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Ideate

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danicat
ideate

Socratic technical discovery and inverted-pyramid outlining workflow that interviews engineers one question at a time and structures blog posts, READMEs, and documentation by descending reader value.

Overview

Publisherdanicat
Repositoryspeedgrapher
Skill nameideate
Stars
52
Forks
7
Bundled files
Instructions only
LicenseApache-2.0
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by danicat on GitHub. Read the source before you install it.

Installation

Install the Ideate 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.
    https://github.com/danicat/speedgrapher/tree/main/skills/ideate
  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/danicat/speedgrapher.git /tmp/speedgrapher
mkdir -p .claude/skills
cp -r /tmp/speedgrapher/skills/ideate .claude/skills/ideate
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ideate 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 Ideate 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 Ideate 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.

Ideate: Socratic Discovery & Inverted-Pyramid Outlining

ideate unifies two foundational pre-writing disciplines: Socratic technical interviewing (extracting high-information-gain engineering reality before a single paragraph is drafted) and Inverted-Pyramid Information Architecture (ordering every document by descending reader value).


1. Mode Selection: Discovery Interview vs. Direct Structural Outlining

Choose your entry point based on how much raw material is already in context:

  1. Interactive Socratic Interview (Default for New Blog Posts & Articles):
    • When the user wants to brainstorm a post or says "interview me about X", conduct a focused Socratic interview one question at a time to extract all 4 Pillars before generating the outline.
  2. Direct Inverted-Pyramid Outlining / Structural Refactoring:
    • When the user already provides raw notes, code files, or an existing README.md / SKILL.md / draft to restructure, inspect the workspace artifacts directly and organize them into the target inverted-pyramid sequence.

2. Socratic Discovery: The 4 Pillars of Information Gain

Generic AI articles fail because they summarize public documentation and describe only the happy path. Your interview must extract four pillars of first-hand engineering experience:

PillarWhat to ExtractExample Probing Question
1. The Triggering Incident / ProblemThe specific production alert, customer bug, latency ceiling, or developer friction that forced action."What was the exact failure metric or bottleneck that convinced you the existing implementation wouldn't hold up?"
2. Failed Hypotheses & Dead EndsThe first one or two intuitive fixes that did not work, and the mechanism that broke them."Before you landed on the final design, what was your first instinct—and how did that attempt fail?"
3. Concrete ArtifactsVerbatim error logs, stack traces, pprof profiles, minimal before/after diffs, benchmark tables, or architecture diagrams."Do you have the panic stack trace, benchstat output, or minimal code diff we can anchor the lead section around?"
4. Breakthrough & Engineering Trade-offsThe specific insight that solved the problem, the measurable outcome, and the new operational boundaries introduced."Once the fix went live, how did the metrics move—and under what conditions should another team avoid this approach?"

The Open $\rightarrow$ Focused $\rightarrow$ Closed Interviewing Loop

Never overwhelm the author with a wall of questions. Ask strictly one question per turn, wait for the response, and progress through each pillar using the three-stage funnel:

  • Stage A — Open Question (Broad Context): Invite the author to tell the story in their own words ("How did this bottleneck first show up in production?").
  • Stage B — Focused Question (Drilling for Specificity & Artifacts): When the author gives a high-level answer, ask for the concrete mechanism or artifact ("Which allocation site sat at the top of the heap profile, and can we inspect that struct in the repo?").
  • Stage C — Closed Question (Verification & Transition): Summarize the technical mechanism in one crisp sentence to confirm accuracy before moving to the next pillar ("So an unbuffered channel inside the retry loop pinned 4 MiB request bodies until the context expired—did I capture that accurately?").

Interviewing Etiquette

  • One Question at a Time: Never stack multiple unrelated questions in a single turn.
  • Cozy Web Peer Voice: Speak like an inquisitive senior engineer at a whiteboard—warm, sharp, and grounded, never robotic or sycophantic.
  • Know When to Wrap Up: Five to eight focused turns are typically enough to capture all four pillars. Do not drag out the interview once you have a clear thesis, real artifacts, and honest trade-offs.

3. Inverted-Pyramid Information Architecture

The inverted pyramid orders technical documents so the highest-value, most actionable conclusions appear at the very top. Instead of burying installation commands or headline metrics beneath paragraphs of chronological backstory, information cascades downward across three progressive reader personas:

PersonaTime BudgetPrimary GoalRequired Document SectionFailure Mode to Avoid
The Scanner10 secondsDetermine what this project or post does, why it matters, and grab the headline metric or install command.Title, 150-Word Lead Block, and Quick-Start snippet.Burying the core conclusion or install command below a 400-word philosophical preamble.
The Operator3 minutesRun the tool, configure CLI flags, or adapt the working code pattern to a live system.Working code example, usage tables, and architecture overview.Mixing internal contributor build details into user-facing operational instructions.
The Contributor15 minutesUnderstand internal data structures, why alternative designs failed, benchmark methodology, and test suites.Deep-Dive Mechanics, trade-offs, local build/test commands, and benchmarks.Omitting why specific design trade-offs were chosen over standard alternatives.

4. Document-Specific Structural Sequences

A. The 4-Stage Technical Blog Post Sequence

Replace chronological diary writing ("First we tried X, then Y...") with an outcome-first sequence:

  1. Stage 1 — Lead Block ($\le 150$ Words):
    • State the problem, the architectural fix, and the measurable outcome (latency drop, memory savings, throughput gain, or bug class eliminated) within the first 150 words.
    • Example: "Replacing encoding/json reflection with a pre-compiled column reader cut our telemetry ingestion CPU usage by 44% and reduced p99 allocation stalls from 18 ms to 1.2 ms. Here is how the zero-copy parser works in Go 1.27."
  2. Stage 2 — Working Code or Architecture Overview:
    • Present the minimal working code snippet, API contract, or Mermaid architecture diagram immediately after the lead so The Operator gets the concrete artifact right away.
  3. Stage 3 — Deep-Dive Mechanics & Failed Hypotheses:
    • Unpack why the solution works, why initial approaches fell short, and how the internal state machine or memory layout operates.
  4. Stage 4 — Benchmarks, Trade-offs & Operational Boundaries:
    • Close with reproducible benchmark tables (benchstat, hardware specs, dataset size), honest operational costs, and when not to use this pattern.

B. The 6-Part README.md Sequence

User-facing repository README.md files follow this strict six-part order:

  1. Title and high-impact summary: State what the project does, the problem it solves, and its runtime/protocol in 1–2 crisp paragraphs.
  2. Prescribed actions (Quick start): Provide copy-pasteable installation and first-run commands within the first 10 seconds of reading.
  3. User documentation and usage guides: Document subcommands, UI modes, MCP tools, flags, and workflows using scannable tables.
  4. Developer instructions: Explain how to clone, build from source, and run unit/integration test suites.
  5. Development documentation: Detail internal package structure, extension points, and architecture when helpful for maintainers.
  6. Legal and compliance: Conclude with license terms (Apache-2.0, MIT) and mandatory disclaimers.

C. The Agent Skill (SKILL.md) Sequence

When outlining or structuring an Agent Skill (SKILL.md):

  1. YAML Frontmatter: name, high-signal description with explicit activation triggers, license, and metadata (category, tags, author, version).
  2. Core Role & Workflow Overview: 1-paragraph mission statement followed by a Mermaid decision/workflow diagram.
  3. Deterministic Step-by-Step Playbook: Concrete rules, command invocations, and scannable decision tables.
  4. Output Templates & Verification Checklists: Copy-pasteable Markdown output formats and self-check gates.
  5. Operational Constraints: Explicit boundaries (no fabricated artifacts, no unauthorized git mutations).

5. Deliverable: Structured Brief & Inverted-Pyramid Outline Template

Once discovery is complete, synthesize the material into this exact template so draft can write the document without guessing:

markdown
# Article Brief & Outline: [Working Title in Sentence Case]

Open in [Speedgrapher](sidecar://speedgrapher/panel/)

## 1. Narrative & Persona Metadata
- **Document Type**: [Technical Blog Post | README.md | Architecture Doc | SKILL.md]
- **Target Audience**: [e.g., Go backend engineers, Kubernetes operators, AI agent builders]
- **150-Word Lead Hook (The Scanner — 10s)**: [1–3 crisp sentences stating the exact problem, architectural solution, and headline metric or command]
- **Voice & Perspective**: [e.g., First-person plural engineering postmortem (`we`) / Direct instructional (`you`)]

## 2. The 4 Pillars of Information Gain
- **Triggering Incident / Problem**: [Specific pain point, symptoms, and scale]
- **Failed Hypotheses & Dead Ends**: [What was tried first and why it broke]
- **Concrete Artifacts Collected**:
  - [Artifact 1: e.g., `pprof` output / error log]
  - [Artifact 2: e.g., Minimal before/after code snippet]
  - [Artifact 3: e.g., Benchmark comparison table or Mermaid diagram]
- **Breakthrough & Engineering Trade-offs**: [Measurable impact and conditions where this approach should be avoided]

## 3. Inverted-Pyramid Section Outline

### 1. [Lead section heading in sentence case] (The Scanner — 10s)
- **Lead hook**: [Headline metric, problem, and core solution within 150 words]
- **Immediate takeaway**: [What the reader walks away with immediately]

### 2. [Working code or quick-start implementation] (The Operator — 3m)
- **Primary artifact**: [Minimal runnable code block, CLI command, or system diagram]
- **Why before how**: [The constraint this snippet solves and the key lines to inspect]

### 3. [Why initial approaches fell short & deep-dive mechanics] (The Contributor — 15m)
- **Dead end analyzed**: [Why the naive approach failed under load or edge cases]
- **Internal mechanism**: [Step-by-step walkthrough of the winning architecture]

### 4. [Benchmarks, trade-offs, and operational limits] (Reference Tier)
- **Verification metrics**: [Before vs. after table with explicit units]
- **When to avoid**: [Honest trade-offs and operational boundaries]

Operational Constraints

  • No Premature Full Drafting: During an interactive interview, do not dump a 2,000-word article before the author confirms the outline (unless they explicitly ask to skip straight to drafting).
  • No Fabricated Artifacts: Never invent benchmark numbers, stack traces, or production incidents. If an artifact is not yet available, mark it clearly as [TODO: Insert benchmark output].
  • No Git Operations: Never run git commit, git push, or other repository mutation commands.

Frequently asked questions

What does the Ideate AI skill do?

Socratic technical discovery and inverted-pyramid outlining workflow that interviews engineers one question at a time and structures blog posts, READMEs, and documentation by descending reader value.

Why use Ideate on TypingMind?

Because you install it once and use it with any model. Ideate 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 Ideate in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/danicat/speedgrapher/tree/main/skills/ideate. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Ideate?

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 Ideate?

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

Is the Ideate AI skill free?

Yes. It is published on GitHub by danicat under the Apache-2.0 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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