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 skills add TechNickAI/ai-coding-config --skill brainstorminggit clone --depth 1 https://github.com/TechNickAI/ai-coding-configWrote 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/technickai/ai-coding-config/brainstorming)<a href="https://agentmods.dev/skills/technickai/ai-coding-config/brainstorming"><img src="https://agentmods.dev/badge/skills/technickai/ai-coding-config/brainstorming/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/technickai/ai-coding-config/brainstorming"><img src="https://agentmods.dev/badge/skills/technickai/ai-coding-config/brainstorming.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00029 | $0.01095 |
| Opus 5.5 | $0.00012 | $0.00438 |
| Sonnet 5.5 | $0.00006 | $0.00219 |
| Haiku 4.5 | $0.00003 | $0.00110 |
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 25d 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 — 121 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Core principle: Ask questions to understand, present options to explore, validate sections to refine.
Skip for clear mechanical tasks with obvious solutions, well-defined requirements with standard implementations, or simple bug fixes.
Ask questions one at a time to refine the idea. Use multiple choice when possible - easier to answer than open-ended. Focus on understanding purpose (what problem does this solve?), constraints (what limits the solution?), and success criteria (how do we know it works?).
One question per message. If a topic needs more exploration, break it into multiple questions. Don't overwhelm with a list of questions.
Example pattern: "I see three main approaches:
-
Direct integration - Fast to implement but creates coupling. Good if this is temporary.
-
Event-driven - More flexible, better separation, but adds complexity. Worth it if we'll extend this.
-
Separate service - Maximum isolation, easier to scale, but operational overhead. Overkill unless we need independent scaling.
I'd recommend #2 (event-driven) because the requirements suggest we'll add features here, and the loose coupling will make that easier. What do you think?"
Present options before recommendation. LLMs process information sequentially - showing options first lets them fully consider each alternative before being influenced by a recommendation. The recommendation comes after all options have been presented.
Make a clear recommendation - pick one approach and explain why it fits best. Don't hedge or suggest combining approaches.
Avoid defaulting to hybrid approaches. Hybrid solutions are rarely the right answer. They often combine the complexity of multiple approaches without clear benefits. Only suggest a hybrid if there's a specific, compelling reason why a pure approach won't work.
Structure alternatives clearly - each option should be distinct with clear tradeoffs. If options are too similar, you haven't explored the design space enough.
Explain the choice criteria - make explicit what factors led to your recommendation (simplicity, performance, maintainability, etc.). This helps validate whether the recommendation aligns with priorities.
Let the human partner react after your recommendation. They may have constraints or priorities you didn't consider.
Ask after each section whether it looks right. Be ready to go back and clarify if something doesn't make sense.
This incremental validation catches misunderstandings early before you've written a complete design document.
Commit the design document to git so it's tracked with the project.
If continuing to implementation, ask whether to proceed. Set up an isolated workspace for development (git worktree or feature branch). Create a detailed implementation plan breaking the design into concrete tasks.
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.
- 25d ago First seen · 121 lines · 29 tokens per session scan A 218935222e55
brainstorming is a skill published in the GitHub repository TechNickAI/ai-coding-config (24 stars, last pushed 26d ago), licensed MIT. It adds 29 tokens to every session and 1,095 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-09-22.
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