hypothesis-evolve-inspiration

hypothesis-evolve-inspiration is a skill for Claude Code, Codex from panjose/Co-Scientist. It costs 26 tokens per session (688 once invoked), scanned A, original, Apache-2.0.

A research skill that creates one new hypothesis by carrying a useful principle from one parent context into another. A hypothesis is a testable explanation or idea.

In plain words
What is it for?
Creating one child hypothesis from selected parent hypotheses and their review materials, then saving the required hypothesis and origin files.
Why use it?
It gives cross-domain research a defined way to generate a novel idea while keeping its source principle explicit.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Creating one child hypothesis from selected parent hypotheses and their review materials, then saving the required hypothesis and origin files.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/panjose/co-scientist/hypothesis-evolve-inspiration
Install

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.

Any agent
npx skills add panjose/Co-Scientist --skill hypothesis-evolve-inspiration
Clone the repo
git clone --depth 1 https://github.com/panjose/Co-Scientist

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for hypothesis-evolve-inspiration

README.md
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Your own site
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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.

agentmods 80×15 button for hypothesis-evolve-inspiration

Your own site · 80×15
<a href="https://agentmods.dev/skills/panjose/co-scientist/hypothesis-evolve-inspiration"><img src="https://agentmods.dev/badge/skills/panjose/co-scientist/hypothesis-evolve-inspiration.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 26 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 688 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00026 $0.00688
Opus 5 $0.00013 $0.00344
Sonnet 5 $0.00005 $0.00138
Haiku 4.5 $0.00003 $0.00069

Measured 11d ago against content hash f36002d06207, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

hypothesis-evolve-inspiration 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 11d 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.

skills/hypothesis-evolve-inspiration/SKILL.md · 68 lines

What it actually says

hypothesis-evolve-inspiration

Goal:

  • Generate exactly one cross-parent child hypothesis by transferring a useful principle from one parent context into another.

Inputs:

  • research_plan/RESEARCH_PLAN.json
  • selected parent hypotheses/<id>/HYPOTHESIS.json artifacts
  • parent review artifacts
  • active state/STRATEGY_PLAN.json

Outputs:

  • hypotheses/<id>/HYPOTHESIS.json
  • hypotheses/<id>/HYPOTHESIS.md
  • hypotheses/<id>/ORIGIN.json

Context Loading:

  • Open skills/shared-references/schema-index.md.
  • Read packages/agent_contracts/hypothesis.py and confirm the exact HypothesisContract shape before writing hypotheses/<id>/HYPOTHESIS.json.
  • Read research_plan/RESEARCH_PLAN.json.
  • Read state/STRATEGY_PLAN.json.
  • Read the selected parent hypotheses and their review bundles.
  • Confirm that the round selected inspiration_evolution.

Execution Prompt Contract:

  • System Intent:
    • You are producing one novel child hypothesis by transferring a principle from one parent into another domain or mechanism context.
  • Required Reasoning Focus:
    • Name the borrowed principle.
    • Keep the analogical leap explicit and testable.
    • Produce a child that is genuinely new, not just a stitched paraphrase.
  • Do Not Do:
    • Do not copy one parent with cosmetic changes.
    • Do not emit more than one child.
  • Quality Floor:
    • The child must directly address at least one specific weakness from the parent review bundle.
    • origin.content.statement must name concrete materials, catalysts, reaction conditions, mechanistic variables, or experimental targets from the parent and research goal.
    • origin.content.mechanism must explain a causal chain; do not write only generic phrases such as improved mechanism, targeted improvement, or review-identified weaknesses.
    • origin.content.experimental_design must include 3-6 numbered steps with measurable readouts, controls, or decision thresholds.
    • Do not use generic refinement placeholder steps such as Apply targeted improvement, Characterize with standard techniques, Benchmark against parent, or Validate improvement quantitatively.
    • If the research plan, parent hypothesis, or parent review bundle is missing, stop and report the missing artifact instead of guessing.
  • Output Shape:
    • Emit the canonical HypothesisContract.
    • origin.strategy: inspiration_evolution
    • Keep summary and category specific to the new cross-parent insight.

Execution Steps:

  1. Open skills/shared-references/schema-index.md, then read packages/agent_contracts/hypothesis.py before writing hypotheses/<id>/HYPOTHESIS.json.
  2. Identify the transferable principle across the parent set.
  3. Produce exactly one inspired child hypothesis.
  4. Persist canonical HYPOTHESIS.json, HYPOTHESIS.md, and ORIGIN.json.
  5. Validate the emitted hypothesis artifact.

Completion Rule:

  • This skill is complete only when one inspired cross-parent child hypothesis has been written in canonical form.
Changes

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.

  1. 11d ago First seen · 68 lines · 26 tokens per session scan A f36002d06207

Subscribe to this mod's changes

hypothesis-evolve-inspiration is a skill published in the GitHub repository panjose/Co-Scientist (5 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 26 tokens to every session and 688 once invoked, about $0.0001 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 2026-08-31.

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