Thinking Triz logo

Thinking Triz

CommunityPopular
tjboudreaux
thinking-triz

When two design requirements seem mutually exclusive, name the contradiction, separate conflicting states, then invent a concrete no-compromise resolution.

Overview

Publishertjboudreaux
Repositorycc-thinking-skills
Skill namethinking-triz
Stars
1.3K
Forks
158
Bundled files
Instructions only
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.

  • Self-contained

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

  • Open source

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

Installation

Install the Thinking Triz 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/tjboudreaux/cc-thinking-skills.git /tmp/cc-thinking-skills
mkdir -p .claude/skills
cp -r /tmp/cc-thinking-skills/skills/thinking-triz .claude/skills/thinking-triz
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Thinking Triz 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 Thinking Triz 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 Thinking Triz 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.

TRIZ

Resolve technical design contradictions without midpoint compromise. Name the conflict, separate the opposing states, and transform the system so both benefits hold.

When to Use

  • Architecture, API, or system parameters pull in opposite directions (stable vs evolving, fresh vs cached, strict vs frictionless).
  • You are about to accept a trade-off because "you can't have both."
  • Every candidate solution shares the same structural weakness—the conflict is in the requirements, not the options list.

When NOT to Use

  • One option is simply better under stated constraints → pick it; do not manufacture a contradiction.
  • A cheap measurement shows which side actually matters → measure instead of inventing a separation.
  • A standard pattern already resolves it (cache-aside, CQRS, feature flags, versioning) → apply the pattern directly.
  • Non-technical people/org conflicts → out of scope; separation targets system parameters.
  • Ordinary prioritization or resource allocation without opposing states of the same parameter.

Procedure

  1. Name the contradiction in template form. Write: "We need [PARAMETER] to be [STATE_1] for [BENEFIT_1] BUT [STATE_2] for [BENEFIT_2]." If this form fails, stop—use another method.
  2. State the ideal final result (IFR). Describe both benefits held with minimal new machinery and no permanent midpoint sacrifice.
  3. Try separation before invention. Test, in order: time (different moments), space (different components/layers), condition (context, load, risk), scale (interface vs implementation, aggregate vs element). Keep the first separation that delivers both benefits without hidden compromise.
  4. If separation fails, apply an inventive transform. Prefer resource-light moves: segmentation, preliminary action, inversion (push/pull), intermediary, copying/replication, dynamization (flags/config), another dimension (metadata/versioning/events). Scan only principles that map to the named parameter conflict.
  5. Reuse existing resources first. Before adding services or stores, check data, traffic, headers, schedulers, and side effects already present that can carry the separation.
  6. Lock a concrete resolution and stop. Specify the design change, where each state lives, and how both benefits are preserved. Stop when a no-compromise design is stated, or when honest analysis shows only a constrained trade-off remains—then document the residual trade-off explicitly rather than forcing TRIZ theater.

Output

Emit a TRIZ resolution:

  • contradiction: parameter / state_1+benefit_1 / state_2+benefit_2
  • ideal_final_result: both benefits without permanent compromise
  • separation_tried: time | space | condition | scale → result each
  • inventive_move: principle or pattern used if separation alone was insufficient (or none)
  • resources_reused: existing capabilities leveraged
  • resolution: concrete design decision and where each state holds
  • residual_tradeoff: none, or the honest remaining compromise

Verification

  • Template gate: if the contradiction cannot be written in the required form, do not apply TRIZ.
  • No-compromise check: a pure midpoint on the trade-off curve without a separation or inventive move is a failed application.
  • Separation-first: principles without attempted time/space/condition/scale separation are incomplete.
  • Over-application guard: if measurement or a standard pattern settles the design, skip TRIZ.
  • Stop: one named contradiction → separation/inventive pass → concrete resolution; do not cycle principles as decoration.

Frequently asked questions

What does the Thinking Triz AI skill do?

When two design requirements seem mutually exclusive, name the contradiction, separate conflicting states, then invent a concrete no-compromise resolution.

Why use Thinking Triz on TypingMind?

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

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

Which AI models can use Thinking Triz?

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 Thinking Triz?

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

Is the Thinking Triz AI skill free?

Yes. It is published on GitHub by tjboudreaux 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇