Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add commands/cl-ml/open-collider/brainstormgit clone --depth 1 https://github.com/CL-ML/open-colliderWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00000 | $0.02607 |
| Opus 5 | $0.00000 | $0.01303 |
| Sonnet 5 | $0.00000 | $0.00521 |
| Haiku 4.5 | $0.00000 | $0.00261 |
Grade A, and why
brainstorm scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 205 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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).
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 3d ago First seen · 205 lines · 0 tokens per session scan A a390b227fb72
brainstorm is a command published in the GitHub repository CL-ML/open-collider (341 stars, last pushed 3mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 2,607 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.