Nav Workflow logo

Nav Workflow

Organization
qf-studio
nav-workflow

Unified workflow orchestration for substantial tasks. Auto-detects complexity, defers to matching skills, or provides phase-based execution. Solves workflow conflicts between skills, loop mode, and CLAUDE.md.

Overview

Publisherqf-studio
Repositorynavigator
Skill namenav-workflow
Stars
232
Forks
12
Bundled files
6
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.

  • 6 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 Workflow 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-workflow .claude/skills/nav-workflow
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Nav Workflow 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 Workflow 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 Workflow 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 Task Mode Skill

Unified workflow orchestration that coordinates between skills, loop mode, and direct execution based on task complexity and type.

Why This Exists

Navigator had three disconnected workflow systems:

  1. Skills (frontend-component, etc.) - have mini-workflows (Step 1 → Step 7)
  2. Loop Mode - separate phase system (INIT → COMPLETE)
  3. CLAUDE.md - documents workflow nobody enforces

Result: Conflicts when multiple systems try to run.

Solution: Task Mode acts as a coordinator - detecting when skills should handle workflow vs when to provide standalone phase guidance.

How It Works

User Request
TASK MODE (this skill)
    ├─ Simple task? → Direct execution (no overhead)
    ├─ Skill matches? → Let skill run (it has workflow)
    └─ Substantial, no skill? → Task Mode phases

When to Invoke

Auto-invoke when:

  • User starts substantial work (3+ steps expected)
  • No obvious skill match (not "create component", "add endpoint", etc.)
  • Request involves planning, refactoring, or multi-file changes
  • Loop mode is disabled but structured execution needed

DO NOT invoke if:

  • Trivial task (typo fix, single line change)
  • Skill will clearly handle it (component creation, endpoint, migration, etc.)
  • User says "quick", "just do", "simple fix"
  • Already in Task Mode or Loop Mode

Configuration

Task Mode settings in .agent/.nav-config.json:

json
{
  "task_mode": {
    "enabled": true,
    "auto_detect": true,
    "defer_to_skills": true,
    "complexity_threshold": 0.5,
    "show_phase_indicator": true
  }
}

Options:

  • enabled: Master switch for Task Mode
  • auto_detect: Auto-detect complexity (vs explicit invocation only)
  • defer_to_skills: Let matching skills handle their own workflow
  • complexity_threshold: Score (0-1) required to activate
  • show_phase_indicator: Show phase banners during execution

Execution Steps

Step 1: Analyze Request

Run complexity detection:

bash
python3 functions/complexity_detector.py \
  --request "{USER_REQUEST}" \
  --context "{RECENT_CONTEXT}"

Complexity signals:

  • Multi-file changes expected (+0.3)
  • Planning language ("implement", "refactor", "add feature") (+0.2)
  • Vague requirements needing research (+0.2)
  • Cross-system changes (frontend+backend) (+0.3)
  • Testing requirements mentioned (+0.1)

Simple task signals:

  • Single file mentioned (-0.3)
  • Fix/typo/update language (-0.2)
  • Specific location given (-0.2)
  • "Quick" or "simple" mentioned (-0.3)

Decision:

IF complexity_score < threshold:
  → Direct execution (exit Task Mode)

Step 2: Check Skill Match

Run skill detection:

bash
python3 functions/skill_detector.py \
  --request "{USER_REQUEST}" \
  --available-skills "{SKILLS_LIST}"

Skill matching rules:

  • "create component" → frontend-component
  • "add endpoint" → backend-endpoint
  • "database migration" → database-migration
  • "write test" → backend-test/frontend-test
  • etc.

Decision:

IF matching_skill AND defer_to_skills:
  → Show: "Detected: {SKILL_NAME} skill will handle this"
  → Exit Task Mode (skill has workflow)

Step 3: Initialize Task Mode

If substantial task, no skill match:

Display activation:

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
TASK MODE ACTIVATED
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Task: {TASK_SUMMARY}
Complexity: {SCORE} (threshold: {THRESHOLD})
Skills matched: None (Task Mode will orchestrate)

Phases:
  ○ RESEARCH - Understand requirements
  ○ PLAN - Create implementation strategy
  ○ IMPL - Execute changes
  ○ VERIFY - Test and validate
  ○ COMPLETE - Commit and document

Starting RESEARCH phase...
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Step 4: Execute Phases

RESEARCH Phase:

  • Use Task agent for codebase exploration
  • State the Ideal Final Result (the no-new-code version) and the reuse inventory (what already does ≥80% of this)
  • If the brief declared a Contradiction, query prior resolutions (graph_manager.py --action contradictions --filter "<terms>") and run the nav-triz divergence step: three candidates from different separation modes, one recommended, before PLAN
  • Identify affected files
  • Find existing patterns
  • Document unknowns

Show phase transition:

bash
python3 functions/phase_indicator.py \
  --phase "RESEARCH" \
  --status "complete" \
  --next "PLAN"
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
PHASE: RESEARCH → PLAN
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Research completed:
  ✓ Found {N} related files
  ✓ Identified patterns in {LOCATION}
  ✓ Dependencies mapped

Moving to PLAN phase...
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

PLAN Phase:

  • Create TodoWrite items
  • Identify order of changes
  • Document approach

IMPL Phase:

  • Execute planned changes
  • Follow project patterns
  • Write tests as appropriate

VERIFY Phase:

  • Run tests
  • Type check
  • Build validation
  • Run nav-simplify (if enabled)

COMPLETE Phase:

  • Commit changes
  • Update documentation
  • Close tickets (if PM configured)
  • Suggest compact

Step 5: Complete Task Mode

Display completion:

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
TASK MODE COMPLETE
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Task: {TASK_SUMMARY}
Phases:
  ✓ RESEARCH - {DURATION}
  ✓ PLAN - {DURATION}
  ✓ IMPL - {DURATION}
  ✓ VERIFY - {DURATION}
  ✓ COMPLETE

Summary:
- {FILES_CHANGED} files changed
- {TESTS_ADDED} tests added
- Committed: {COMMIT_SHA}

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Predefined Functions

functions/complexity_detector.py

Analyzes request to determine complexity score.

Usage:

bash
python3 functions/complexity_detector.py \
  --request "Refactor the auth system to use JWT" \
  --context "Working on user management"

Returns:

json
{
  "complexity_score": 0.7,
  "signals": {
    "multi_file": true,
    "planning_language": true,
    "cross_system": false
  },
  "recommendation": "task_mode",
  "reason": "Refactoring task with multi-file scope"
}

functions/skill_detector.py

Checks if a skill should handle this request.

Usage:

bash
python3 functions/skill_detector.py \
  --request "Add a login component" \
  --available-skills '["frontend-component", "backend-endpoint", "database-migration"]'

Returns:

json
{
  "matching_skill": "frontend-component",
  "confidence": 0.95,
  "triggers": ["create component", "add component"],
  "defer": true,
  "reason": "Request matches frontend-component skill triggers"
}

functions/phase_indicator.py

Generates phase transition displays.

Usage:

bash
python3 functions/phase_indicator.py \
  --phase "IMPL" \
  --status "in_progress" \
  --progress 60 \
  --details '{"files_changed": 3, "tests_written": 2}'

Returns: Formatted phase indicator block.


Integration with Navigator

With Loop Mode

Task Mode is lighter weight than Loop Mode:

  • Loop Mode: Strict iteration control, stagnation detection, EXIT_SIGNAL
  • Task Mode: Phase guidance, skill coordination, no strict gates

When to use which:

  • Loop Mode: "Run until done", autonomous iteration
  • Task Mode: Substantial task, need structure but not strict iteration

Can coexist: Loop Mode wraps Task Mode phases if both active.

With Skills

Task Mode defers to skills by default:

  • Skill has its own workflow (Steps 1-7)
  • Task Mode doesn't add overhead
  • Just shows "Skill X will handle this"

Override: Set defer_to_skills: false to always use Task Mode phases.

With nav-simplify

Simplification runs during VERIFY phase:

  • After tests pass
  • Before committing
  • Respects simplification.enabled config

With Autonomous Completion

COMPLETE phase triggers autonomous protocol:

  • Commit changes
  • Archive documentation
  • Close tickets
  • Create markers

Comparison Table

AspectDirect ExecutionTask ModeLoop Mode
ComplexityLowMedium-HighHigh
Phase trackingNoneVisual phasesStrict phases
Iteration controlNoneNoneEXIT_SIGNAL
Skill coordinationNoneDefersIndependent
Best forQuick fixesFeaturesAutonomous work

Examples

Example 1: Simple Fix (Direct Execution)

User: "Fix the typo in README"

→ Complexity: 0.1 (below threshold)
→ Direct execution (no Task Mode overhead)

Example 2: Component Creation (Skill Defers)

User: "Create a UserProfile component"

→ Complexity: 0.6
→ Skill match: frontend-component (0.95 confidence)
→ Task Mode defers: "frontend-component skill will handle this"
→ Skill executes its own Step 1-7 workflow

Example 3: Refactoring (Task Mode Active)

User: "Refactor auth to use JWT instead of sessions"

→ Complexity: 0.8
→ Skill match: None
→ Task Mode activates
→ RESEARCH: Explore current auth implementation
→ PLAN: Document JWT migration steps
→ IMPL: Execute changes across files
→ VERIFY: Run tests, simplify code
→ COMPLETE: Commit, document

Example 4: With Loop Mode

User: "Run until done: implement user roles"

→ Loop Mode activated (explicit trigger)
→ Task Mode phases guide each iteration:
    Iteration 1: RESEARCH phase
    Iteration 2: PLAN + IMPL phases
    Iteration 3: IMPL + VERIFY phases
    Iteration 4: COMPLETE + EXIT_SIGNAL

Error Handling

Config not found:

Task Mode config not found in .nav-config.json.
Using defaults: auto_detect=true, threshold=0.5

Skill detection fails:

  • Fall back to Task Mode (conservative)
  • Log warning but continue

Phase detection ambiguous:

  • Default to current or next logical phase
  • Show reasoning for user

Success Criteria

Task Mode succeeds when:

  • Simple tasks execute without overhead
  • Skills handle their matched requests
  • Substantial tasks get phase structure
  • No conflicts between systems
  • User sees clear progress indicators

Verification Checklist

After implementation:

  • "Add login component" → frontend-component runs (not Task Mode)
  • "Refactor auth system" → Task Mode runs (no matching skill)
  • "Fix typo" → Direct execution (not substantial)
  • Phase indicators show during Task Mode
  • Skills continue working unchanged
  • Loop Mode can wrap Task Mode phases

This skill provides unified workflow orchestration, resolving conflicts between Navigator's multiple workflow systems.

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 Workflow AI skill do?

Unified workflow orchestration for substantial tasks. Auto-detects complexity, defers to matching skills, or provides phase-based execution. Solves workflow conflicts between skills, loop mode, and CLAUDE.md.

Why use Nav Workflow on TypingMind?

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

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

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 Workflow?

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

Is the Nav Workflow 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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