Hypothesis generation pipeline

hypothesis-generation-pipeline

Goal:

• Dispatch one enabled generation strategy for a given ResearchPlan.

Inputs:

• ResearchPlan
• active state/STRATEGY_PLAN.json
• one enabled generation strategy
• optional resume context from state/PIPELINE_STATE.json

Outputs:

• hypotheses/<id>/HYPOTHESIS.json
• hypotheses/<id>/HYPOTHESIS.md
• hypotheses/<id>/ORIGIN.json
• literature/queries/<query_id>/* when hypothesis-generate-literature is selected
• hypotheses/<id>/REVIEW/*.json
• updated meta/INSIGHTS_FROM_REVIEWS.json when the hypothesis is viable
• updated proximity receipt/status artifacts, plus state/PROXIMITY_GRAPH.json when the embedding bridge succeeds
• updated tournaments/*.json
• updated islands/ISLANDS.json
• updated state/PIPELINE_STATE.json

Sub-skills:

• hypothesis-generate-literature
• literature-search
• hypothesis-generate-debate
• hypothesis-generate-assumptions
• hypothesis-review-pipeline
• insights-from-reviews
• hypothesis-proximity-update
• hypothesis-ranking-pipeline

Context Loading:

• Open skills/shared-references/schema-index.md.
• Read packages/agent_contracts/research_plan.py before consuming research_plan/RESEARCH_PLAN.json as the canonical generation brief.
• Read packages/agent_contracts/strategy_plan.py before consuming state/STRATEGY_PLAN.json as the active generation-stage routing input.
• Read packages/agent_contracts/hypothesis.py before dispatching any atomic generation skill that will write hypotheses/<id>/HYPOTHESIS.json.
• Read packages/agent_contracts/literature.py and skills/shared-references/literature-search-contract.md before dispatching hypothesis-generate-literature.
• Read packages/agent_contracts/state.py before assigning or updating islands/ISLANDS.json.
• Read packages/agent_contracts/pipeline_runtime.py before updating state/PIPELINE_STATE.json.
• Read state/STRATEGY_PLAN.json and confirm the current round permits the selected generation strategy.
• Read RUN_POLICY.yaml when review rigor or downstream optional review behavior depends on the effective run policy.

Execution Contract:

• This pipeline skill does not own hidden prompt templates.
• state/STRATEGY_PLAN.json is a routing input to this skill, not a generation-stage artifact that this skill may rewrite ad hoc.
• research_plan/RESEARCH_PLAN.json is a required canonical input. If it is missing or invalid, stop immediately and return control to the top-level workflow or configuration stage instead of attempting generation.
• Append-only routing audit artifacts such as state/STRATEGY_DECISIONS.jsonl remain owned by the top-level orchestration layer and the canonical router surface in python -m tools.policy.plan_strategy <run_dir>.
• It coordinates downstream generation, review, insights, proximity, and ranking skills; those downstream skills remain responsible for the field-level canonical shapes of the artifacts they write.
• The selected generation strategy must be consistent with state/STRATEGY_PLAN.json.
• When the selected generation strategy is literature_exploration_generation, the dispatched hypothesis-generate-literature skill must call tools.search_literature(run_dir, request) and consume a non-blocked EvidenceBundleContract before writing a literature-grounded hypothesis.
• The generation pipeline must not accept prompt-invented literature evidence in place of literature/queries/<query_id>/EVIDENCE_BUNDLE.json.
• On a fresh run, execute one generated hypothesis per selected generation strategy so the initial frontier mirrors the full seed set instead of collapsing to a single synthetic output.
• On a regeneration pass triggered from evolution, execute one generated hypothesis per selected generation strategy and then refresh the evolution plan before continuing.
• After each generated hypothesis is written, immediately run:
  • hypothesis-review-pipeline
  • insights-from-reviews when the hypothesis is viable
  • hypothesis-proximity-update for each viable hypothesis
  • hypothesis-ranking-pipeline
• The generation pipeline must not skip proximity because no embedding vector is already present. The bridge owns provider invocation and records a receipt/status when the provider is disabled, unavailable, invalid, or failed.
• Do not generate, infer, or fabricate embeddings in prompt output. Proceed to ranking through the documented receipt-gated fallback path only after hypothesis-proximity-update has recorded a skipped or failed receipt/status.
• For initial frontier seeding, assign one non-empty island_id to each viable generated hypothesis, then call tools.ensure_run_islands_for_hypotheses(run_dir) before the round is considered complete.
• Newly created seed islands are initialization records only: they must keep decayed_reward = 0.0, decayed_visits = 0.0, and visit_count = 0.
• Initial frontier island assignment is distinct from the later single-island reward / decay mechanics in hypothesis-evolution-loop; do not apply tools.update_single_island_reward(...), tools.update_run_single_island_reward(...), or any manual reward/visit increment during seeding.
• Persist only canonical island fields. Do not add dashboard-derived or router-derived fields such as hypothesis_ids, ucb_score, or strategy_label to islands/ISLANDS.json.

Execution Steps:

1. Open skills/shared-references/schema-index.md, then read packages/agent_contracts/research_plan.py, packages/agent_contracts/strategy_plan.py, packages/agent_contracts/hypothesis.py, packages/agent_contracts/literature.py, packages/agent_contracts/state.py, and packages/agent_contracts/pipeline_runtime.py before consuming generation-stage routing state or performing any directly managed generation-stage write.
2. Read research_plan/RESEARCH_PLAN.json, active state/STRATEGY_PLAN.json, and relevant policy context.
3. If the research plan artifact is missing or invalid, stop immediately. Do not dispatch any generation strategy.
4. Confirm that the selected generation strategy is allowed for the active round.
5. Dispatch exactly one matching atomic generation skill for the current strategy and let that sub-skill write the canonical hypothesis and origin artifacts. For literature_exploration_generation, require the sub-skill to call tools.search_literature(run_dir, request) and preserve the resulting literature artifacts.
6. Run hypothesis-review-pipeline for the newly created hypothesis.
7. If the hypothesis remains viable, run insights-from-reviews.
8. Run hypothesis-proximity-update for each viable hypothesis by calling tools.update_hypothesis_proximity(run_dir, hypothesis_id) through that skill. If the bridge records a skipped, disabled, failed, or provider-unavailable receipt/status, preserve it and continue to ranking without fabricating placeholder embeddings.
9. Run hypothesis-ranking-pipeline.
10. For initial frontier seeding, assign exactly one non-empty island_id to each viable generated hypothesis, call tools.ensure_run_islands_for_hypotheses(run_dir), and verify the resulting canonical islands/ISLANDS.json contains one unvisited island record per viable seed hypothesis.
11. Update state/PIPELINE_STATE.json as required by the top-level workflow and refreshed downstream status.
12. Do not append or rewrite state/STRATEGY_DECISIONS.jsonl from this skill. If the caller needs a routing refresh or new decision record, return control to the top-level orchestration layer or strategy-router.
13. Run python -m tools.validation.contract_validation <run_dir> --skill hypothesis-generation-pipeline before declaring completion.

Completion Rule:

• This skill is complete only when the selected generation strategy has produced exactly one canonical hypothesis candidate, required downstream review and frontier-upkeep work has been dispatched, and any directly managed state or island updates validate.