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 diffdockgit 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/diffdock)<a href="https://agentmods.dev/skills/k-dense-ai/scientific-agent-skills/diffdock"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/diffdock/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/diffdock"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/diffdock.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector warn
SkillSpector: 3 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium analysis-evasion · line 1 Suspicious Unicode normalization or mixed-script contentFix: Review the flagged content for security risks. Ensure no credentials, secrets, or sensitive data are exposed.
- medium MCP Rug Pull · line 64 Docker image references without a specific tag (:latest is implicit) or digest (@sha256:...) can be silently replaced by a malicious image.Fix: Pin the image: image:tag or image@sha256:abc123
- medium MCP Rug Pull · line 65 Docker image references without a specific tag (:latest is implicit) or digest (@sha256:...) can be silently replaced by a malicious image.Fix: Pin the image: image:tag or image@sha256:abc123
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 | $0.00063 | $0.02735 |
| Opus 5.5 | $0.00025 | $0.01094 |
| Sonnet 5.5 | $0.00013 | $0.00547 |
| Haiku 4.5 | $0.00006 | $0.00274 |
Grade A, and why
diffdock 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 14d 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
8 near-identical copies found in the catalogue:
- diffdock — 100% identical, 577 lines differ
- diffdock — 100% identical, 577 lines differ
- diffdock — 100% identical, 577 lines differ
- diffdock — 100% identical, 577 lines differ
- diffdock — 100% identical, 577 lines differ
- diffdock — 91% identical, 564 lines differ
- diffdock — 91% identical, 548 lines differ
- diffdock — 91% identical, 563 lines differ
How it starts
The opening of the file, as written. The whole thing — 232 lines — stays where its author put it; the contents beside it link to each section on GitHub.
DiffDock: protein-small-molecule docking
DiffDock-L generates candidate ligand poses and ranks them by model confidence. Confidence is neither a measured probability of correctness nor binding affinity. Use this skill for one pair, batches, or separate receptor conformations; treat library-wide confidence sorting as pose triage, not hit identification.
Verified scope
Targets the current v1.1.3 release.
Released source and current main's identical inference.py were checked on
2026-09-30. Bundled helpers were tested on tiny synthetic inputs; pretrained docking,
ESMFold, CUDA, Docker, GNINA, and hosted-demo execution were not run in this
review. Commands requiring those components are source-verified recipes, not
successful end-to-end demonstrations.
Set up the upstream environment
git clone --branch v1.1.3 --depth 1 https://github.com/gcorso/DiffDock.git
cd DiffDock
conda env create --file environment.yml
conda activate diffdock
The upstream environment pins Python 3.9.18, CUDA 11.7 Torch/PyG wheels and old
scientific dependencies. Do not substitute current torch or the unrelated PyPI
esm package for fair-esm. The published CUDA environment is not a macOS-native
installation recipe. Follow upstream Docker instructions if suitable:
docker pull rbgcsail/diffdock
docker run -it --gpus all --entrypoint /bin/bash rbgcsail/diffdock
micromamba activate diffdock
Record the image digest: its unversioned tag need not equal the checked source.
PDB-based inference has a CPU path; sequence folding calls .cuda() unconditionally.
ESM2 embeddings are needed even for PDB inputs. First use can download docking,
ESM2 and (for sequence inputs) ESMFold weights and build SO(2)/SO(3) tables. Budget
storage and memory for all components; do not assume a single small checkpoint.
Run this skill's checker from the DiffDock checkout using its absolute path:
python /path/to/diffdock-skill/scripts/setup_check.py
What ships with it
8 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.
- assets/batch_template.csv 410 B
- assets/custom_inference_config.yaml 1.1 KB
- references/confidence_and_limitations.md 4.8 KB
- references/parameters_reference.md 5.2 KB
- references/workflows_examples.md 4.8 KB
- scripts/analyze_results.py 12 KB runs code
- scripts/prepare_batch_csv.py 11 KB runs code
- scripts/setup_check.py 8.7 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.
- 14d ago Changed · -264 lines · +12 tokens per session 5fafc5d9f53f
- 15d ago Changed a4b57fdff399
- 1mo ago Changed · +7 lines c6feade726b2
- 1mo ago First seen · 489 lines · 51 tokens per session scan A 42d9550fab2a
diffdock is a skill published in the GitHub repository K-Dense-AI/scientific-agent-skills (50,453 stars, last pushed today), licensed MIT. It adds 63 tokens to every session and 2,735 once invoked, about $0.0003 per session on Opus 5.5. 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 reciprocal hits, genomic context, and gene trees. Use for 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.
notebooks
Author, run and convert marimo or Jupyter notebooks. Use when delivering an analysis notebook executed end to end with figures embedded.
bio-annotation
Annotate proteins with functions, domains, and homology-based taxonomy. Use when assigning functional or taxonomic labels to proteins from genomes, contigs, or MAGs.