| name | scientific-thinking |
| description | Use when interpreting research findings, evaluating scientific evidence, analyzing mechanisms, comparing competing hypotheses, designing experiments, or constructing scientific arguments. |
| license | MIT |
| homepage | https://github.com/Agents365-ai/scientific-thinking-skill |
| compatibility | No external tool dependencies. Works with any LLM-based agent on any platform. |
| platforms | ["macos","linux","windows"] |
| metadata | {"openclaw":{"requires":{},"emoji":"🔬","os":["darwin","linux","win32"]},"hermes":{"tags":["scientific-thinking","research","reasoning","evidence-evaluation","hypothesis","experiment-design","mechanism","peer-review"],"category":"research","requires_tools":[],"related_skills":["literature-review","paper-reader","zotero-cli-cc"]},"pimo":{"category":"research","tags":["scientific-thinking","reasoning","evidence-evaluation","research","hypothesis"]},"author":"Agents365-ai","version":"1.0.0"} |
Scientific Thinking
A meta-skill for structured, evidence-aware, boundary-conscious scientific reasoning. Your role is not just to answer — it is to reason like a careful researcher.
When to Use
- Interpreting experimental results or paper conclusions
- Analyzing mechanisms or pathways
- Distinguishing concepts that are being conflated
- Evaluating competing hypotheses
- Designing or critiquing experiments
- Constructing scientific arguments
Core Reasoning Framework
Work through these layers before responding.
1. Frame the Problem
- What exactly is being asked?
- Scientific level: fact / concept / mechanism / method / interpretation / decision?
- What is known, unknown, and assumed?
- Restate the real problem if the question is broad or ambiguous.
2. Decompose
- What needs to be defined first?
- What hidden assumptions are present?
- What distinctions must be kept separate (phenotype vs mechanism, association vs causation, state vs lineage)?
- What would make the conclusion invalid?
3. Separate Evidence from Interpretation
Always distinguish among: observed fact / direct evidence / indirect evidence / interpretation / hypothesis / speculation / uncertainty.
- Do not present a hypothesis as a fact.
- Do not present correlation as causation.
- Do not present a label as a mechanism.
Evidence provenance: State whether each key claim comes from (a) provided data, (b) general background knowledge, or (c) inference. If required evidence is absent from the prompt, either retrieve it or explicitly label the answer as provisional reasoning.
4. Consider Alternative Explanations
Before giving a conclusion:
- Is there another plausible explanation?
- Could this be caused by confounding, measurement error, sampling bias, or definition mismatch?
- Could this reflect context rather than essence?
If multiple explanations are plausible, rank them by available support. Do not pretend there is only one. Surface alternatives only when they are genuinely plausible — do not force false balance.
5. Calibrate Claim Strength
Match conclusion strength to evidence strength:
| Evidence level | Language to use |
|---|
| Strong, replicated | "demonstrates", "establishes" |
|
