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 hypothesis-generationgit 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/hypothesis-generation)<a href="https://agentmods.dev/skills/k-dense-ai/scientific-agent-skills/hypothesis-generation"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/hypothesis-generation/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/hypothesis-generation"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/hypothesis-generation.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 | $0.00058 | $0.03174 |
| Opus 5 | $0.00029 | $0.01587 |
| Sonnet 5 | $0.00012 | $0.00635 |
| Haiku 4.5 | $0.00006 | $0.00317 |
Grade A, and why
hypothesis-generation 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 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.
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:
- hypothesis-generation — 100% identical, 19 lines differ
How it starts
The opening of the file, as written. The whole thing — 282 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Scientific Hypothesis Generation
Turn an observation into a transparent set of candidate explanations and tests. A hypothesis is a proposal to be challenged, not a finding, fact, diagnosis, or recommendation.
Non-negotiable boundaries
Before using unpublished, sensitive, controlled, personal, proprietary, export-controlled, or security-relevant material:
- Confirm authorization and the applicable institutional, funder, publisher, data-use, privacy, and AI policies.
- Keep the material local unless an authorized human explicitly approves a named external destination and data scope.
- Minimize inputs. Do not place sensitive or unpublished data in web searches or external AI systems without authorization.
- Stop at the appropriate human, animal, biosafety, dual-use, data-governance, or regulatory gate.
Never:
- present a hypothesis, mechanism, causal effect, citation, or apparent pattern as established evidence;
- claim novelty because a quick search found nothing;
- infer causation from association, temporal order alone, predictive accuracy, or model output;
- supply patient-specific diagnosis, treatment, dose, prognosis, or other clinical advice;
- provide harmful experimental optimization or operational detail for pathogens, toxins, weapons, evasion, or other misuse;
- bypass IRB/REC, IACUC, IBC, biosafety, dual-use, privacy, legal, or regulatory review;
- fabricate sources, identifiers, search coverage, data, results, approvals, or preregistration;
- automatically score, rank, select, accept, or reject scientific hypotheses.
If a request crosses a safety gate, produce only a high-level risk/oversight note and route it to the qualified local authority. Do not continue with operational detail.
Keep the objects distinct
| Object | Meaning |
|---|---|
| Observation | What was measured, noticed, or reported, with provenance and uncertainty |
| Research question | The answerable question that defines scope |
| Hypothesis | A candidate explanatory or relational proposition |
| Mechanism | The proposed process connecting conditions to an outcome |
| Causal estimand | The precisely defined causal contrast to estimate |
| Prediction | An observable implication derived before checking the target result |
| Alternative explanation | A rival account, including bias or non-causal explanations |
| Null hypothesis | A specified no-effect/no-difference model used by an analysis |
| Negative control | A control expected not to operate through the proposed mechanism |
| Operationalization | How a construct becomes a variable, measurement, intervention, or category |
| Analysis plan | Prespecified transformations, models, contrasts, uncertainty, and decision rules |
| Evidence | Observations or sources that bear on a claim; never the claim itself |
What ships with it
26 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/evidence_ledger_template.csv 581 B
- assets/falsification_controls_template.json 5.7 KB
- assets/hypothesis_record_template.json 13 KB
- assets/operationalization_template.json 2.2 KB
- assets/prediction_rival_matrix_template.csv 1.6 KB
- assets/preregistration_scaffold_template.md 4.9 KB
- assets/search_boundary_template.json 1.1 KB
- assets/source_ledger.csv 12 KB
- references/causal_inference_and_claims.md 7.6 KB
- references/concepts_and_workflow.md 7.2 KB
- references/ethics_safety_and_ai.md 9.7 KB
- references/experimental_design_patterns.md 9.5 KB
- references/hypothesis_quality_criteria.md 7.8 KB
- references/literature_search_strategies.md 7.9 KB
- references/preregistration_and_open_science.md 6.6 KB
- references/security_validation.md 3.6 KB
- references/source_ledger.md 7.2 KB
- references/tool_reference.md 7.9 KB
- scripts/_common.py 14 KB runs code
- scripts/audit_evidence_ledger.py 12 KB runs code
- scripts/check_falsification_controls.py 16 KB runs code
- scripts/check_operationalization.py 7.9 KB runs code
- scripts/generate_preregistration_scaffold.py 14 KB runs code
- scripts/lint_causal_claims.py 6.6 KB runs code
- scripts/validate_hypothesis_schema.py 38 KB runs code
- scripts/validate_prediction_matrix.py 9.6 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 Changed · +17 lines 8153fdc33e0a
- 12d ago First seen · 265 lines · 58 tokens per session scan A 039881416522
hypothesis-generation is a skill published in the GitHub repository K-Dense-AI/scientific-agent-skills (44,469 stars, last pushed today), licensed MIT. It adds 58 tokens to every session and 3,174 once invoked, about $0.0003 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…
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.