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 pymatgengit 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/pymatgen)<a href="https://agentmods.dev/skills/k-dense-ai/scientific-agent-skills/pymatgen"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/pymatgen/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/pymatgen"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/pymatgen.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
- 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 | $0.00047 | $0.03997 |
| Opus 5 | $0.00023 | $0.01998 |
| Sonnet 5 | $0.00009 | $0.00799 |
| Haiku 4.5 | $0.00005 | $0.00400 |
Grade C, and why
pymatgen scanned grade C with 1 finding 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 8d 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.
Harvests environment variableshighData exfiltration
Enumerating or grepping the environment for keys collects credentials unrelated to what the mod says it does.
the key as a CLI argument, traverse `.env` files, dump environment variables, How it starts
The opening of the file, as written. The whole thing — 422 lines — stays where its author put it; the contents beside it link to each section on GitHub.
pymatgen
Use pymatgen for explicit, provenance-preserving work with compositions, molecules, periodic structures, computed entries, symmetry, phase diagrams, electronic structures, and electronic-structure-code files. Treat every parse, conversion, symmetry assignment, transformation, and database result as method- and parameter-dependent.
The MIT frontmatter license covers this skill. pymatgen and
pymatgen-core are MIT; mp-api declares BSD-3-Clause-LBNL. Materials Project
data is generally CC BY 4.0, while contributed data remains owned by its
contributors. Check the exact artifact and data terms before redistribution.
Verified snapshot (2026-07-23)
pymatgen==2026.5.4is the latest stable wrapper release (2026-05-04). Package metadata requires Python 3.11+ and directly requirespymatgen-core>=2026.4.16.pymatgen-core==2026.7.16is the latest stable core release (2026-07-16). It now contains core objects, symmetry/lattice operations, and the I/O layer, all under the existingpymatgen.*namespace.mp-api==0.46.4is the latest stable Materials Project client (2026-06-15), requires Python 3.11+, and depends onpymatgen>2024.2.20.- The current API site is built from 2026.7.16 core documentation. Pinning both
distributions prevents
pymatgen==2026.5.4from silently resolving to a different future core. - Pymatgen uses date-based versions. PyPI renders the date with dots; do not infer semantic-version compatibility from the numbers.
Create a project lock for reproducibility:
uv init --python 3.11
uv add "pymatgen==2026.5.4" "pymatgen-core==2026.7.16" "mp-api==0.46.4"
uv lock
uv sync --frozen
For a disposable reviewed environment:
uv venv --python 3.11 .venv-pymatgen
uv pip install --python .venv-pymatgen/bin/python \
"pymatgen==2026.5.4" "pymatgen-core==2026.7.16" "mp-api==0.46.4"
Direct pins do not freeze all transitive wheels. Preserve uv.lock, platform,
Python version, package versions, and artifact hashes.
What ships with it
14 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/analysis_modules.md 11 KB
- references/core_classes.md 8.9 KB
- references/io_formats.md 11 KB
- references/materials_project_api.md 13 KB
- references/transformations_workflows.md 12 KB
- scripts/_common.py 10 KB runs code
- scripts/artifact_manifest.py 5.5 KB runs code
- scripts/composition_structure_validator.py 11 KB runs code
- scripts/io_conversion_plan.py 6.8 KB runs code
- scripts/mp_query.py 15 KB runs code
- scripts/phase_diagram_generator.py 14 KB runs code
- scripts/structure_analyzer.py 11 KB runs code
- scripts/structure_converter.py 7.4 KB runs code
- scripts/symmetry_sensitivity_report.py 7.5 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.
- 8d ago First seen · 422 lines · 47 tokens per session scan C 84e4d8eaf9da
pymatgen is a skill published in the GitHub repository K-Dense-AI/scientific-agent-skills (44,469 stars, last pushed today), licensed MIT. It adds 47 tokens to every session and 3,997 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it C with 1 finding (harvests environment variables). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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…
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.
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.
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.