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 skills/romiluz13/cc-teams/brainstormingnpx skills add romiluz13/cc-teams --skill brainstorminggit clone --depth 1 https://github.com/romiluz13/cc-teamsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/romiluz13/cc-teams/brainstorming)<a href="https://agentmods.dev/skills/romiluz13/cc-teams/brainstorming"><img src="https://agentmods.dev/badge/skills/romiluz13/cc-teams/brainstorming.svg" alt="Measured on agentmods" height="20"></a>What 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 | $0.00018 | $0.02207 |
| Opus 5 | $0.00009 | $0.01104 |
| Sonnet 5 | $0.00004 | $0.00441 |
| Haiku 4.5 | $0.00002 | $0.00221 |
Grade A, and why
brainstorming 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 4d 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 — 364 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Brainstorming Ideas Into Designs
Overview
Help turn rough ideas into fully formed designs through collaborative dialogue. Don't jump to solutions - explore the problem space first.
Core principle: Understand what to build BEFORE designing how to build it.
Violating the letter of this process is violating the spirit of brainstorming.
The Iron Law
NO DESIGN WITHOUT UNDERSTANDING PURPOSE AND CONSTRAINTS
If you can't articulate why the user needs this and what success looks like, you're not ready to design.
When to Use
ALWAYS before:
- Creating new features
- Building new components
- Adding new functionality
- Modifying existing behavior
- Making architectural decisions
Signs you need to brainstorm:
- Requirements feel vague
- Multiple approaches seem valid
- Success criteria unclear
- User intent ambiguous
Spec File Workflow (Optional)
If user references a spec file (SPEC.md, spec.md, plan.md):
- Read existing spec - Use as interview foundation
- Interview to expand - Fill gaps using Phase 2 questions
- Write back - Save expanded design to same file
# Check for existing spec (permission-free)
Read(file_path="SPEC.md") # or spec.md if that doesn't exist
The Process
Phase 1: Understand Context
Before asking questions:
- Check project state (files, docs, recent commits)
- Understand what exists
- Identify relevant patterns
# Check recent context (permission-free)
Bash(command="git log --oneline -10")
Bash(command="ls -la src/") # or relevant directory
Phase 2: Explore the Idea (One Question at a Time)
Use AskUserQuestion tool - provides multiple choice options, better UX than text questions.
Ask questions sequentially, not all at once.
Question 1: Purpose
"What problem does this solve for users?"
Options format:
A. [Specific use case 1] B. [Specific use case 2] C. Something else (please describe)
Question 2: Users
"Who will use this feature?"
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.
- 4d ago First seen · 364 lines · 18 tokens per session scan A 2da765290e21
brainstorming is a skill published in the GitHub repository romiluz13/cc-teams (5 stars, last pushed 4mo ago), licensed MIT. It adds 18 tokens to every session and 2,207 once invoked, about $0.0001 per session on Opus 5. 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 2026-08-31.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…