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adeerkhanadeerkhan/vitruvius/skills/hypothesis-generation/SKILL.md

hypothesis-generation

Skill10 starsChanged 4 days agoMITView on GitHub

Generate and freeze engineering research hypotheses before testing. Use when the user asks to "formulate hypotheses", "what could explain this", "propose mechanisms", or needs structured hypothesis generation for an engineering observation. Produces dated evidence boundaries, rival hypotheses, and discriminating predictions. Do NOT use for final hypothesis selection or experimental design — this skill generates candidates only, never scores or selects.

23 other projects use a version of this.

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  • YuchengZhang256Skill

    paper-lookup

    Look up a specific paper or exact-known query across 10 academic databases via their REST APIs (PubMed, PMC, bioRxiv, medRxiv, arXiv, OpenAlex, Crossref, Semantic Scholar, CORE, Unpaywall). Trigger ONLY when the user supplies a precise identifier (DOI, PMID, PMCID, arXiv ID, exact title) OR explicitly names one of these databases. Use for: fetching metadata for one known paper, pulling an abstract or full text for a known article, checking open-access status, retrieving a citation count, listing an author's publications, fetching references or citing papers for one specific paper. Single-shot targeted queries only — do NOT trigger on broad topic discovery ("find papers about X"), systematic review workflows, or methodological overlap scanning; those are handled elsewhere.

    Also in 3 projects that use this

  • ZanderRussSkill

    venue-templates

    Access comprehensive LaTeX templates, formatting requirements, and submission guidelines for major scientific publication venues (Nature, Science, PLOS, IEEE, ACM), academic conferences (NeurIPS, ICML, CVPR, CHI), research posters, and grant proposals (NSF, NIH, DOE, DARPA). This skill should be used when preparing manuscripts for journal submission, conference papers, research posters, or grant proposals and need venue-specific formatting requirements and templates.

    Also in 3 projects that use this

The file

We split this skill into the rules it gives an agent

3 rules. 0 are common, 1 is distinctive, and 2 are very specific to this project.

  • 0

    Common

    Most of the 23 edited versions we read keep these.

  • 1

    Distinctive

    This author's own choices. Worth a look.

  • 2

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Before searching, name the question, the artifact/scope (or say it is a literature review), the jurisdiction/edition, and the effort budget …

    line 26
  2. Very specific:

    Write outputs/hypothesis/<slug>.provenance.md beside the record (sources consulted, accepted, and rejected.

    line 110
  3. Very specific:

    Never fabricate evidence to support a hypothesis.

    line 116

“Common” is measured: kept by at least 6 in 10 of the 23 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md
<!-- VITRUVIUS-COMPILED-SKILL:BEGIN v1 sha256=a617c15afa89b437f63aaa4faef797b4a12ddcfd77b4f30fadfa32427112dd20 -->

Hypothesis Generation

Generate evidence-bounded engineering hypotheses. This skill freezes the observation, frames the question, establishes evidence boundaries, generates rival candidates, and derives discriminating predictions. It never scores, ranks, or selects hypotheses — that is a human decision.

Input Gate

Before searching, name the question, the artifact/scope (or say it is a literature review), the jurisdiction/edition, and the effort budget (user-set or thorough). Ask ONE clarifying question if the ask is too vague, then proceed. See references/input-gate.md.

Invocation

/hypothesis-generation <observation>

Include: the observed phenomenon, context, and any relevant engineering domain (materials, structures, thermal, etc.).

Methodology

  1. Freeze the observation — write the exact observation before interpreting. Distinguish what was measured from what is inferred. Timestamp the observation date.
  2. Frame the research question — restate as a falsifiable engineering question. Identify the claim type: descriptive, associational, predictive, causal, or mechanistic.
  3. Establish evidence boundary — document: search date, databases consulted, query terms, inclusion/exclusion criteria, limitations. "Not located within documented search boundary" — never "no prior work exists".
  4. Generate rival hypotheses — produce 3-5 candidates from DIFFERENT explanatory classes (e.g., material defect, design error, environmental, manufacturing process, maintenance). Each must be falsifiable. No two hypotheses from the same class.
  5. Declare claim type and estimand — specify what would be measured, the expected direction/magnitude, and the population/context where it applies.
  6. Derive discriminating predictions — for each hypothesis, state: condition, observable, expected pattern, falsifier (what result would be incompatible), and rival contrast (how does this prediction differ from the others?).
  7. Prevent HARKing — timestamp the generation. Preserve this document as the pre-registration record. Any deviation in testing must be reported as a deviation, not hidden.

Output

Inline Summary (chat response)

  • Observation (frozen, dated)
  • Research question + claim type
  • Evidence boundary (date, sources, queries, limitations)
  • 3-5 rival hypotheses with class labels
  • For each: discriminating prediction + falsifier

Full Record (saved to disk)

Save to outputs/hypothesis/<slug>.md:

# Hypothesis Generation: <observation>

## Observation (Frozen)
- **Date observed:** <date>
- **Phenomenon:** <exact description>
- **Distinguish:** measured vs inferred
- **Context:** <engineering domain, conditions>

## Research Question
- **Question:** <falsifiable formulation>
- **Claim type:** descriptive / associational / predictive / causal / mechanistic

## Evidence Boundary
- **Search date:** <date>
- **Databases:** <OpenAlex, arXiv, standards, etc.>
- **Query terms:** <exact queries>
- **Inclusion:** <criteria>
- **Exclusion:** <criteria>
- **Limitations:** <what was not searched>

## Rival Hypotheses

### H1: <name> (<explanatory class>)
- **Mechanism:** <how it would cause the observation>
- **Prediction:** <expected result under this hypothesis>
- **Falsifier:** <what result would rule this out>
- **Rival contrast:** <how this differs from H2, H3...>

(repeat for H2, H3...)

## Discriminating Tests
- **Test that separates H1 from H2:** <experiment or analysis>
- **Test that separates H1 from H3:** <experiment or analysis>
- ...

## Pre-registration Record
- **Generated:** <timestamp>
- **Status:** candidate (never scored/selected by tool)
- **Deviations:** none yet

Write outputs/hypothesis/<slug>.provenance.md beside the record (sources consulted, accepted, and rejected; verification status). Minimal shape: references/provenance-sidecar.md.

Scope and Boundaries

  • This skill generates hypothesis candidates — it does NOT score, rank, or select. Status must remain candidate.
  • Research-only, not for final engineering sign-off. Hypothesis selection requires human engineering judgment.
  • Never fabricate evidence to support a hypothesis. If a hypothesis lacks evidence, mark it unverified, not inferred.
  • Evidence quality: see references/evidence-quality-tiers.md.

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