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AgentFlow

Free

Automate your AI development pipelines with Kanban.

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Free · Opens the source repo

What AgentFlow does

AgentFlow transforms your existing Kanban board into a fully autonomous AI development pipeline, allowing you to manage tasks seamlessly without the need for custom orchestration infrastructure. By treating your project management tool—such as Asana, GitHub Projects, or Linear—as a distributed state machine, AgentFlow enables AI agents to read and write task states through comments. This integration not only enhances observability but also allows for human intervention directly within the familiar UI of your Kanban board.

The skill operates on a 7-stage Kanban pipeline, where tasks progress through stages like Backlog, Research, Build, Review, Test, Integrate, and Done. Each stage enforces specific quality gates, ensuring that code quality is maintained before moving forward. With a stateless orchestrator that runs on a crontab schedule, tasks are managed without session dependencies, allowing for crash recovery and continuous operation. The system also incorporates adversarial reviews, where a different AI agent must identify multiple issues before a task is approved, thereby improving code review processes.

AgentFlow is particularly beneficial for teams or individuals looking to streamline their development workflows, providing a clear view of task status and cost tracking. It supports multiple Claude Code workers running in parallel, optimizing the development lifecycle from build to integration. The automatic cost tracking feature helps monitor expenses associated with each task, providing alerts and limits to prevent overspending. Overall, AgentFlow is designed for those who want to leverage AI capabilities while maintaining control and oversight of their development processes.

When to use it

Use AgentFlow when you want to manage multiple AI tasks across a development lifecycle with clear visibility and control.

When not to use it

This skill may not be suitable for projects that do not utilize a Kanban board or require a more traditional orchestration infrastructure.

What you can build with it

Automating Development Workflows

Use AgentFlow to manage complex development tasks across multiple AI agents, ensuring efficient task progression and quality control.

Cost Tracking for Projects

Leverage the built-in cost tracking feature to monitor expenses associated with each task, helping to manage budgets effectively.

Improving Code Review Processes

Implement adversarial reviews within your pipeline to enhance code quality, requiring multiple issues to be identified before approval.

How to install AgentFlow

View source

1. Install with the skills CLI

npx skills add sickn33/agentic-awesome-skills/agentflow --agent claude-code

2. Or install it manually

Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.

Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs

Inside SKILL.md

Written by sickn33

AgentFlow

Overview

AgentFlow turns your existing Kanban board into a fully autonomous AI development pipeline. Instead of building custom orchestration infrastructure, it treats your project management tool (Asana, GitHub Projects, Linear) as a distributed state machine — tasks move through stages, AI agents read and write state via comments, and humans intervene through the same UI they already use.

The result is complete pipeline observability from your phone, free crash recovery (state lives in your PM tool, not in memory), and human override at any point by dragging a card.

When to Use This Skill

  • Use when you need to orchestrate multiple Claude Code workers across a full development lifecycle (build, review, test, integrate)
  • Use when you want deterministic quality gates (tsc/eslint/tests) before AI review on AI-generated code
  • Use when you want full pipeline visibility from your Kanban board or phone
  • Use when running a solo or team project that needs autonomous task dispatch with cost tracking
  • Use when you need crash-proof orchestration that survives session restarts

Core Concepts

7-Stage Kanban Pipeline

Tasks flow through: Backlog, Research, Build, Review, Test, Integrate, Done. Each stage has specific gates. The Kanban board IS the orchestration layer — no separate database, no message queue, no custom infrastructure.

Stateless Orchestrator

A crontab-driven one-shot sweep runs every 15 minutes. No daemon, no session dependency. If it crashes, the next sweep picks up where it left off because all state lives in your PM tool.

Deterministic Before Probabilistic

Hard gates (tsc + eslint + tests) run before any AI review, catching roughly 60% of issues at near-zero cost. AI review comes after, as a second layer.

Adversarial Review

A different AI agent reviews code and must list 3 things wrong before deciding to pass. This prevents rubber-stamp approvals.

Transitive Priority Dispatch

Tasks that unblock the most downstream work get built first, automatically computing the critical path.

Skills / Commands

/spec-to-board

Decomposes a SPEC.md into atomic tasks on your Kanban board with dependencies mapped.

/sdlc-orchestrate

Dispatches tasks to workers based on transitive priority and conflict detection. Runs as a crontab sweep.

/sdlc-worker --slot <N>

Runs a worker in a terminal slot that picks up tasks, builds code, and creates PRs. Run 3-4 workers in parallel.

/sdlc-health

Real-time pipeline status dashboard showing current stage, assigned agent, retry count, and accumulated cost for every task.

/sdlc-stop

Graceful shutdown: active workers finish their current task, unstarted tasks return to Backlog.

Step-by-Step Guide

1. Write Your Spec

Create a SPEC.md for your project describing what you want to build.

2. Decompose Into Tasks

claude -p "/spec-to-board"

This reads your SPEC.md, decomposes it into atomic tasks, maps dependencies, and creates them on your Kanban board.

3. Start Workers

Open 3-4 terminal windows, each as a worker slot:

# Terminal 2 — Builder
claude -p "/sdlc-worker --slot T2"

# Terminal 3 — Builder
claude -p "/sdlc-worker --slot T3"

# Terminal 4 — Reviewer
claude -p "/sdlc-worker --slot T4"

# Terminal 5 — Tester
claude -p "/sdlc-worker --slot T5"

4. Start the Orchestrator

# Add to crontab (runs every 15 minutes)
crontab -e
# Add: */15 * * * * ~/.claude/sdlc/agentflow-cron.sh >> /tmp/agentflow-orchestrate.log 2>&1

5. Monitor and Intervene

Open your Kanban board on your phone. Watch tasks flow through the pipeline. Drag any card to "Needs Human" to intervene. Run /sdlc-health for a terminal dashboard.

6. Stop the Pipeline

claude -p "/sdlc-stop"

Quality Gates

Each stage enforces specific gates before promotion:

  • Build to Review: tsc + eslint + npm test must all pass (deterministic)
  • Review to Test: Adversarial reviewer must list 3 issues before passing
  • Test to Integrate: 80% coverage threshold on new files
  • Integrate to Done: Full test suite on main after merge; auto-reverts on failure

Cost Tracking

Per-task cost tracking with stage ceilings (Sonnet defaults):

  • Research: ~$0.10
  • Build: ~$0.40
  • Review: ~$0.10
  • Test: ~$0.05
  • Integrate: ~$0.03

Automatic guardrails: warning at $3/$8, hard stop at $10/$20 (Sonnet/Opus) with human escalation.

Safety and Recovery

  • Auto-revert: Integration failures trigger git revert (new commit, never force-push)
  • Blocked tasks: After 2 failed attempts, tasks escalate to human review
  • Dead agent detection: Heartbeat every 5 min, reassign after 10 min timeout
  • Graceful shutdown: /sdlc-stop drains workers, returns unstarted tasks to backlog
  • Scope creep detection: PR diff files compared against predicted files list
  • Spec drift detection: SHA-256 hash comparison catches requirement changes mid-sprint

Installation

# Clone the repo
git clone https://github.com/UrRhb/agentflow.git

# Copy skills and prompts to your Claude Code config
cp -r agentflow/skills/* ~/.claude/skills/
cp -r agentflow/prompts/* ~/.claude/sdlc/prompts/
cp agentflow/conventions.md ~/.claude/sdlc/conventions.md

Or install as a Claude Code plugin:

/plugin marketplace add UrRhb/agentflow
/plugin install agentflow

Best Practices

  • Do: Write a clear SPEC.md before running /spec-to-board
  • Do: Start with 3-4 workers for a typical project
  • Do: Monitor from your Kanban board and drag cards to "Needs Human" when needed
  • Do: Review LEARNINGS.md periodically — it captures common failure patterns
  • Don't: Skip the deterministic quality gates — they catch most issues cheaply
  • Don't: Force-push to main — AgentFlow uses git revert for safety
  • Don't: Run more workers than your project's parallelism supports

Troubleshooting

Problem: Worker appears stuck or dead

Symptoms: Task card hasn't moved in 15+ minutes, no new comments Solution: The orchestrator detects dead agents via heartbeat and reassigns after 10 minutes. If the issue persists, run /sdlc-health to check status and manually drag the card back to Backlog.

Problem: Cost guardrail triggered

Symptoms: Task moved to "Needs Human" with COST:CRITICAL tag Solution: Review the task's comment thread for accumulated context. Decide whether to increase the budget, simplify the task, or split it into smaller pieces.

Problem: Integration test failure after merge

Symptoms: Task auto-reverted from main Solution: The auto-revert preserves main stability. Check the task's retry context in comments, which carries what was tried and what failed. The next worker assigned will use this context.

Related Skills

  • @brainstorming - Use before AgentFlow to design your SPEC.md
  • @writing-plans - Complements spec writing for task decomposition
  • @test-driven-development - Works well with AgentFlow's quality gates
  • @subagent-driven-development - Alternative approach to multi-agent coordination

Additional Resources

Limitations

  • Use this skill only when the task clearly matches the scope described above.
  • Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
  • Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.

Frequently asked questions about AgentFlow

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