Nav Triz logo

Nav Triz

Organization
qf-studio
nav-triz

Divergent solving for a declared contradiction ("improving X worsens Y"). Produces three competing candidates from different TRIZ separation modes, each with cost and downside, then recommends one. Auto-invoke when user says "find a better solution", "alternatives for", "resolve the contradiction", "triz this", or when a nav-brief declares a Contradiction on a substantial task.

Overview

Publisherqf-studio
Repositorynavigator
Skill namenav-triz
Stars
232
Forks
12
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 qf-studio on GitHub. Read the source before you install it.

Installation

Install the Nav 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/qf-studio/navigator.git /tmp/navigator
mkdir -p .claude/skills
cp -r /tmp/navigator/skills/nav-triz .claude/skills/nav-triz
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Nav 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 Nav 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 Nav 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.

Navigator TRIZ Skill

Navigator finds a solution fast. This skill exists for the minority of tasks where the first fitting idea is a compromise: improving one thing worsens another. It forces three genuinely different candidates onto the table before PLAN picks one, then records the resolution so the next session can find it.

When This Fires (and when it must not)

Fires when either:

  • The user asks for it: "find a better solution for X", "alternatives for X", "resolve the contradiction", "triz this".
  • A nav-brief Contradict row is not none AND the task is substantial (Task Mode complexity ≥ 0.5, or touching more than one subsystem).

Does NOT fire for routine work, a brief with Contradict none, "just do it" / "quick fix" passthroughs, or when the user already named the approach. Ceremony is the failure mode of this skill; when in doubt, skip it and say so in one line. (Resolved before: mem-060, mem-074 — new blocking behaviour ships opt-in, by condition.)

Protocol (one screen of output)

  1. State the contradiction in one line, technical form ("improving A vs worsening B") or physical form ("X must be both A and not-A"). If you cannot, stop: there is no contradiction, proceed normally.
  2. Ideal Final Result: what if the function existed with no new code? Name what would have to be true. If reachable, that is candidate 0 and usually the answer.
  3. Reuse inventory: what already does ≥80% of this? path:line or none found.
  4. Prior resolutions + principle prompts:
    bash
    PLUGIN_DIR="${CLAUDE_PLUGIN_ROOT:-$HOME/.claude/plugins/cache/navigator-marketplace/navigator}"
    [ -d "$PLUGIN_DIR" ] || PLUGIN_DIR="$HOME/.claude/plugins/marketplaces/navigator-marketplace"
    python3 "$PLUGIN_DIR/skills/nav-triz/functions/triz_suggest.py" \
      --contradiction "<improving A> vs <worsening B>"
    Prints decisions in this graph that resolved a related tension, then three principle prompts from different separation modes (generic trio if nothing matches). Read reference/PRINCIPLES.md only if a prompt is unclear.
  5. Three candidates, one per prompt, in the table below. Each MUST name what it worsens. A candidate with no downside is a compromise misdescribed, or the IFR.
  6. Recommend one with the reason in one sentence. Max one open question.
  7. After the work, if the resolution was non-obvious, capture it:
    bash
    python3 "$PLUGIN_DIR/skills/nav-graph/functions/graph_manager.py" --action add-memory \
      --memory-type decision --summary "<what was chosen>" --concepts "<a,b>" \
      --contradiction "<A vs B>" --separation <time|space|condition|level> \
      --principle "<principle: concrete move>"

Output Template

┌─ TRIZ: <short title> ─────────────────────────────────────────────┐
│ Contradiction  improving <A> vs worsening <B>                       │
│ IFR            <no-new-code version; reachable? yes/no>             │
│ Reuse          <path:line does X> | none found                      │
│ Prior          mem-NNN (<separation>) | none                        │
├───┬────────────┬───────────────────────────┬──────┬────────────────┤
│ # │ mode/prin. │ what changes              │ cost │ worsens        │
├───┼────────────┼───────────────────────────┼──────┼────────────────┤
│ 1 │ time / 10  │ ...                       │ S    │ ...            │
│ 2 │ cond / 15  │ ...                       │ M    │ ...            │
│ 3 │ level / 1  │ ...                       │ L    │ ...            │
├───┴────────────┴───────────────────────────┴──────┴────────────────┤
│ Recommend  #<n> — <one-sentence reason>                            │
└────────────────────────────────────────────────────────────────────┘
Open question (0-1): ...

Rules: three candidates, not five; different separation modes, not three variants of one move; each row's "worsens" cell is mandatory; total output fits one screen.

Hand-offs

  • nav-brief → here when Contradict ≠ none on a substantial task. The brief's Approach cites the recommended candidate.
  • navigator-research agent Phase 0.5 already produces IFR + reuse inventory; when it reports a contradiction, run this protocol in the main session (agents do not invoke skills).
  • Task Mode RESEARCH phase lists this as the divergence step before PLAN.

Predefined Functions

functions/triz_suggest.py --contradiction "<A vs B>" [--graph-path P] [--limit 3] [--format text|json] Deterministic, stdlib, always exits 0. Keyword-ranks the ~20 mapped principles, picks one per separation mode first, pads with prior action / dynamization / segmentation, and lists prior decisions from the graph via query_contradictions.

Related

  • reference/PRINCIPLES.md — separation modes + mapped principles (lazy-load)
  • skills/nav-brief/SKILL.md — Contradict row
  • skills/nav-graph/SKILL.md Step 3B — capturing tagged decisions

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 Nav Triz AI skill do?

Divergent solving for a declared contradiction ("improving X worsens Y"). Produces three competing candidates from different TRIZ separation modes, each with cost and downside, then recommends one. Auto-invoke when user says "find a better solution", "alternatives for", "resolve the contradiction", "triz this", or when a nav-brief declares a Contradiction on a substantial task.

Why use Nav Triz on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/qf-studio/navigator/tree/main/skills/nav-triz. 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 Nav 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 Nav Triz?

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

Is the Nav Triz AI skill free?

Yes. It is published on GitHub by qf-studio 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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