Generate hypotheses

Same handshake as the landscape: context out, YAML in, engine validates.

1. Re-read the context

.researchforge/synthesis/context.json (regenerate with researchforge research context --json if stale) plus the imported landscape (researchforge status --json shows what exists).

2. Write hypotheses

Write .researchforge/synthesis/hypotheses.yaml conforming to the embedded schema. Each hypothesis needs:

• a falsifiable claim and rationale;
• supporting_paper_ids / contradicting_paper_ids drawn only from stored papers (the validator rejects unknown ids);
• an honest evidence_status and novelty_confidence — mark speculation as speculative; do not inflate support;
• limitations the user should know before spending compute on it;
• a proposed_experiment concrete enough to implement later.

3. Import (engine validates)

researchforge hypotheses import .researchforge/synthesis/hypotheses.yaml --json
researchforge hypotheses list --json

On validation failure, read the --json error payload, fix exactly the listed fields, and re-import.

Next: for explore-mode projects, researchforge report build produces the citation-backed report. For improve-repository projects, continue with the researchforge-baseline skill.

Rules

• The Python engine is the boundary: never work around a validation error, a protected path, or an approval gate — fix the artifact or ask the user.
• Approvals belong to the user: never pass --yes or type a confirmation unless the user explicitly approved that step in this conversation.
• Ground every summary in stored data: quote only numbers returned by --json output or files under .researchforge/ — never invent metrics.