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scientific-thinking

Use when interpreting research findings, evaluating scientific evidence, analyzing mechanisms, comparing competing hypotheses, designing experiments, or constructing scientific arguments.

skill-install — Terminal

Install via CLI (Recommended)

clawhub install openclaw/skills/skills/agents365-ai/scientific-thinking-general
Or

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 levelLanguage to use
Strong, replicated"demonstrates", "establishes"
Consistent, single source"supports", "is consistent with"
Suggestive, indirect"suggests", "is compatible with"
Speculative"raises the possibility", "cannot exclude"
Absent"is insufficient to conclude"

6. Define the Boundary

Every meaningful conclusion has limits. State when relevant:

  • what this conclusion supports vs. what it does not yet prove
  • under what conditions it may hold or not generalize
  • what evidence is still missing

7. Move Toward Resolution

Do not stop at abstract interpretation. Suggest:

  • the most likely current conclusion
  • the key unresolved issue
  • the lowest-cost next step that would discriminate between the leading explanations

Output Structure

Metadata

Stars4473
Views0
Updated2026-05-01
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Add to Configuration

Paste this into your clawhub.json to enable this plugin.

{
  "plugins": {
    "official-agents365-ai-scientific-thinking-general": {
      "enabled": true,
      "auto_update": true
    }
  }
}
Safety NoteClawKit audits metadata but not runtime behavior. Use with caution.

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