co-scientist-start

Goal:

• Create a Co-Scientist run, bootstrap the canonical host-agent pipeline, and continue from the repository workflow skills.

Expected input:

• a natural-language research goal
• or a brief path
• or no explicit arguments, which should trigger a short guided intake

Execution steps:

1. If the user only invokes $co-scientist-start, ask only for the missing high-level run inputs: goal, exploration preference, iteration policy, and optional brief path.
2. Convert high-level controls into explicit CLI flags. Do not pass free-form key: value text when a real flag exists.
3. Before creating files, run:

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

Use the equivalent --brief <path> command for imported briefs.

1. Show the returned summary and wait for user confirmation.

• 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.

1. After confirmation, run:

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

1. Read the emitted runs/<run_id>/state/HOST_AGENT_HANDOFF.json.
2. Read the CLI JSON result and the run-local dashboard receipt artifacts:

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

1. 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.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>

1. Open skills/co-scientist-pipeline/SKILL.md and the listed shared references from the handoff.
2. Continue execution from the canonical repository-local workflow. If routing returns run_configuration, run research-config and validate research_plan/RESEARCH_PLAN.json before generation.
3. After major phase writes, run:

   python -m tools.validation.contract_validation runs/<run_id> --skill co-scientist-pipeline

Rules:

• Use tools.host.project_cli; do not call the Claude-specific CLI from Codex entry skills.
• Treat skills/ as canonical and .agents/skills/ as the installed Codex discovery surface.
• Do not invent run artifacts. Read the CLI JSON and run-local receipts.
• Treat runs/<run_id>/dashboard/LINKS.md as the human-readable dashboard receipt and runs/<run_id>/dashboard/LINKS.json as the machine-readable receipt.
• Fresh runs normally pass through the explicit Configuration stage first so research-config can materialize research_plan/RESEARCH_PLAN.json.
• Under completion_driven + auto, continue until the routing plan reaches overview, a configured checkpoint, or a blocking validator/safety state.
• If you must stop before convergence or a terminal route, tell the user the run is paused, current convergence has not been reached, persisted state is resumable, and the next recommended action is continue evolution through $co-scientist-resume <run-dir> or an explicit continue request.