co-scientist-start

co-scientist-start

Goal:

• Start one Co-Scientist run from Claude Code using a natural-language goal or an imported brief.

Expected input:

• either a natural-language research goal
• or a brief path such as notes/drug_resistance_brief.md
• or no explicit arguments, which should trigger a short guided intake

Guided intake:

• If the user only says /co-scientist-start, ask at most four short questions:
  1. What is the research goal?
  2. Should the run favor exploration or grounded progress?
  3. Should the run use a completion-driven search or a capped low-cost iteration budget?
  4. Is there an existing brief, paper note, or other file to import?
• Do not ask for low-level mechanics settings such as num_debaters, elo_k_factor, or ucb_exploration_constant.
• If the user explicitly asks for stricter or lighter review, capture that as a high-level review override for critique depth without replacing the iteration-strategy question or disabling any review stage.
• After collecting the answers, render a confirmation summary before you create any files.

Execution steps:

1. If the user supplied a natural-language goal, convert it into --goal "<goal>".

2. If the user supplied a brief file, convert it into --brief <path>.

3. When the user also specified high-level controls such as exploration, iteration strategy, or review rigor, pass them through as:

   --exploration <value> --generation-bias <value> --review <value> --budget <value> --evolution <value> --stop-policy <value> --iteration-policy <value> --iteration-band <value> --human-checkpoint <value>
   

   Convert Claude-style key: value controls into real CLI flags before execution. For example:

   • exploration: aggressive -> --exploration aggressive
   • review: strict -> --review strict
   • iteration policy: capped -> --iteration-policy capped
   • iteration band: 6 10 -> --iteration-band 6_10
   • human checkpoint: before overview -> --human-checkpoint before_overview

4. Before creating the run, render a summary with:

   python -m tools.host.claude_project_cli start --goal "<goal>" --skill co-scientist-pipeline --summary-only
   

   or the equivalent brief-based variant.

5. Show the returned summary to the user and wait for confirmation.

   • When you present the summary, also tell the user that the run-local dashboard receipt will be written to:
     • runs/<run_id>/dashboard/LINKS.md
     • runs/<run_id>/dashboard/LINKS.json
   • Tell the user that /co-scientist-dashboard <run-dir> is the ready-link follow-up when the background bootstrap has not finished yet.

6. After confirmation, run:

   python -m tools.host.claude_project_cli start --goal "<goal>" --skill co-scientist-pipeline
   

   or the equivalent brief-based variant.

7. Read the emitted handoff artifact:

   • runs/<run_id>/state/HOST_AGENT_HANDOFF.json

8. Read the CLI JSON result and the run-local dashboard receipt artifacts:

   • runs/<run_id>/dashboard/LINKS.md
   • runs/<run_id>/dashboard/LINKS.json

9. If the CLI JSON contains dashboardLinks:

   • If dashboard.status is running, return dashboardLinks.dashboard as the primary dashboard URL and include the deep links.
   • If dashboard.status is starting, immediately run:

   python -m tools.host.claude_project_cli dashboard <run-dir>
   

   • Read the refreshed CLI JSON result plus runs/<run_id>/dashboard/LINKS.md.
   • If runtime.status is now running, return the refreshed links.dashboard URL as the primary dashboard URL and include the deep links.
   • If runtime.status is still starting, tell the user that the dashboard is still booting, point them to runs/<run_id>/dashboard/LINKS.md, and include the retry command:

   /co-scientist-dashboard <run-dir>
   

10. Open the canonical workflow and shared contracts:

• skills/co-scientist-pipeline/SKILL.md
• skills/shared-references/artifact-contract.md
• skills/shared-references/state-contract.md
• skills/shared-references/integration-contract.md
• skills/shared-references/execution-modes.md

1. Continue execution from the canonical repository-local skills tree instead of inventing a parallel flow. If the first refreshed routing plan returns run_configuration, execute research-config before any generation work.
2. After each major phase write, run: