# Hypothesis Review Pipeline > Run the decomposed review pipeline for a single hypothesis and persist each review stage as a structured artifact. - Skill: `panjose/hypothesis-review-pipeline` (Agent Skill) - Install (CLI): `npx skillmds@latest add panjose/hypothesis-review-pipeline` - Raw SKILL.md: https://api.skillmd.com/api/skills/panjose/hypothesis-review-pipeline/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: DevOps & Infra - Author: panjose (https://skillmd.com/u/panjose) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/panjose/hypothesis-review-pipeline --- # hypothesis-review-pipeline Goal: - Run the decomposed review pipeline for a single hypothesis and persist each review stage as a structured artifact. Inputs: - `ResearchPlan` - `MetaReview` - `Hypothesis` - optional resume context from `state/PIPELINE_STATE.json` Outputs: - `hypotheses//REVIEW/INITIAL_REVIEW.json` - `hypotheses//REVIEW/FULL_REVIEW.json` - `hypotheses//REVIEW/DEEP_VERIFICATION.json` - `hypotheses//REVIEW/OBSERVATION_REVIEW.json` - `hypotheses//REVIEW/SIMULATION_REVIEW.json` - `hypotheses//REVIEW/REVIEW_SUMMARY.json` - `literature/queries//*` when full review or deep verification needs external literature evidence - updated `hypotheses//HYPOTHESIS.json` - updated `state/PIPELINE_STATE.json` - updated `state/CURRENT_STAGE.json` Sub-skills: - `hypothesis-initial-review` - `literature-search` - `hypothesis-full-review` - `hypothesis-deep-verification` - `hypothesis-observation-review` - `hypothesis-simulation-review` - `hypothesis-review-summary` Context Loading: - Open `skills/shared-references/schema-index.md`. - Read `packages/agent_contracts/review.py` before writing any per-stage review artifact under `hypotheses//REVIEW/`. - Read `packages/agent_contracts/hypothesis.py` before updating the embedded review payload inside `hypotheses//HYPOTHESIS.json`. - Read `packages/agent_contracts/literature.py` and `skills/shared-references/literature-search-contract.md` before dispatching `hypothesis-full-review` or `hypothesis-deep-verification`. - Read `packages/agent_contracts/pipeline_runtime.py` before updating `state/PIPELINE_STATE.json` or `state/CURRENT_STAGE.json`. - Read `skills/shared-references/codex-reviewer-routing.md` before using any optional Codex reviewer subagent route. - Read `RUN_POLICY.yaml` before dispatching review work so the active `review_rigor` setting is honored. - Read the current hypothesis, research plan, and any optional run-level review guidance before starting the stage sequence. Execution Contract: - This pipeline skill does not own hidden prompt templates. - It orchestrates atomic review skills so the runtime can trace each review stage independently. - Downstream atomic review skills remain responsible for the exact field-level contents of each standalone review artifact. - The canonical `hypotheses//HYPOTHESIS.json` artifact must be updated after every review stage so the embedded `review` payload stays in sync with the per-stage review files. - Use `from tools import sync_hypothesis_review` after each stage artifact write. The canonical synchronization helper is implemented in `packages/run_artifacts/review_sync.py`; do not hand-edit embedded review JSON. - Use `from tools import sync_pipeline_stage_artifacts` as the canonical paired write surface for entering the `Reflection` substage. - Treat `tools.sync_hypothesis_review(run_dir, hypothesis_id)` as a hard gate. If it fails, stop the review sequence immediately instead of continuing with later stages. - On entry, call `tools.sync_pipeline_stage_artifacts(run_dir, current_phase="Reflection", current_skill="hypothesis-review-pipeline")` before starting the review sequence. - When consumed by a host agent, validate both the embedded hypothesis review payload and the per-stage review artifacts. If validation fails, stop and repair the review bundle before running the next stage or returning control. - Full review and deep verification must use `tools.search_literature(run_dir, request)` or an existing matching `EvidenceBundleContract` before making external literature support claims. - The review pipeline must not accept prompt-invented paper metadata or model-memory citations as formal evidence. - The review pipeline must not accept empty completed review artifacts. A completed or passing stage must contain concrete, hypothesis-specific findings that downstream evolution and ranking can use. - Do not use placeholder review phrases such as `Viable evolved hypothesis`, `Refined from parent`, `Must outperform parent`, `Synthesize evolved catalyst`, or `Benchmark against parent` as the substantive content of any completed review artifact. - Codex reviewer subagents are optional. If unavailable, run the same review contract in the main thread and record `reviewerRoute = local_main_thread` in the trace. - If a Codex reviewer subagent is used, record `state/agent_traces/codex//.json`; the trace is audit evidence and must not replace canonical review artifacts. - Reviewer subagents must not write deterministic mechanics artifacts or bypass `tools.sync_hypothesis_review` and schema validation. - Regardless of `review_rigor`, run the full review stack for every hypothesis: - `hypothesis-full-review` - `hypothesis-deep-verification` - `hypothesis-observation-review` - `hypothesis-simulation-review` - `review_rigor` may shorten or deepen the reasoning inside those stages, but it must not disable any review artifact. Execution Steps: 1. Open `skills/shared-references/schema-index.md` and `skills/shared-references/codex-reviewer-routing.md`, then read `packages/agent_contracts/review.py`, `packages/agent_contracts/hypothesis.py`, `packages/agent_contracts/literature.py`, and `packages/agent_contracts/pipeline_runtime.py` before writing any per-stage review artifact, embedded review state, literature evidence artifact, reviewer trace, or run-level stage artifact. 2. Read the research plan, current hypothesis, optional meta-review guidance, and active `RUN_POLICY.yaml`. 3. Before dispatching atomic review work, call `tools.sync_pipeline_stage_artifacts(run_dir, current_phase="Reflection", current_skill="hypothesis-review-pipeline")`. 4. Run `hypothesis-initial-review` and persist the resulting stage artifact. 5. Immediately call `tools.sync_hypothesis_review(run_dir, hypothesis_id)` to synchronize `review.initial_review` back into `hypotheses//HYPOTHESIS.json`. If sync fails, stop here and repair the artifact instead of continuing. 6. Determine the required reasoning depth from `review_rigor`, then run the full remaining atomic review stack for the hypothesis. When `hypothesis-full-review` or `hypothesis-deep-verification` needs external literature evidence, require those skills to call `tools.search_literature(run_dir, request)` or consume an existing matching evidence bundle. 7. After each later review stage, call `tools.sync_hypothesis_review(run_dir, hypothesis_id)` again so the standalone stage artifact and aggregate hypothesis artifact remain consistent. If sync fails, stop before the next stage. 8. Run `hypothesis-review-summary` after the required review stages finish. 9. Run `python -m tools.validation.contract_validation --skill hypothesis-review-pipeline`. If validation fails, stop and repair the review bundle before declaring completion or returning control. Completion Rule: - This skill is complete only when all review-stage artifacts exist, the embedded review payload in `hypotheses//HYPOTHESIS.json` matches those stage artifacts, and the resulting files validate for downstream routing.