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Agentflow

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FrancoStino
agentflow

Orchestrate autonomous AI development pipelines through your Kanban board (Asana, GitHub Projects, Linear). Manages multi-worker Claude Code dispatch, deterministic quality gates, adversarial review, per-task cost tracking, and recoverable pipeline design.

Overview

PublisherFrancoStino
Repositoryopencode-skills-collection
Skill nameagentflow
Stars
90
Forks
12
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 FrancoStino on GitHub. Read the source before you install it.

Installation

Install the Agentflow 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/FrancoStino/opencode-skills-collection/tree/main/bundled-skills/agentflow
  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/FrancoStino/opencode-skills-collection.git /tmp/opencode-skills-collection
mkdir -p .claude/skills
cp -r /tmp/opencode-skills-collection/bundled-skills/agentflow .claude/skills/agentflow
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Agentflow 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 Agentflow 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 Agentflow 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.

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, recoverable task state (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 orchestration designed to recover after 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 detectable errors before the more expensive review; coverage and cost must be measured. AI review comes after, as a second layer.

Adversarial Review

An independent review should report only substantiated findings and incomplete coverage. Do not require an arbitrary number of flaws or fabricate issues to pass a gate.

Transitive Priority Dispatch

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

Prerequisites and authorization

This AAS tree contains the guide only. Slash commands, cron script, worker runtime, board integration and templates named below belong to the separately reviewed upstream installation; do not claim they exist locally. Verify a fixed upstream revision, license, schemas and setup first. Creating board tasks/comments, spawning workers, configuring schedules, paid model calls, merging and publishing each require the user's applicable authorization. Preserve existing host files and use the current repository maintainer gates.

For concurrent workers, require atomic task claims or leases, disjoint write ownership, bounded concurrency and verified artifact SHAs. A stale heartbeat does not prove a worker stopped; prevent duplicate paid jobs or writes before reassignment.

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:

bash
# 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

bash
# Add to crontab (runs every 15 minutes)
crontab -e
# Add: */15 * * * * ~/.claude/sdlc/agentflow-cron.sh >> /path/to/private/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: Evidence-based review resolves blocking findings and records coverage
  • Test to Integrate: 80% coverage threshold on new files
  • Integrate to Done: Required protected merge checks plus post-merge verification; scoped rollback follows the repository workflow

Cost Tracking

The following contributed numbers are historical illustrations, not current prices or implemented guarantees. Determine provider/model/tool charges, reserve the agreed currency budget before dispatch, track retries and parallel costs, then reconcile actual billing.

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 require a verified scoped rollback; a revert may not undo database changes or external effects
  • 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

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

# Copy skills and prompts to your Claude Code config
# Inspect the fixed checkout, license and host-supported setup.
# Copy only explicitly selected files to the authorized host location, preserving existing files.

Or install as a Claude Code plugin:

bash
/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 protected scoped rollback for recovery
  • 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: A verified revert may restore source behavior, but does not by itself restore data or guarantee 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

What does the Agentflow AI skill do?

Orchestrate autonomous AI development pipelines through your Kanban board (Asana, GitHub Projects, Linear). Manages multi-worker Claude Code dispatch, deterministic quality gates, adversarial review, per-task cost tracking, and recoverable pipeline design.

Why use Agentflow on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/FrancoStino/opencode-skills-collection/tree/main/bundled-skills/agentflow. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Agentflow?

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 Agentflow?

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

Is the Agentflow AI skill free?

Yes. It is published on GitHub by FrancoStino 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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