Skills hypothesis-generation

📦

hypothesis-generation

Name: hypothesis-generation
Author: K-Dense-AI

Content revision r1 Safe 🌐 Network access⚙️ External commands

Generate Testable Scientific Hypotheses

Researchers need to turn observations into clear, testable explanations. This skill structures evidence review, competing hypotheses, predictions, and experiment plans.

Supports: Claude Codex Code(CC)

🥈 80 Silver

Install with my Agent

Copy this request to your Agent. It includes the canonical Skill page and manifest.

Agent request

Review the Skillstore skill "hypothesis-generation" from https://skillstore.io/skills/k-dense-ai-hypothesis-generation.md and its manifest at https://skillstore.io/api/skills/k-dense-ai-hypothesis-generation/manifest. Verify the artifact. You may proceed after verification, subject to the environment's own policy.

Your Agent should still show its plan and request any confirmation required by the security policy.

Agent-readable resources

Use these links when an AI agent, crawler, or script needs clean context instead of reading the full page.

Markdown detail GET /skills/k-dense-ai-hypothesis-generation.md Signed manifest GET /api/skills/k-dense-ai-hypothesis-generation/manifest Signed lockfile GET /api/skills/k-dense-ai-hypothesis-generation/lockfile

Test it

Using "hypothesis-generation". Observation: Cell growth slows after treatment, but viability remains high.

Expected outcome:

Three competing mechanisms that explain slowed growth without cell death.
Predictions for cell-cycle markers, metabolic activity, and recovery after washout.
Two experiments with controls that separate cytostasis from delayed toxicity.

Using "hypothesis-generation". Observation: A biomarker rises before symptoms in a longitudinal cohort.

Expected outcome:

Causal, compensatory, and confounded association hypotheses.
Quality assessment across testability, parsimony, and explanatory power.
A study plan using time-lag analysis, matched controls, and validation cohorts.

Using "hypothesis-generation". Goal: Prepare a hypothesis report for a grant planning meeting.

Expected outcome:

Executive summary with prioritized hypotheses and key evidence.
Prediction matrix showing results that support or falsify each explanation.
Appendix outline for literature, protocols, assumptions, and limitations.

Security Audit

Safe

v7 • 7/6/2026 Open versioned report

Static reconnaissance and network findings are false positives from scientific prose and PubMed search templates. External-command detections are Markdown code fences or inline LaTeX and file references, with no evidence of hidden execution, credential access, or exfiltration. No prompt injection attempt or semantic abuse was found.

Files scanned

3,001

Lines analyzed

Review items

False positives ignored

No confirmed security findings were detected by the latest completed static and semantic audit. This does not prove the skill has no side effects.

Audited by: codex View Audit History →

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Open versioned report

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Academic citations (APA · BibTeX · CFF)

APA citation

K-Dense-AI. (2026). hypothesis-generation security audit report (audit version 7) [Author version unspecified]. Skillstore. https://skillstore.io/skills/k-dense-ai-hypothesis-generation/audits/7

BibTeX citation

@techreport{k-dense-ai-k-dense-ai-hypothesis-generation-2026,
  author = {K-Dense-AI},
  title = {hypothesis-generation security audit report (audit version 7)},
  institution = {Skillstore},
  year = {2026},
  number = {7},
  url = {https://skillstore.io/skills/k-dense-ai-hypothesis-generation/audits/7},
  note = {Author version unspecified}
}

CITATION.cff

cff-version: 1.2.0
message: "If you use this Skill, cite its author and this versioned security audit report."
title: "hypothesis-generation security audit report (audit version 7)"
version: "unspecified"
type: report
authors:
  - name: "K-Dense-AI"
date-released: "2026-07-06"
url: "https://skillstore.io/skills/k-dense-ai-hypothesis-generation/audits/7"
identifiers:
  - type: other
    value: "skillstore:k-dense-ai-hypothesis-generation:audit:7"
    description: "Skillstore immutable audit report identifier"

Compare variants

2 installable variants

Each author remains a separate installable skill. The recommended variant is ranked by Skillstore evidence.

Why this variant is first

Highest Skillstore Score

Author / source Skillstore Score Evidence Confidence Skillstore usage Updated

K-Dense-AI Recommended Current

k-dense-ai-hypothesis-generation

Skillstore Score 80

Evidence Confidence High

Skillstore usage 14

Updated

2026-08-21

davila7

davila7-hypothesis-generation

Skillstore Score 79

Evidence Confidence High

Skillstore usage 12

Updated

2026-08-21

Skillstore Score

Why this score Evidence Confidence: High

Architecture

Maintainability

Content

Community

Spec Compliance

What You Can Build

Plan a Thesis Project

Convert preliminary observations into competing hypotheses, measurable predictions, and experiments for a research proposal.

Explore Product Research Signals

Analyze unexpected experimental results and produce mechanistic explanations with tests for the next lab cycle.

Review Research Directions

Compare several scientific explanations and prioritize the studies most likely to distinguish them.

Try These Prompts

Generate Initial Hypotheses

Use this observation: [observation]. Generate three testable hypotheses, key assumptions, and one prediction for each.

Compare Explanations

Analyze these observations: [observations]. Create competing hypotheses, rank them by testability, and list evidence needed to distinguish them.

Plan Experiments

For this hypothesis: [hypothesis]. Propose experiments, controls, measurements, confounds, and falsifying outcomes.

Build Research Report

Develop a structured hypothesis report for [phenomenon]. Include literature search strategy, competing mechanisms, predictions, experimental designs, and appendix-ready evidence notes.

Best Practices

Provide the core observation, domain, existing data, and known constraints before starting.
Ask for competing hypotheses rather than a single preferred explanation.
Review proposed experiments with domain experts before using them in real studies.