Nav Marker logo

Nav Marker

Organization
qf-studio
nav-marker

Create context save points to preserve conversation state before breaks, risky changes, or compaction. Use when user says "save my progress", "create checkpoint", "mark this point", or before clearing context.

Overview

Publisherqf-studio
Repositorynavigator
Skill namenav-marker
Stars
232
Forks
12
Bundled files
1
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.

  • 1 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 Marker 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-marker .claude/skills/nav-marker
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Nav Marker 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 Marker 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 Marker 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 Marker Skill

Create context markers - save points that preserve conversation state so you can resume work later without re-explaining everything.

When to Invoke

Invoke this skill when the user:

  • Says "save my progress", "create checkpoint", "mark this"
  • Says "before I take a break", "save before lunch"
  • Mentions "risky refactor ahead", "experiment with new approach"
  • Says "end of day", "stopping for today"
  • Before compacting context

DO NOT invoke if:

  • User is asking about existing markers (use listing, not creation)
  • Context is fresh (< 5 messages exchanged)

Execution Steps

Step 1: Check Navigator Structure

Verify .agent/.context-markers/ directory exists:

bash
mkdir -p .agent/.context-markers

Step 2: Determine Marker Name

If user provided name:

  • Use their name (sanitize: lowercase, hyphens for spaces)
  • Example: "Before Big Refactor" → "before-big-refactor"

If no name provided:

  • Auto-generate with timestamp: marker-{YYYY-MM-DD}-{HHmm}
  • Example: marker-2025-10-16-1430

Ask user for optional note:

Creating marker: [name]

Add a note? (optional - helps remember context later)
Example: "OAuth working, need to add tests"

Note:

Step 3: Generate Marker Content [EXECUTE]

IMPORTANT: You MUST actively capture ToM sections (User Intent, Corrections, Belief State).

Create marker document with this structure:

markdown
# Context Marker: [name]

**Created**: [YYYY-MM-DD HH:MM]
**Note**: [user's note or "No note provided"]

---

## Conversation Summary

[Summarize last 10-15 messages:
- What user was working on
- Key decisions made
- Problems solved
- Current progress state
]

## Documentation Loaded

[List docs that were Read during session:
- Navigator: ✅ .agent/DEVELOPMENT-README.md
- Task: TASK-XX-feature.md
- System: project-architecture.md
- SOPs: [if any]
]

## Files Modified

[List files with Write/Edit calls:
- src/auth/login.ts (implemented OAuth)
- src/routes/auth.ts (added endpoints)
- tests/auth.test.ts (created tests)
]

## Current Focus

[What user is working on right now:
- Feature: Authentication with OAuth
- Phase: Integration complete, testing pending
- Blockers: [if any]
]

## Technical Decisions

[Key architectural choices:
- Using passport.js over next-auth (better control)
- JWT tokens in httpOnly cookies (XSS protection)
- Redis for session storage (scalability)
]

## Next Steps

[What to do after restore:
1. Finish writing tests for OAuth flow
2. Add error handling for failed logins
3. Document setup in README
]

## User Intent & Goals (ToM) [CAPTURE ACTIVELY]

[Theory of Mind section - captures user's mental state for better restoration]

**⚠️ CRITICAL: Analyze conversation to extract these - do not leave empty!**

**Primary goal this session**:
[What the user was ultimately trying to accomplish - not just the surface task]
- Review conversation for "I want to...", "The goal is...", "We need to..."
- Infer from task context if not explicitly stated

**Stated preferences**:
[Any preferences expressed during session:
- Communication style (concise/detailed)
- Code patterns preferred
- Confirmation behavior wanted
]
- Look for "I prefer...", "Don't do...", "Always use..."

**Corrections made**:
[Important corrections that should persist:
- "Should be /users not /user (plural convention)"
- "Prefer functional components over class"
- "Always use TypeScript strict mode"
]
- Look for "No, I meant...", "Actually...", "Not X, use Y"
- These MUST be captured to avoid repeating mistakes

## Belief State [CAPTURE ACTIVELY]

[Captures mutual understanding state for accurate restoration]

**⚠️ CRITICAL: Infer from conversation - do not leave empty!**

**What user knows**:
[User's demonstrated knowledge level:
- Familiar with Express, new to Passport
- Knows about JWT, unfamiliar with refresh tokens
- Senior developer, skip basics
]

**Assumptions I made**:
[Key assumptions during session:
- Using Redis for sessions (confirmed by user)
- Auth endpoints follow /api/auth/* pattern
- Testing with Jest + React Testing Library
]

**Uncertainty areas**:
[Questions that weren't fully resolved:
- Not sure if user wants social logins beyond Google
- Rate limiting requirements unclear
- Error message format preferences unknown
]

## Loop State (if in loop mode)

[Capture loop mode state for resumption - skip if not in loop mode]

**Iteration**: [N]/[MAX] (e.g., 3/5)
**Phase**: [INIT|RESEARCH|IMPL|VERIFY|COMPLETE]
**State Hash**: [6-char hash for continuity]
**Completion Indicators**:
- [ ] Code committed
- [ ] Tests passing
- [ ] Documentation updated
- [ ] Ticket closed
- [ ] Marker created

**EXIT_SIGNAL**: [true/false]
**Stagnation Count**: [N]/[THRESHOLD]

## Knowledge Graph State (v6.0.0+)

[Capture graph state for restoration - skip if no knowledge graph]

**Check if graph exists**:
```bash
PLUGIN_DIR="${CLAUDE_PLUGIN_ROOT:-$HOME/.claude/plugins/cache/navigator-marketplace/navigator}"
[ -d "$PLUGIN_DIR" ] || PLUGIN_DIR="$HOME/.claude/plugins/marketplaces/navigator-marketplace"
if [ -f ".agent/knowledge/graph.json" ]; then
  python3 "$PLUGIN_DIR/skills/nav-graph/functions/graph_manager.py" --action stats --graph-path .agent/knowledge/graph.json
fi

Memories surfaced this session: [List memories that were queried or created:

  • mem-001: "Auth changes break session tests" (surfaced)
  • mem-002: "Use plural REST endpoints" (created from correction) ]

Concepts active: [Concepts relevant to current work:

  • authentication
  • testing
  • api ]

Graph queries made: [Knowledge graph queries from this session:

  • "What do we know about auth?" → 3 tasks, 1 memory ]

This allows restoration to re-surface relevant memories when resuming.

Restore Instructions

To restore this marker: ```bash Read .agent/.context-markers/[filename] ```

Or use: /nav:markers and select this marker


### Step 4: Save Marker File

Write marker to file:

Write( file_path: ".agent/.context-markers/[timestamp]_[name].md", content: [generated marker content] )


Filename format: `{YYYY-MM-DD-HHmm}_{name}.md`
Example: `2025-10-16-1430_before-big-refactor.md`

### Step 4.5: Verify Marker Creation

After creating marker, verify it was written successfully:

```bash
# Verify file exists and is non-empty
if [ -f ".agent/.context-markers/[filename]" ] && [ -s ".agent/.context-markers/[filename]" ]; then
  # Calculate checksum for verification
  checksum=$(md5 -q ".agent/.context-markers/[filename]" 2>/dev/null || md5sum ".agent/.context-markers/[filename]" | cut -d' ' -f1)

  # Log to central marker log
  echo "[$(date -u +"%Y-%m-%dT%H:%M:%SZ")] ✅ Marker created: [filename] (checksum: $checksum)" >> .agent/.marker-log

  echo "✅ Marker verified successfully"
else
  echo "❌ Marker creation failed - file missing or empty"
  exit 1
fi

Marker verification ensures:

  • File exists on disk
  • File has content (non-empty)
  • Checksum logged for integrity verification
  • Creation event logged to central log

Step 5: Confirm Creation

Show success message with verification details:

✅ Context marker created!

Marker: [name]
File: .agent/.context-markers/[filename]
Size: [X] KB (~[Y] tokens)
Checksum: [md5-hash]
Verified: ✅

This marker captures:
- Last [N] messages of conversation
- Files you were working on
- Technical decisions made
- Next steps to continue

To restore later:
- Start new session
- Say "load marker [name]"
- Or use /nav:markers to list all markers

Logged to: .agent/.marker-log

Scripts

create_marker.py: Generates marker content from conversation analysis

  • Input: Conversation history (from Claude)
  • Output: Formatted markdown marker

Common Use Cases

Before Lunch Break

User: "Save my progress, taking lunch"
→ Creates marker: "lunch-break-2025-10-16"
→ Captures current state
→ User resumes after lunch: "Load my lunch marker"

Before Risky Refactor

User: "Mark this before I refactor routing"
→ Creates marker: "before-routing-refactor"
→ If refactor fails, restore marker
→ If refactor succeeds, delete marker

End of Day

User: "End of day checkpoint"
→ Creates marker: "eod-2025-10-16"
→ Note: "OAuth done, tests tomorrow"
→ Next morning: "Load yesterday's marker"

Before Context Compact

Automatic (via nav-compact skill):
→ Creates marker: "before-compact-2025-10-16-1500"
→ Compact clears conversation
→ Marker preserves knowledge
→ Next session: Auto-offers to restore

Marker Best Practices

Good marker names:

  • lunch-break (clear when/why)
  • before-api-refactor (indicates purpose)
  • feature-complete (marks milestone)
  • eod-friday (specific timing)

Bad marker names:

  • temp (not descriptive)
  • marker1 (meaningless)
  • test (confusing)

When to create markers:

  • ✅ Before breaks (lunch, EOD)
  • ✅ Before risky changes
  • ✅ Before context compact
  • ✅ At milestones (feature complete)
  • ❌ After every single message (noise)
  • ❌ When context is fresh (< 5 messages)

Error Handling

Marker directory missing:

Creating .agent/.context-markers/ directory...
✅ Ready to save markers

Duplicate marker name:

⚠️  Marker "[name]" already exists

Options:
1. Overwrite (replace existing)
2. Append timestamp (create "[name]-v2")
3. Choose different name

Your choice [1-3]:

Insufficient context:

⚠️  Very little context to save (< 5 messages)

Markers work best when there's significant progress to preserve.
Continue anyway? [y/N]:

Success Criteria

Marker creation is successful when:

  • Marker file created in .agent/.context-markers/
  • Filename is unique and descriptive
  • Content includes: summary, loaded docs, files modified, next steps
  • User knows how to restore marker later
  • Marker is 2-5k tokens (comprehensive but efficient)

Notes

  • Markers are git-ignored (personal session save points)
  • Team members don't see each other's markers
  • Markers can be deleted anytime with /nav:markers clean
  • Typical marker size: 2-5k tokens (97.7% compression from 130k conversation)

This skill provides same functionality as /nav:marker command but with natural language invocation.

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 Marker AI skill do?

Create context save points to preserve conversation state before breaks, risky changes, or compaction. Use when user says "save my progress", "create checkpoint", "mark this point", or before clearing context.

Why use Nav Marker on TypingMind?

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

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

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 Marker?

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

Is the Nav Marker 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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