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 | $0.00068 | $0.03943 |
| Opus 5.5 | $0.00027 | $0.01577 |
| Sonnet 5.5 | $0.00014 | $0.00789 |
| Haiku 4.5 | $0.00007 | $0.00394 |
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 24d 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`. How it starts
The opening of the file, as written. The whole thing — 387 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PufferLib
Choose the version before choosing an API. Reviewed 2026-10-01:
| Profile | Status | Main use |
|---|---|---|
Native source 5.0 |
Current default branch and live documentation | C/CUDA environments, native trainer; CPU evaluation only |
pufferlib==3.0.0 |
Latest PyPI release, published 2025-06-23; sdist only | Python/Gymnasium/PettingZoo adaptation and Torch PuffeRL |
Pinned source 4.0 |
Historical snapshot | C Ocean interface with an optional Torch fallback |
For 5.0, read references/native-5.md. The reviewed
revision is 6ffa5b10dbbbe4d1e8288367c7d9d3acd3bad4a2. Its CLI is
./puffer train after building an environment, not puffer train ENV_NAME.
There is no 5.0 Python emulation/vector API or --slowly fallback.
The bundled plan schema deliberately supports only 3.0 and pinned 4.0; it does
not launch training. All native/PufferLib training examples are source-reviewed,
illustrative, and not executed in this review. Bundled synthetic checks are
executed CPU tests, not evidence of PufferLib installation or learning quality.
The PyPI sdist was hash-verified and its Python sources inspected; the moving
3.0 branch differs, including its load_policy and logger contracts.
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.
What ships with it
15 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 11 KB
- references/integration.md 9.4 KB
- references/native-5.md 8.1 KB
- references/policies.md 7.2 KB
- references/training.md 13 KB
- references/vectorization.md 8.7 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 6.9 KB runs code
- scripts/repro_plan.py 5.9 KB runs code
- scripts/train_template.py 10 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.
- 24d ago Changed · +37 lines · -7 tokens per session 031975c6abaa
- 25d ago Changed · +4 lines · +10 tokens per session dffc958b212f
- 1mo 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 (54,587 stars, last pushed 7d ago), licensed MIT. It adds 68 tokens to every session and 3,943 once invoked, about $0.0003 per session on Opus 5.5. 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
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-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.
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-optimization
Use when auditing an existing prompt for clarity, structure, or completeness issues, fixing vague instructions, or producing a before/after optimization report.