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 pufferlibgit 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/pufferlib)<a href="https://agentmods.dev/skills/k-dense-ai/scientific-agent-skills/pufferlib"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/pufferlib/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/pufferlib"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/pufferlib.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 | $0.00065 | $0.03259 |
| Opus 5 | $0.00032 | $0.01630 |
| Sonnet 5 | $0.00013 | $0.00652 |
| Haiku 4.5 | $0.00006 | $0.00326 |
Grade C, and why
pufferlib 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 7d 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.
9. Never dump all environment variables or recursively search for `.env`. Copies of this mod
1 near-identical copy found in the catalogue:
- pufferlib — 91% identical, 19 lines differ
How it starts
The opening of the file, as written. The whole thing — 346 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PufferLib
Use PufferLib with an explicit version profile. Upstream currently has two incompatible surfaces:
| Profile | Status on 2026-07-23 | Main use |
|---|---|---|
pufferlib==3.0.0 |
Latest stable PyPI release, published 2025-06-23 | Python/Gymnasium/PettingZoo emulation, pufferlib.vector, Torch PuffeRL |
source 4.0 |
Upstream default branch; not the latest stable PyPI artifact | Native C Ocean environments, native CUDA trainer, optional Torch fallback |
Do not combine 3.0 imports with 4.0 config/CLI examples. The 4.0 redesign
removed the 3.0 emulation, vector, and pytorch modules from the current
package tree.
Safe defaults
- Start with bundled synthetic, CPU-only, network-free tools.
- Do not import an arbitrary environment by dotted path. Bundled tools accept only allowlisted built-ins and slug identifiers.
- Do not install or execute an unreviewed environment package, native extension, ROM, map, checkpoint, or pickle file.
- Verify official source, immutable revision, licenses, checksums or attestations, and build hooks. Sandbox native builds and first execution.
- Cap steps, environments, agents, workers, threads, buffers, memory, disk, render size, and wall time.
- Keep training and evaluation environments/seeds separate.
- Default logging to local/none. External logging requires explicit opt-in, disclosure acknowledgment, and separate artifact-upload approval.
- Never pass W&B or Neptune credentials via CLI, INI, JSON, tags, run names, or logger configuration. Never print them.
- Never dump all environment variables or recursively search for
.env. - Hash checkpoint bytes before trusted, sandboxed loading; metadata inspection is not proof of safety.
First local checks
All bundled CLIs are dependency-free and emit strict JSON:
python3 scripts/env_template.py --help
python3 scripts/env_contract_validator.py
python3 scripts/benchmark_vectorization.py --backend serial
python3 scripts/train_template.py
python3 scripts/validate_plan.py
python3 scripts/repro_plan.py
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/environments.md 9.1 KB
- references/integration.md 8.7 KB
- references/policies.md 6.8 KB
- references/training.md 10 KB
- references/vectorization.md 8.2 KB
- scripts/__init__.py 62 B runs code
- scripts/_common.py 6.5 KB runs code
- scripts/benchmark_vectorization.py 8.1 KB runs code
- scripts/env_contract_validator.py 6.7 KB runs code
- scripts/env_template.py 7.1 KB runs code
- scripts/inspect_checkpoint.py 6.7 KB runs code
- scripts/repro_plan.py 5.9 KB runs code
- scripts/train_template.py 9.7 KB runs code
- scripts/validate_plan.py 18 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.
- 7d ago First seen · 346 lines · 65 tokens per session scan C dc920861122e
pufferlib is a skill published in the GitHub repository K-Dense-AI/scientific-agent-skills (44,220 stars, last pushed 3d ago), licensed MIT. It adds 65 tokens to every session and 3,259 once invoked, about $0.0003 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
AI & LLM Security
LLM and AI application security testing — prompt injection, jailbreak resistance, OWASP LLM Top 10 (2025), RAG and agent/tool-use security, model supply chain, and AI red teaming for authorized assessments.
laravel-ai-sdk
Use when integrating AI agents, tool calling, embeddings, structured output, or streaming in Laravel 13 via the laravel/ai package.
laravel-vector-search
Use when implementing semantic/vector search in Laravel 13 with PostgreSQL + pgvector.
prompt-library
Use when looking for a ready-made agent or task template (code review, support, data analysis, translation, extraction) instead of writing one from scratch.
prompt-testing
Use when comparing two prompt variants, defining quality/efficiency/robustness metrics, or deciding whether to adopt a challenger prompt over a baseline.
prompt-creation
Use when writing a new system prompt from scratch, structuring it with Anthropic's 9-element format, or choosing emphasis levels and few-shot examples.