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Topic Brainstormer

Community
notque
topic-brainstormer

Generate blog topic ideas: problem mining, gap analysis, expansion.

Overview

Publishernotque
Repositoryvexjoy-agent
Skill nametopic-brainstormer
Stars
443
Forks
50
Bundled files
3
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.

  • 3 bundled files

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

  • Open source

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

Installation

Install the Topic Brainstormer 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/notque/vexjoy-agent/tree/main/skills/content/topic-brainstormer
  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/notque/vexjoy-agent.git /tmp/vexjoy-agent
mkdir -p .claude/skills
cp -r /tmp/vexjoy-agent/skills/content/topic-brainstormer .claude/skills/topic-brainstormer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Topic Brainstormer 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 Topic Brainstormer 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 Topic Brainstormer 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.

Topic Brainstormer

Generate blog topic ideas that align with a content identity built around solving frustrating technical problems. Three phases: ASSESS, DECIDE, GENERATE. Every topic must pass the three-question content quality filter before presentation.

Deep References

SignalLoadContent
Filtering topics: three-question test, category examplesreferences/content-filter.mdFull quality filter with green/red/yellow topic categories, salvage patterns
Scoring: impact, vex, resolution matrixreferences/priority-scoring.mdRubrics per dimension, score ranges, calibration, worked examples
Mining: problem sources, gap analysis, expansionreferences/topic-sources.mdMining prompts, signal strength, gap types, expansion strategies

Phase 1: ASSESS

Gather context about existing content and available sources.

  1. Read all posts in the content directory. Document post count, content clusters, technologies covered, last post date.
  2. Identify available sources: problem mining (recent debugging, errors, config struggles), gap analysis (cross-references leading nowhere), tech expansion (adjacent technologies).
  3. Extract all "see also" and cross-reference mentions. Flag any pointing to content that does not exist.

Gate: Content landscape documented, at least 2 sources identified with material.

Phase 2: DECIDE

Generate candidates and filter through the content quality test.

  1. Mine 5-10 raw candidates from at least 2 sources. Capture: source type, raw topic, initial vex signal.
  2. Apply the content quality filter -- every candidate must answer YES to all three:
    • Was there genuine frustration? Real time lost, failed attempts, unclear docs, unexpected behavior.
    • Is there a satisfying resolution? Clear fix, understanding gained, prevention strategy, "a-ha moment."
    • Would this help others? Reproducible problem, actionable solution, relatable frustration.
  3. Reject failing candidates. Document each rejection: topic, failed question, reason.

Failure modes to catch:

  • Tutorial-only topics: "How to Set Up X" with vex listed as "learning a new tool" is not genuine frustration. Find the specific friction. "Hugo Local Build Works But Cloudflare Deploy Fails" has real vex.
  • Opinion without experience: "Why Go Is Better Than Python" is debate, not experience. Ground in measurement.

Gate: At least 3 candidates pass. Fewer than 3 -- return to Step 1 with different sources.

Phase 3: GENERATE

Score, prioritize, and present the filtered list.

Scoring: Priority = Impact(1-5) x Vex(1-5) x Resolution(1-5). Thresholds: 60-125 HIGH, 30-59 MEDIUM, 15-29 LOW, 1-14 SKIP.

Titles: Replace vague categories with failure-mode titles. Bad: "Kubernetes Networking Issues". Good: "Pod-to-Pod Traffic Works But Service Discovery Fails".

Output format: Grouped by priority tier. Each topic: title, vex, joy, content cluster fit, word estimate, score breakdown. End with recommendations: top pick, quick win, deep dive.

Tie-breaking: prefer topics that fill an existing gap, complement recent posts, use already-covered technologies, have clearer narrative structure.

Gate: All topics scored, prioritized, presented with recommendations.

Angle Lenses Mode

When the task asks for angles on a single topic (not fresh candidates), apply these six lenses instead of the content quality filter:

LensQuestion
Perspective shiftWhose view changes the story?
Ladder of abstractionOne rung up (the trend) or down (the single case)?
News valuesWhich value carries it: conflict, proximity, novelty, impact, human interest?
Data angleWhat does the dataset say that the narrative misses?
ContrarianWhat if the consensus framing is wrong?
Timeliness pegWhy now? What event or deadline makes this urgent?

Rules: one lens per kept angle (distinctness is structural). Each kept angle must pass the "so what?" gate -- one sentence naming a concrete reader payoff. Log refused angles with reason.

Error Handling

ErrorCauseSolution
No existing postsContent directory emptyFocus on problem mining; ask about recent debugging sessions
All candidates fail filterSources lack frustration signalsAsk probing questions: "What broke recently?" Shift sources.
Topic too broadCategory, not a specific problemBreak into failure modes: "[A] works but [B] fails"
Resolution unclearOngoing issue, no fix yetDefer until resolved; or assess if "understanding the workaround" suffices

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 Topic Brainstormer AI skill do?

Generate blog topic ideas: problem mining, gap analysis, expansion.

Why use Topic Brainstormer on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/notque/vexjoy-agent/tree/main/skills/content/topic-brainstormer. 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 Topic Brainstormer?

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 Topic Brainstormer?

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

Is the Topic Brainstormer AI skill free?

Yes. It is published on GitHub by notque 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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