allocation, and confirmation…
discovery-director
Operate as a research director making original discoveries from a given biological question and dataset. Use when the task is open-ended scientific research, exploring omics or experimental data for findings, hypothesis generation and testing, screening a large candidate space of genes, variants, features or…
bio-interdomain-hgt
Detect and polarize interdomain horizontal gene transfer with homology, context, and phylogenetic checks. Use when studying lateral gene transfer, virus-host gene exchange, endogenous viral elements, or donor direction.
polars-dovmed
Search PMC Open Access and bioRxiv corpora with polars-dovmed. Use when structured, reproducible literature queries should run through the hosted API or local parquet indexes.
csag-extraction
Extract a Conditional Scientific Argumentation Graph and grounded Q&A from a manuscript. Use when representing assertions, contexts, evidence links, and inference steps in machine-readable form.
exploratory-data-analysis
Inspect scientific data and generate a Markdown structure-and-quality report. Use when triaging tabular, array, sequence, HDF5, JSON, or raster files before downstream analysis.