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nav-marker导航标记

Agent Skill

nav-marker 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

188

周安装

8

GitHub Stars

161

下载量

66
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:nav-marker(导航标记)
来源仓库:https://github.com/alekspetrov/navigator
仓库路径:skills/nav-marker
安装命令:
npx skills add https://github.com/alekspetrov/navigator --skill nav-marker
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/alekspetrov/navigator --skill nav-marker

简介

nav-marker 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词快速定位候选结果时使用。

  • 适用于研究检索类任务,支持基于来源仓库和任务场景进行信息聚合与过滤。
  • 通过 npx skills add 命令从指定 GitHub 仓库安装,具体路径为 skills/nav-marker。
  • 安装前需确认权限范围、维护状态,以及是否涉及联网或文件操作等敏感行为。
  • 建议结合原始 README 文档进一步核验功能细节与使用边界。

SKILL.md

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:

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:

# 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**:

if [ -f ".agent/knowledge/graph.json" ]; then python3 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:

# 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.

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

37.43%
按下载量换算25

Claude

29.21%
按下载量换算19

Cursor

18.85%
按下载量换算12

Gemini CLI

9.66%
按下载量换算6

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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