ワンクリックで
hypothesis-generate-literature
Generate exactly one literature-grounded hypothesis candidate for the active round.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
メニュー
Generate exactly one literature-grounded hypothesis candidate for the active round.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
| name | hypothesis-generate-literature |
| description | Generate exactly one literature-grounded hypothesis candidate for the active round. |
Goal:
Inputs:
research_plan/RESEARCH_PLAN.jsonstate/STRATEGY_PLAN.jsonliterature/queries/<query_id>/EVIDENCE_BUNDLE.json produced by tools.search_literature(...)Outputs:
hypotheses/<id>/HYPOTHESIS.jsonhypotheses/<id>/HYPOTHESIS.mdhypotheses/<id>/ORIGIN.jsonliterature/queries/<query_id>/* search bridge artifacts for the evidence query used by this generationContext Loading:
skills/shared-references/schema-index.md.skills/shared-references/literature-search-contract.md.packages/agent_contracts/literature.py before building or consuming any search bridge request or evidence bundle.research_plan/RESEARCH_PLAN.json.research_goal as the task anchor.preferences as the quality axes that the hypothesis should optimize for.constraints as hard boundaries that the hypothesis and experiment design must satisfy.state/STRATEGY_PLAN.json and confirm that the current round allows literature_exploration_generation.Execution Prompt Contract:
tools.search_literature(run_dir, request) with consumer="hypothesis-generate-literature" and use the returned EvidenceBundleContract as the formal external evidence input.tools.search_literature(...).HypothesisContract.origin.content.statement: 2-3 sentences maximum.origin.content.mechanism: 2-3 sentences maximum.origin.content.experimental_design: one concise multiline string with 3-6 numbered steps.origin.content.experimental_design must remain one string field containing embedded line breaks; do not emit it as a list, array, or nested object.origin.content.summary: one concise sentence.origin.content.category: 1-5 words.Execution Steps:
skills/shared-references/schema-index.md, skills/shared-references/literature-search-contract.md, then read packages/agent_contracts/hypothesis.py and packages/agent_contracts/literature.py before writing hypotheses/<id>/HYPOTHESIS.json or consuming search bridge artifacts.SearchRequestContract for the active research goal and call tools.search_literature(run_dir, request).EvidenceBundleContract. If retrieval_metadata.status is blocked, stop or return a degraded state; do not write a literature-grounded hypothesis.HypothesisContract. Any origin.retrieval_results entries must be derived from the evidence bundle rather than invented in prompt text.hypotheses/<id>/HYPOTHESIS.json, hypotheses/<id>/HYPOTHESIS.md, and hypotheses/<id>/ORIGIN.json.Artifact Rules:
hypotheses/<id>/HYPOTHESIS.json is the authoritative artifact and must serialize the canonical shared contract.ORIGIN.json may mirror the generation payload and grounding notes, but it does not replace the canonical hypothesis artifact.literature/queries/<query_id>/EVIDENCE_BUNDLE.json.origin.strategy is literature_exploration_generation, the canonical hypothesis origin must include non-empty retrieval_results, evidence_bundle_ids, and literature_query_ids derived from the returned evidence bundle; validation fails if these bridge linkages are omitted or point to unknown artifacts.Completion Rule:
Run the full Co-Scientist pipeline for one research run.
Evaluate whether a hypothesis newly entered the current top-k set and update the convergence counter deterministically.
Assemble a dashboard-ready snapshot from the canonical state.
Choose exactly one concrete evolution strategy for the active evolution round.
Run the assumption decomposition and deep verification review for a hypothesis.
Run the iterative evolution loop from the current persisted run state until convergence.