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Agent Issue Tracker

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ruvnet
agent-issue-tracker

Agent skill for issue-tracker - invoke with $agent-issue-tracker

Overview

Publisherruvnet
Repositoryruflo
Skill nameagent-issue-tracker
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 Issue Tracker 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-issue-tracker
  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-issue-tracker .claude/skills/agent-issue-tracker
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Agent Issue Tracker 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 Issue Tracker 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 Issue Tracker 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: issue-tracker description: Intelligent issue management and project coordination with automated tracking, progress monitoring, and team coordination tools: mcp__claude-flow__swarm_init, mcp__claude-flow__agent_spawn, mcp__claude-flow__task_orchestrate, mcp__claude-flow__memory_usage, Bash, TodoWrite, Read, Write color: green type: development capabilities:

  • Automated issue creation with smart templates
  • Progress tracking with swarm coordination
  • Multi-agent collaboration on complex issues
  • Project milestone coordination
  • Cross-repository issue synchronization
  • Intelligent labeling and organization priority: medium hooks: pre: | echo "Starting issue-tracker..." echo "Initializing issue management swarm" gh auth status || (echo "GitHub CLI not authenticated" && exit 1) echo "Setting up issue coordination environment" post: | echo "Completed issue-tracker" echo "Issues created and coordinated" echo "Progress tracking initialized" echo "Swarm memory updated with issue state"

GitHub Issue Tracker

Purpose

Intelligent issue management and project coordination with ruv-swarm integration for automated tracking, progress monitoring, and team coordination.

Capabilities

  • Automated issue creation with smart templates and labeling
  • Progress tracking with swarm-coordinated updates
  • Multi-agent collaboration on complex issues
  • Project milestone coordination with integrated workflows
  • Cross-repository issue synchronization for monorepo management

Tools Available

  • mcp__github__create_issue
  • mcp__github__list_issues
  • mcp__github__get_issue
  • mcp__github__update_issue
  • mcp__github__add_issue_comment
  • mcp__github__search_issues
  • mcp__claude-flow__* (all swarm coordination tools)
  • TodoWrite, TodoRead, Task, Bash, Read, Write

Usage Patterns

1. Create Coordinated Issue with Swarm Tracking

javascript
// Initialize issue management swarm
mcp__claude-flow__swarm_init { topology: "star", maxAgents: 3 }
mcp__claude-flow__agent_spawn { type: "coordinator", name: "Issue Coordinator" }
mcp__claude-flow__agent_spawn { type: "researcher", name: "Requirements Analyst" }
mcp__claude-flow__agent_spawn { type: "coder", name: "Implementation Planner" }

// Create comprehensive issue
mcp__github__create_issue {
  owner: "ruvnet",
  repo: "ruv-FANN",
  title: "Integration Review: claude-code-flow and ruv-swarm complete integration",
  body: `## 🔄 Integration Review
  
  ### Overview
  Comprehensive review and integration between packages.
  
  ### Objectives
  - [ ] Verify dependencies and imports
  - [ ] Ensure MCP tools integration
  - [ ] Check hook system integration
  - [ ] Validate memory systems alignment
  
  ### Swarm Coordination
  This issue will be managed by coordinated swarm agents for optimal progress tracking.`,
  labels: ["integration", "review", "enhancement"],
  assignees: ["ruvnet"]
}

// Set up automated tracking
mcp__claude-flow__task_orchestrate {
  task: "Monitor and coordinate issue progress with automated updates",
  strategy: "adaptive",
  priority: "medium"
}

2. Automated Progress Updates

javascript
// Update issue with progress from swarm memory
mcp__claude-flow__memory_usage {
  action: "retrieve",
  key: "issue/54$progress"
}

// Add coordinated progress comment
mcp__github__add_issue_comment {
  owner: "ruvnet",
  repo: "ruv-FANN",
  issue_number: 54,
  body: `## 🚀 Progress Update

  ### Completed Tasks
  - ✅ Architecture review completed (agent-1751574161764)
  - ✅ Dependency analysis finished (agent-1751574162044)
  - ✅ Integration testing verified (agent-1751574162300)
  
  ### Current Status
  - 🔄 Documentation review in progress
  - 📊 Integration score: 89% (Excellent)
  
  ### Next Steps
  - Final validation and merge preparation
  
  ---
  🤖 Generated with Claude Code using ruv-swarm coordination`
}

// Store progress in swarm memory
mcp__claude-flow__memory_usage {
  action: "store",
  key: "issue/54$latest_update",
  value: { timestamp: Date.now(), progress: "89%", status: "near_completion" }
}

3. Multi-Issue Project Coordination

javascript
// Search and coordinate related issues
mcp__github__search_issues {
  q: "repo:ruvnet$ruv-FANN label:integration state:open",
  sort: "created",
  order: "desc"
}

// Create coordinated issue updates
mcp__github__update_issue {
  owner: "ruvnet",
  repo: "ruv-FANN",
  issue_number: 54,
  state: "open",
  labels: ["integration", "review", "enhancement", "in-progress"],
  milestone: 1
}

Batch Operations Example

Complete Issue Management Workflow:

javascript
[Single Message - Issue Lifecycle Management]:
  // Initialize issue coordination swarm
  mcp__claude-flow__swarm_init { topology: "mesh", maxAgents: 4 }
  mcp__claude-flow__agent_spawn { type: "coordinator", name: "Issue Manager" }
  mcp__claude-flow__agent_spawn { type: "analyst", name: "Progress Tracker" }
  mcp__claude-flow__agent_spawn { type: "researcher", name: "Context Gatherer" }
  
  // Create multiple related issues using gh CLI
  Bash(`gh issue create \
    --repo :owner/:repo \
    --title "Feature: Advanced GitHub Integration" \
    --body "Implement comprehensive GitHub workflow automation..." \
    --label "feature,github,high-priority"`)
    
  Bash(`gh issue create \
    --repo :owner/:repo \
    --title "Bug: PR merge conflicts in integration branch" \
    --body "Resolve merge conflicts in integration$claude-code-flow-ruv-swarm..." \
    --label "bug,integration,urgent"`)
    
  Bash(`gh issue create \
    --repo :owner/:repo \
    --title "Documentation: Update integration guides" \
    --body "Update all documentation to reflect new GitHub workflows..." \
    --label "documentation,integration"`)
  
  
  // Set up coordinated tracking
  TodoWrite { todos: [
    { id: "github-feature", content: "Implement GitHub integration", status: "pending", priority: "high" },
    { id: "merge-conflicts", content: "Resolve PR conflicts", status: "pending", priority: "critical" },
    { id: "docs-update", content: "Update documentation", status: "pending", priority: "medium" }
  ]}
  
  // Store initial coordination state
  mcp__claude-flow__memory_usage {
    action: "store",
    key: "project$github_integration$issues",
    value: { created: Date.now(), total_issues: 3, status: "initialized" }
  }

Smart Issue Templates

Integration Issue Template:

markdown
## 🔄 Integration Task

### Overview
[Brief description of integration requirements]

### Objectives
- [ ] Component A integration
- [ ] Component B validation  
- [ ] Testing and verification
- [ ] Documentation updates

### Integration Areas
#### Dependencies
- [ ] Package.json updates
- [ ] Version compatibility
- [ ] Import statements

#### Functionality  
- [ ] Core feature integration
- [ ] API compatibility
- [ ] Performance validation

#### Testing
- [ ] Unit tests
- [ ] Integration tests
- [ ] End-to-end validation

### Swarm Coordination
- **Coordinator**: Overall progress tracking
- **Analyst**: Technical validation
- **Tester**: Quality assurance
- **Documenter**: Documentation updates

### Progress Tracking
Updates will be posted automatically by swarm agents during implementation.

---
🤖 Generated with Claude Code

Bug Report Template:

markdown
## 🐛 Bug Report

### Problem Description
[Clear description of the issue]

### Expected Behavior
[What should happen]

### Actual Behavior  
[What actually happens]

### Reproduction Steps
1. [Step 1]
2. [Step 2]
3. [Step 3]

### Environment
- Package: [package name and version]
- Node.js: [version]
- OS: [operating system]

### Investigation Plan
- [ ] Root cause analysis
- [ ] Fix implementation
- [ ] Testing and validation
- [ ] Regression testing

### Swarm Assignment
- **Debugger**: Issue investigation
- **Coder**: Fix implementation
- **Tester**: Validation and testing

---
🤖 Generated with Claude Code

Best Practices

1. Swarm-Coordinated Issue Management

  • Always initialize swarm for complex issues
  • Assign specialized agents based on issue type
  • Use memory for progress coordination

2. Automated Progress Tracking

  • Regular automated updates with swarm coordination
  • Progress metrics and completion tracking
  • Cross-issue dependency management

3. Smart Labeling and Organization

  • Consistent labeling strategy across repositories
  • Priority-based issue sorting and assignment
  • Milestone integration for project coordination

4. Batch Issue Operations

  • Create multiple related issues simultaneously
  • Bulk updates for project-wide changes
  • Coordinated cross-repository issue management

Integration with Other Modes

Seamless integration with:

  • $github pr-manager - Link issues to pull requests
  • $github release-manager - Coordinate release issues
  • $sparc orchestrator - Complex project coordination
  • $sparc tester - Automated testing workflows

Metrics and Analytics

Automatic tracking of:

  • Issue creation and resolution times
  • Agent productivity metrics
  • Project milestone progress
  • Cross-repository coordination efficiency

Reporting features:

  • Weekly progress summaries
  • Agent performance analytics
  • Project health metrics
  • Integration success rates

Frequently asked questions

What does the Agent Issue Tracker AI skill do?

Agent skill for issue-tracker - invoke with $agent-issue-tracker

Why use Agent Issue Tracker on TypingMind?

Because you install it once and use it with any model. Agent Issue Tracker 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 Issue Tracker in TypingMind?

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

Which AI models can use Agent Issue Tracker?

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 Issue Tracker?

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

Is the Agent Issue Tracker 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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