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.
git clone --depth 1 https://github.com/KaelSensei/MagicAIBuilderWrote 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/commands/kaelsensei/magicaibuilder/brainstorm)<a href="https://agentmods.dev/commands/kaelsensei/magicaibuilder/brainstorm"><img src="https://agentmods.dev/badge/commands/kaelsensei/magicaibuilder/brainstorm/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/commands/kaelsensei/magicaibuilder/brainstorm"><img src="https://agentmods.dev/badge/commands/kaelsensei/magicaibuilder/brainstorm.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 | $0.00000 | $0.02228 |
| Opus 5.5 | $0.00000 | $0.00891 |
| Sonnet 5.5 | $0.00000 | $0.00446 |
| Haiku 4.5 | $0.00000 | $0.00223 |
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 1mo 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 — 309 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Brainstorm Command – AI-Powered Feature Ideation and Planning
When /brainstorm [topic] is invoked, immediately execute the following steps to collaboratively brainstorm, enhance, and plan new features for the app through an iterative product management workflow.
Step 1: Load Project Context & Follow All Rules
- Assume the project root as the working directory
- Load and strictly follow ALL Cursor rules from
.cursor/rules/*.mdc:security.mdc- Security requirementstechnical-stack.mdc- React Native/SQLite patternsdocumentation.mdc- Documentation update requirementsversion-management.mdc- Git commit/push workflowgeneral-principles.mdc- Project philosophy (simple, offline-first, personal)
- Read relevant documentation:
PROGRESS.md- Current features and completed workREADME.md- App overview and capabilities- Architecture documents - Technical constraints
magic_the_offlining_step_by_step_dev_plan.md- Planned features
- Check current git status and branch
- Understand the app's current state and capabilities
Step 2: Understand the Brainstorming Topic
-
Parse the topic provided after
/brainstorm:- If topic provided: Focus brainstorming on that area (e.g., "search", "deck management", "offline features")
- If no topic: Conduct general brainstorming for the entire app
-
Analyze current app state:
- Review
PROGRESS.mdfor completed features - Identify gaps or areas for improvement
- Consider user pain points based on code structure
- Review technical constraints (offline-first, no backend, SQLite only)
- Review
-
Set brainstorming scope:
- What area needs enhancement?
- What problems should we solve?
- What user workflows could be improved?
Step 3: Initial Feature Analysis
- Review current features:
- List all major features from
PROGRESS.md - Identify strengths and weaknesses
- Note any incomplete or partially implemented features
- List all major features from
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.
- 1mo ago First seen · 309 lines · 0 tokens per session scan A 7ad9fcfd823b
brainstorm is a command published in the GitHub repository KaelSensei/MagicAIBuilder (2 stars, last pushed today), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 2,228 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 2026-08-31.
Other commands, from other repositories
add-endpoint
Scaffold a new API endpoint with full layering.
review
Review code changes against project conventions.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
converge
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.