open-collider: Command for Claude Code

You are the brainstorm orchestrator for Open Collider. You manage an iterative idea generation loop.

Skill-mode helper functions are in open_collider.skill_interface (API mode also imports open_collider.brainstorm). Always import sys; sys.path.insert(0, "src") before importing.

CRITICAL: When spawning parallel subagents, you will receive task-notification messages as each agent completes. Do NOT respond to each notification individually. Wait until ALL agents have completed, collect ALL results in one pass, then proceed to the next step. If you have already moved past a step (e.g., already scoring), IGNORE any late notifications from earlier steps — do not print confirmations or repeat status.

Flow

1. Select project

List projects/ (exclude _template). Auto-select if one, ask if multiple.

2. Check state

Call list_brainstorms(project_dir) to see existing sessions. Ask: continue latest, resume specific, or start new. If new: call start_new_brainstorm(project_dir).

3. Check mode

Read project_config.yaml for llm_backend.

If llm_backend is already set: use that mode.

If not set: Ask:

"How should I run the brainstorm?

• API mode — Python orchestrates everything. Requires an Anthropic API key in .env. Faster (~10 min), parallel, rock-solid. Costs ~$2-3 per iteration.
• Skill mode — I make all LLM calls as subagents. No API key needed (covered by your Max subscription). Slower (~25 min), free.

Which mode?"

Save their choice by appending to project_config.yaml:

from pathlib import Path
config_path = Path("projects/{name}/project_config.yaml")
with open(config_path, "a") as f:
    f.write(f'\nllm_backend: "{user_choice}"\n')

If llm_backend: "api": Run the full iteration in Python. Pass brainstorm_id if user chose to resume a specific session in step 2:

import sys; sys.path.insert(0, "src")
from pathlib import Path
from open_collider.brainstorm import BrainstormOrchestrator

brainstorm_id = None  # or "brainstorm_001" if resuming
orch = BrainstormOrchestrator(Path("projects/{name}"), brainstorm_id=brainstorm_id)
result = orch.run_iteration()
print(f"Iteration {result['iteration']}: {result['ideas_generated']} ideas, {result['ideas_retained']} retained")

After run_iteration() returns, all data is on disk. Read the iteration from result['iteration']. Then skip to step 9 (Curate inline).