Nav Brief logo

Nav Brief

Organization
qf-studio
nav-brief

Render a one-screen intent brief (Goal/Scope/Approach/Limits/Verify/Won't-do/Contradiction) before implementing ambiguous task-shaped prompts, triggered by the prompt_brief op (UserPromptSubmit via hooks/nav_dispatch.py). Confirms scope with max 2 open questions before touching files; detects brief drift mid-task.

Overview

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

Installation

Install the Nav Brief 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-brief .claude/skills/nav-brief
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Nav Brief 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 Brief 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 Brief 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 Intent Brief Skill

Front-load the scope negotiation. When a prompt is task-shaped but ambiguous, render a one-screen INTENT BRIEF and get confirmation before writing code — turning 2-4 correction exchanges into 1 confirmation exchange.

Why This Exists

Ambiguity is not complexity. A small task can be highly ambiguous ("fix the bug" — which bug?) while a large one can be fully specified. The expensive failure mode is implementing on guessed scope: rework burns tokens on undo/redo, pollutes context with dead ends, and forces mid-feature compacts.

When This Fires

Hook contract: render a brief ONLY when this turn's injected context contains a NAV-BRIEF reminder block (emitted by the prompt_brief op — hooks/ops/prompt_brief.py, dispatched by nav_dispatch.py — on UserPromptSubmit when ambiguity score >= threshold). Never self-trigger from your own judgment alone — the hook is the single trigger source, so behavior stays predictable and tunable via config.

Config (.agent/.nav-config.json):

json
"brief_hook": {
  "enabled": true,
  "ambiguity_threshold": 0.5,
  "memory_budget_chars": 1200
}

Rendering the Brief

Use the template below. Pre-fill every field you can from the injected ## Relevant Memories section and current session context; mark assumptions explicitly.

┌─ BRIEF: <short title> ──────────────────────────────┐
│ Goal      <outcome, one line>                       │
│ Scope     <files/dirs/endpoints — concrete>         │
│           NOT <adjacent things left untouched>      │
│ Approach  <how — cite memory if pre-filled>         │
│ Limits    <numbers, constraints — mark ASSUMED>     │
│ Verify    <how completion will be proven>           │
│ Won't do  <explicit non-goals>                      │
│ Contradict <improving X worsens Y — or "none">      │
└─────────────────────────────────────────────────────┘
Confirm / edit? (open questions: <0-2>)

Rules:

  • Max 2 open questions. Ask only what you cannot infer or safely assume; everything else is a pre-filled default the user can veto.
  • Stop and wait for user confirmation before any Edit/Write/Bash that modifies the project.
  • After confirmation, capture corrections: changed defaults are candidate knowledge-graph memories ("Remember we decided..." flow via nav-graph), so future briefs pre-fill better.

When a Contradiction Is Declared (TRIZ, TASK-72)

Contradict is the TRIZ row: the task is inventive only when improving one thing worsens another ("faster session start vs enough context"). Most tasks are routine — none is the expected common case. Never invent a tension to fill the row.

When the row is not none, query prior resolutions BEFORE filling Approach:

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-graph/functions/graph_manager.py" \
  --action contradictions --filter "<2-3 key terms>"

Cite matching mem-NNN ids in Approach ("resolved by separation in time before: mem-063"). If nothing matches, say so in one line. On a substantial task (Task Mode complexity ≥ 0.5 or more than one subsystem), hand off to nav-triz before filling Approach: it produces three candidates from different separation modes and recommends one. After the task, if the resolution was non-obvious, capture it as a decision with --contradiction / --separation / --principle (see nav-graph Step 3B) so the next brief can find it. Drift detection does not cover this row.

Passthrough Rules (do NOT render a brief)

  • No NAV-BRIEF reminder in this turn's context.
  • The prompt is plainly an answer to a pending brief's open questions (model-side judgment — the hook is stateless in v1 and may re-fire on answers that contain task verbs; known limitation).
  • User signals urgency/override: "just do it", "quick fix", "skip the brief". Respect it silently.

Brief Drift Detection

Mid-implementation, if the work is about to exceed the confirmed brief's Scope or Limits (touching files outside scope, changing an untouched-by- agreement area), STOP and surface:

⚠ BRIEF DRIFT: <what exceeds the brief, one line>
Options: extend brief / skip this change

One question, then continue per the answer.

Interaction with workflow_enforcer / Task Mode

Both UserPromptSubmit hooks may fire on the same prompt. Show both blocks — they are complementary: WORKFLOW CHECK decides how to execute (mode), the brief decides what is in scope. In Task Mode, the confirmed brief replaces ad-hoc scope derivation in the PLAN phase.

Examples

Brief rendered"add rate limiting to the API" (score 0.7): brief with Scope pre-filled to public routes (graph memory), Limits marked ASSUMED, 1 open question about limit numbers. User answers; implementation starts.

Passthrough"fix the typo in README.md" (score 0.1): file reference gives full scope; no brief, direct execution.

Drift — brief confirmed scope /api/public/*; implementation later needs a shared middleware file used by internal routes → BRIEF DRIFT raised, user extends the brief.

Future Work (explicitly deferred from v1)

  • Statefulness: persist pending-brief state across turns (removes the answer-re-fire limitation).
  • strict_block: hard-gate implementation until a brief is confirmed.
  • Measurement: briefs-shown/passthrough counters + corrections-per-task metrics (pairs with nav-stats; deferred until there is a consumer).
  • Prompt rewriting.

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 Brief AI skill do?

Render a one-screen intent brief (Goal/Scope/Approach/Limits/Verify/Won't-do/Contradiction) before implementing ambiguous task-shaped prompts, triggered by the prompt_brief op (UserPromptSubmit via hooks/nav_dispatch.py). Confirms scope with max 2 open questions before touching files; detects brief drift mid-task.

Why use Nav Brief on TypingMind?

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

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

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 Brief?

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

Is the Nav Brief 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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