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Brainstorm

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oliver-kriska
brainstorm

Brainstorm Elixir/Phoenix features — explore ideas, compare approaches, gather requirements. Use when vague idea, not sure how to approach, or want to discuss before plan.

Overview

Publisheroliver-kriska
Repositoryclaude-elixir-phoenix
Skill namebrainstorm
Stars
555
Forks
40
Bundled files
2
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by oliver-kriska on GitHub. Read the source before you install it.

Installation

Install the Brainstorm AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/oliver-kriska/claude-elixir-phoenix.git /tmp/claude-elixir-phoenix
mkdir -p .claude/skills
cp -r /tmp/claude-elixir-phoenix/plugins/elixir-phoenix/skills/brainstorm .claude/skills/brainstorm
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Brainstorm in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Brainstorm on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Brainstorm is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Brainstorm — Adaptive Requirements Gathering

Interactive interview → research → synthesis loop. Produces structured interview.md that /phx:plan detects and consumes (skipping clarification).

Usage

text
/phx:brainstorm Add some kind of notification system
/phx:brainstorm Improve authentication security
/phx:brainstorm                    # starts with open question

Workflow

/phx:brainstorm {topic}
    |
    v
[INTERVIEW] ←──────────────────┐
    |                           |
    v (sufficient OR user exit) |
[DECISION POINT]                |
    ├─ Research ──→ [RESEARCH] ─┘
    ├─ Continue interview ──────┘
    ├─ Make a plan ──→ STOP (suggest /phx:plan {slug})
    ├─ Store & exit ──→ STOP (artifacts saved)
    └─ Discuss ──→ freeform ──→ [DECISION POINT]

Phase 1: Adaptive Interview

Create .claude/plans/{slug}/ directory. Start asking ONE question at a time.

Coverage Dimensions

Track coverage across 6 dimensions (0=uncovered, 1=partial, 2=sufficient). Ask Scope early — for "optimize X" topics, ask about boundaries (upstream OK? Local-only? CI vs dev?) before research, not during.

DimTargetSufficient signal
WhatSpecific behavior/featuresConcrete verbs, not "some kind of"
WhyProblem solved, user needClear benefit stated
ScopeIn/out boundariesExplicit exclusions stated
WhereModules, contexts, pagesFile paths or context names mentioned
HowApproach, constraintsAt least one concrete constraint
EdgeError states, scale, auth2+ edge cases identified

Interview is "sufficient" when total score >= 8 out of 12.

Context-Aware Questioning

Before each question, run a brief codebase scan on topics the user mentioned:

  1. User mentions a topic (e.g., "notifications") → run Grep/Glob for related patterns
  2. Use scan results to ground your next question in what actually exists
  3. Unknown/niche topic → suggest research pause before continuing

Signal Detection

  • Vague answer ("maybe", "not sure") → probe deeper on same dimension
  • Niche topic mentioned → "This involves {X}. Want me to research it first?"
  • Detailed answer covering 3+ dimensions → mark all covered, advance
  • No new coverage for 2 consecutive questions → suggest moving to Decision Point

Phase 2: Decision Point

MANDATORY: Write interview.md FIRST, then use AskUserQuestion. Never let the conversation flow past this point without a formal choice.

  1. Write current state to .claude/plans/{slug}/interview.md

  2. Show coverage summary: "Coverage: What 2/2 | Why 2/2 | Scope 1/2 | ..."

  3. Use AskUserQuestion with EXACTLY these options (4 max — the tool's hard limit; the auto-added "Other" covers freeform discussion):

    • Research — search codebase + internet for approaches (2 agents)
    • Continue interview — ask more questions
    • Make a plan — I'll suggest: /phx:plan .claude/plans/{slug}/interview.md
    • Store & exit — save everything, come back later
  4. Wait for user response. Do NOT proceed without explicit choice

AskUserQuestion discipline: decisions only, never narration or rhetorical check-ins. Every option states concrete impact (what happens, what it costs) so the user can pick without follow-up questions.

Phase 3: Research (Diverge → Evaluate → Converge)

First cycle: MAX 2 agents — keep it fast (~2-3 min). Spawn in ONE Tool Use block with run_in_background: true:

  • phoenix-patterns-analyst: "How does this codebase handle {topics}?" Write to .claude/plans/{slug}/research/codebase-scan.md
  • web-researcher: "Elixir/Phoenix approaches to {topics}" Return 500-word summary

Do NOT spawn additional specialist agents in the first cycle. If user wants deeper investigation, they pick "More research" at the next Decision Point — then spawn focused agents for specific questions.

Evaluate — for each approach found:

  • Thesis: why it works for THIS codebase
  • Antithesis: why it might NOT work (scale, complexity, pattern conflicts)

Converge — present 2-3 approaches with honest trade-offs. Do NOT recommend one. Return to Decision Point (AskUserQuestion).

See ${CLAUDE_SKILL_DIR}/references/research-integration.md for details.

Iron Laws

  1. NEVER auto-transition to /phx:plan — always present as option, let user choose
  2. ONE question at a time — never dump a question list
  3. Always write artifactsinterview.md is the contract with /phx:plan
  4. Scan codebase between questions — every question must be context-aware
  5. AskUserQuestion at EVERY decision point — never flow past without formal choice. This is the most critical law. After interview, after research, after discuss — ALWAYS present options via AskUserQuestion. Never let conversation skip the checkpoint
  6. STOP after presenting options — do not proceed without user input
  7. MAX 2 agents in first research cycle — deeper dives are subsequent cycles. User picks "More research" to go deeper, not the skill

Integration

/phx:brainstorm ──→ interview.md ──→ /phx:plan (skips clarification)
                                 ──→ /phx:plan --existing (deepens)
                                 ──→ stored for later session

Position: optional upstream of /phx:plan in workflow cycle.

References

  • ${CLAUDE_SKILL_DIR}/references/interview-techniques.md — coverage scoring, question templates, scan patterns, signal detection, interview.md format
  • ${CLAUDE_SKILL_DIR}/references/research-integration.md — diverge-evaluate-converge, agent spawn templates, approach presentation format

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Brainstorm AI skill do?

Brainstorm Elixir/Phoenix features — explore ideas, compare approaches, gather requirements. Use when vague idea, not sure how to approach, or want to discuss before plan.

Why use Brainstorm on TypingMind?

Because you install it once and use it with any model. Brainstorm is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Brainstorm in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/oliver-kriska/claude-elixir-phoenix/tree/main/plugins/elixir-phoenix/skills/brainstorm. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Brainstorm?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Brainstorm?

As many as you like. As long as a model supports skills, you can use Brainstorm with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Brainstorm AI skill free?

Yes. It is published on GitHub by oliver-kriska under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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