co-scientist-doctor

Goal:

• Run the Co-Scientist environment diagnostics and explain any failing or warning checks to the user.

Expected input:

• no arguments for the default project-local doctor

Execution steps:

1. Run:

   python -m tools.host.claude_project_cli doctor
   

   or, when machine-readable output is needed:

   python -m tools.host.claude_project_cli doctor --format json
   

2. Read the emitted diagnostic output and summarize:

   • Python version health
   • Python dependency health
   • active environment / conda or virtualenv status
   • dashboard tooling (node, pnpm)
   • dashboard install/build readiness
   • project-local skill installation state
   • how budget, iteration-policy, iteration-band, stop-policy, and human-checkpoint divide responsibility
   • how dashboard ready URLs are resolved after a background bootstrap
   • what inspect_state or validation blocked means operationally

3. If the dashboard checks warn, recommend:

   pnpm --dir apps/dashboard install
   pnpm --dir apps/dashboard build
   

4. If the dashboard checks pass, remind the user:

   • fresh start, run, and resume commands try a background dashboard bootstrap
   • python -m tools.host.claude_project_cli dashboard <run-dir> resolves the ready dashboard URL
   • runs/<run_id>/dashboard/LINKS.md is the human-readable dashboard receipt
   • budget controls per-round intensity, while iteration-policy controls semantic vs capped stopping
   • human-checkpoint=auto means completion-driven runs should not pause after every evolution round
   • inspect_state means review/ranking/evolution artifacts disagree and need repair before resume or overview

5. If the project-local skills are not installed, recommend:

   powershell -File tools/install/install_co_scientist.ps1
   

Rules:

• Prefer conda run -n <env> python ... over conda activate when the user is running commands through Claude Code shells.
• Treat doctor warnings as actionable guidance, not as fatal runtime errors unless the check status is fail.