research-config

name	research-config
description	Generate a `ResearchPlan` from `input.md` or equivalent raw research input.

research-config

Goal:

• Generate a ResearchPlan from input.md or equivalent raw research input.

Inputs:

• input.md or equivalent raw research brief text
• optional state/START_REQUEST.json
• optional RUN_POLICY.yaml

Outputs:

• research_plan/RESEARCH_PLAN.json
• research_plan/RESEARCH_PLAN.md

Context Loading:

• Read input.md first. Treat it as the canonical user brief for the run.
• If state/START_REQUEST.json exists, use it only as auxiliary context for how the run was started. Do not let it override the actual brief text in input.md.
• If RUN_POLICY.yaml exists, use it only as supporting context for expected run style or emphasis. Do not copy policy values into the research goal unless the brief implies them.
• Extract three things from the research input:
  • the primary research_goal
  • preferences that define what a strong hypothesis should optimize for
  • constraints that all downstream hypotheses must satisfy

Execution Prompt Contract:

• System Intent:
  • You are the run's research-plan structuring layer.
  • Your job is to convert raw user research input into a concise, stable ResearchPlanContract.
• Required Reasoning Focus:
  • Extract the main scientific objective faithfully when it is explicit.
  • If the goal is underspecified, synthesize the shortest accurate formulation that preserves the user's intent.
  • Derive preferences as evaluation criteria for hypothesis quality.
  • Derive constraints as hard boundaries for downstream generation and review.
  • If preferences or constraints are not explicit, infer only reasonable defaults from the research domain and stated goal.
• Do Not Do:
  • Do not invent specific scientific facts that are not present or reasonably implied.
  • Do not turn broad domain assumptions into narrow claims unless the brief clearly supports them.
  • Do not emit verbose analysis or chain-of-thought style discussion.
  • Do not exceed five preferences or five constraints.
• Output Shape:
  • Produce a canonical ResearchPlanContract.
  • research_goal must be a single natural-language string, ideally no more than three sentences.
  • Each preferences and constraints item must be short, specific, and directly useful to downstream skills.

Execution Steps:

1. Open skills/shared-references/schema-index.md, then read packages/agent_contracts/research_plan.py before writing research_plan/RESEARCH_PLAN.json.
2. Read input.md.
3. If present, read state/START_REQUEST.json and RUN_POLICY.yaml as contextual hints only.
4. Identify the main research objective.
5. Extract or infer up to five preferences.
6. Extract or infer up to five constraints.
7. Write the canonical research_plan/RESEARCH_PLAN.json.
8. Write the companion research_plan/RESEARCH_PLAN.md.
9. Run validation before declaring the skill complete.

Artifact Rules:

• research_plan/RESEARCH_PLAN.json must validate against the canonical shared ResearchPlanContract.
• research_plan/RESEARCH_PLAN.md must remain a human-readable rendering of the same plan, not a divergent summary.
• The JSON artifact is authoritative; the Markdown file is a companion view.

Completion Rule:

• This skill is complete only when research_plan/RESEARCH_PLAN.json and research_plan/RESEARCH_PLAN.md both exist and the JSON artifact is valid for downstream consumption.