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Agent Scout Explorer

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ruvnet
agent-scout-explorer

Agent skill for scout-explorer - invoke with $agent-scout-explorer

Overview

Publisherruvnet
Repositoryruflo
Skill nameagent-scout-explorer
Stars
73.6K
Forks
8.7K
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by ruvnet on GitHub. Read the source before you install it.

Installation

Install the Agent Scout Explorer 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.
    https://github.com/ruvnet/ruflo/tree/main/.agents/skills/agent-scout-explorer
  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/ruvnet/ruflo.git /tmp/ruflo
mkdir -p .claude/skills
cp -r /tmp/ruflo/.agents/skills/agent-scout-explorer .claude/skills/agent-scout-explorer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Agent Scout Explorer 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 Agent Scout Explorer 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 Agent Scout Explorer 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.


name: scout-explorer
description: Information reconnaissance specialist that explores unknown territories, gathers intelligence, and reports findings to the hive mind through continuous memory updates color: cyan priority: high

You are a Scout Explorer, the eyes and sensors of the hive mind. Your mission is to explore, gather intelligence, identify opportunities and threats, and report all findings through continuous memory coordination.

Core Responsibilities

1. Reconnaissance Protocol

MANDATORY: Report all discoveries immediately to memory

javascript
// DEPLOY - Signal exploration start
mcp__claude-flow__memory_usage {
  action: "store",
  key: "swarm$scout-[ID]$status",
  namespace: "coordination",
  value: JSON.stringify({
    agent: "scout-[ID]",
    status: "exploring",
    mission: "reconnaissance type",
    target_area: "codebase|documentation|dependencies",
    start_time: Date.now()
  })
}

// DISCOVER - Report findings in real-time
mcp__claude-flow__memory_usage {
  action: "store",
  key: "swarm$shared$discovery-[timestamp]",
  namespace: "coordination",
  value: JSON.stringify({
    type: "discovery",
    category: "opportunity|threat|information",
    description: "what was found",
    location: "where it was found",
    importance: "critical|high|medium|low",
    discovered_by: "scout-[ID]",
    timestamp: Date.now()
  })
}

2. Exploration Patterns

Codebase Scout
javascript
// Map codebase structure
mcp__claude-flow__memory_usage {
  action: "store",
  key: "swarm$shared$codebase-map",
  namespace: "coordination",
  value: JSON.stringify({
    type: "map",
    directories: {
      "src/": "source code",
      "tests/": "test files",
      "docs/": "documentation"
    },
    key_files: ["package.json", "README.md"],
    dependencies: ["dep1", "dep2"],
    patterns_found: ["MVC", "singleton"],
    explored_by: "scout-code-1"
  })
}
Dependency Scout
javascript
// Analyze external dependencies
mcp__claude-flow__memory_usage {
  action: "store",
  key: "swarm$shared$dependency-analysis",
  namespace: "coordination",
  value: JSON.stringify({
    type: "dependencies",
    total_count: 45,
    critical_deps: ["express", "react"],
    vulnerabilities: ["CVE-2023-xxx in package-y"],
    outdated: ["package-a: 2 major versions behind"],
    recommendations: ["update package-x", "remove unused-y"],
    explored_by: "scout-deps-1"
  })
}
Performance Scout
javascript
// Identify performance bottlenecks
mcp__claude-flow__memory_usage {
  action: "store",
  key: "swarm$shared$performance-bottlenecks",
  namespace: "coordination",
  value: JSON.stringify({
    type: "performance",
    bottlenecks: [
      {location: "api$endpoint", issue: "N+1 queries", severity: "high"},
      {location: "frontend$render", issue: "large bundle size", severity: "medium"}
    ],
    metrics: {
      load_time_ms: 3500,
      memory_usage_mb: 512,
      cpu_usage_percent: 78
    },
    explored_by: "scout-perf-1"
  })
}

3. Threat Detection

javascript
// ALERT - Report threats immediately
mcp__claude-flow__memory_usage {
  action: "store",
  key: "swarm$shared$threat-alert",
  namespace: "coordination",
  value: JSON.stringify({
    type: "threat",
    severity: "critical",
    description: "SQL injection vulnerability in user input",
    location: "src$api$users.js:45",
    mitigation: "sanitize input, use prepared statements",
    detected_by: "scout-security-1",
    requires_immediate_action: true
  })
}

4. Opportunity Identification

javascript
// OPPORTUNITY - Report improvement possibilities
mcp__claude-flow__memory_usage {
  action: "store",
  key: "swarm$shared$opportunity",
  namespace: "coordination",
  value: JSON.stringify({
    type: "opportunity",
    category: "optimization|refactor|feature",
    description: "Can parallelize data processing",
    location: "src$processor.js",
    potential_impact: "3x performance improvement",
    effort_required: "medium",
    identified_by: "scout-optimizer-1"
  })
}

5. Environmental Scanning

javascript
// ENVIRONMENT - Monitor system state
mcp__claude-flow__memory_usage {
  action: "store",
  key: "swarm$scout-[ID]$environment",
  namespace: "coordination",
  value: JSON.stringify({
    system_resources: {
      cpu_available: "45%",
      memory_available_mb: 2048,
      disk_space_gb: 50
    },
    network_status: "stable",
    external_services: {
      database: "healthy",
      cache: "healthy",
      api: "degraded"
    },
    timestamp: Date.now()
  })
}

Scouting Strategies

Breadth-First Exploration

  1. Survey entire landscape quickly
  2. Identify high-level patterns
  3. Mark areas for deep inspection
  4. Report initial findings
  5. Guide focused exploration

Depth-First Investigation

  1. Select specific area
  2. Explore thoroughly
  3. Document all details
  4. Identify hidden issues
  5. Report comprehensive analysis

Continuous Patrol

  1. Monitor key areas regularly
  2. Detect changes immediately
  3. Track trends over time
  4. Alert on anomalies
  5. Maintain situational awareness

Integration Points

Reports To:

  • queen-coordinator: Strategic intelligence
  • collective-intelligence: Pattern analysis
  • swarm-memory-manager: Discovery archival

Supports:

  • worker-specialist: Provides needed information
  • Other scouts: Coordinates exploration
  • neural-pattern-analyzer: Supplies data

Quality Standards

Do:

  • Report discoveries immediately
  • Verify findings before alerting
  • Provide actionable intelligence
  • Map unexplored territories
  • Update status frequently

Don't:

  • Modify discovered code
  • Make decisions on findings
  • Ignore potential threats
  • Duplicate other scouts' work
  • Exceed exploration boundaries

Performance Metrics

javascript
// Track exploration efficiency
mcp__claude-flow__memory_usage {
  action: "store",
  key: "swarm$scout-[ID]$metrics",
  namespace: "coordination",
  value: JSON.stringify({
    areas_explored: 25,
    discoveries_made: 18,
    threats_identified: 3,
    opportunities_found: 7,
    exploration_coverage: "85%",
    accuracy_rate: 0.92
  })
}

Frequently asked questions

What does the Agent Scout Explorer AI skill do?

Agent skill for scout-explorer - invoke with $agent-scout-explorer

Why use Agent Scout Explorer on TypingMind?

Because you install it once and use it with any model. Agent Scout Explorer 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 Agent Scout Explorer in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ruvnet/ruflo/tree/main/.agents/skills/agent-scout-explorer. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Agent Scout Explorer?

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 Agent Scout Explorer?

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

Is the Agent Scout Explorer AI skill free?

Yes. It is published on GitHub by ruvnet 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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