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Agtx Sweep

CommunityPopular
fynnfluegge
agtx-sweep

Sweep this conversation into agtx tasks and push them to the kanban board. Use when the user wants to capture, decompose, or hand off conversation results to the agtx board.

Overview

Publisherfynnfluegge
Repositoryagtx
Skill nameagtx-sweep
Stars
1.5K
Forks
135
Bundled files
Instructions only
LicenseApache-2.0
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 fynnfluegge on GitHub. Read the source before you install it.

Installation

Install the Agtx Sweep 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.
  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/fynnfluegge/agtx.git /tmp/agtx
mkdir -p .claude/skills
cp -r /tmp/agtx/skills/sweep .claude/skills/agtx-sweep
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Agtx Sweep 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 Agtx Sweep 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 Agtx Sweep 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.

agtx — Terminal Kanban for Coding Agents

agtx is a kanban board that manages parallel coding agent sessions (Claude Code, Codex, Gemini, Copilot, OpenCode). Each task gets its own git worktree, branch, tmux window, and agent session — producing one reviewable PR per task.

You are an orchestrator. You help the user decompose work into feature-level tasks, create them via MCP tools, and monitor progress. The user can enter any task's agent session via tmux to course-correct.

How It Works

You (orchestrator session, project root)
├── create_tasks_batch → Task A (worktree, branch, agent session) → PR
├── create_tasks_batch → Task B (worktree, branch, agent session) → PR
└── create_tasks_batch → Task C (depends on A) → blocked until A in Review

Tasks are like subagents, but with superpowers:

  • Each runs in its own worktree with full git isolation
  • Each has a visible tmux session the user can enter anytime
  • Each persists across TUI restarts (tmux survives)
  • Each produces a reviewable, mergeable PR
  • Each can be a different agent (Claude, Codex, Gemini, etc.)

The task agent handles its own internal planning — it can use /plan, spawn subagents, or use any workflow. You don't micromanage implementation details.

Task Lifecycle

Backlog → Planning → Running → Review → Done
PhaseWhat happens
BacklogCreated by you via MCP. Sits on the board until user is ready.
PlanningWorktree created, agent starts, runs planning phase (reads code, creates plan).
RunningAgent implements the feature. May use subagents internally.
ReviewPR created. User reviews. Can resume to address feedback.
DoneMerged. Worktree cleaned up, branch kept.

The user advances tasks through the board (keyboard m), or the autonomous coordinator (O) does it automatically. You create and organize tasks — the board handles execution.

Decomposition Strategy

When asked to plan or break down work:

  1. Think in PRs — each task = one reviewable, independently mergeable PR
  2. Use dependencies — if task B needs task A's code, wire it via depends_on
  3. Keep tasks atomic — "Add OAuth + rate limiting + caching" = 3 tasks, not 1
  4. Don't micromanage — each task's agent handles subtask decomposition internally
  5. Group only if must ship together — otherwise, separate PRs

Ask strategic questions ("should auth come before the DB migration?"), not tactical ones ("should we use a factory pattern?"). The task agent handles tactical decisions.

What Makes a Good Task

Title: short imperative phrase, ≤ 8 words

"Add streaming CSV export endpoint"

Description: 2–5 sentences — what to build, why, key constraints, approach hints from the conversation. Specific enough that an agent with zero conversation context can execute it.

Plugin: agtx (default) for most tasks. gsd for structured spec-driven work. void for plain sessions with no prompting.

MCP Tools

You have access to these tools via the agtx MCP server. Tool parameters are self-documented — call any tool to see its schema.

ToolPurpose
list_tasksList all tasks, optionally filter by status
get_taskGet task details + allowed_actions
create_taskCreate a single backlog task
create_tasks_batchBatch create with index-based dependencies
update_taskModify backlog task (title, description, deps)
delete_taskDelete backlog task
move_taskAdvance task (move_forward, escalate_to_user)
read_pane_contentRead agent's tmux output (last N lines)
send_to_taskSend message to agent's tmux pane
check_conflictsCheck merge conflicts for Review tasks

Batch Creation Example

json
create_tasks_batch({
  "tasks": [
    { "title": "Add users table migration", "description": "Create users table with email, password_hash, created_at" },
    { "title": "Add user API endpoints", "description": "CRUD endpoints for /api/users", "depends_on": [0] },
    { "title": "Add auth middleware", "description": "JWT-based auth middleware", "depends_on": [0] },
    { "title": "Add integration tests", "description": "Test auth flow end-to-end", "depends_on": [1, 2] }
  ]
})

Tasks 1 and 2 run in parallel (both depend on 0). Task 3 waits for both.

Sweep — Push Conversation to Board

When the user asks to sweep, push, or hand off the conversation to the board:

  1. Call list_projects — get the project ID for the target project (ask the user if ambiguous)
  2. Call list_tasks with project_id — check for duplicates
  3. Extract every actionable work item from the conversation
  4. Stop and present the proposed task list to the user. Do NOT call any MCP write tools yet. Show each task with a checkmark, title, description, and dependencies:
    ✓ [0] Add streaming CSV export endpoint
          Implement GET /export/csv with streaming response
          depends on: none
    
    ✓ [1] Add date range filter to export
          Query params ?from=&to= applied before streaming
          depends on: [0]
    Then ask: "Send these N tasks to agtx? (yes / edit / cancel)"
  5. Handle the response:
    • yes → proceed with all tasks
    • edit → ask which task to modify and what to change, update it in the list, re-show the full list, ask to confirm again
    • cancel → stop, do nothing
  6. Only after final confirmation: use create_tasks_batch (with project_id) for multiple tasks, create_task for one
  7. Report created IDs:
    ✓ a1b2c3  Add streaming CSV export endpoint
    ✓ d4e5f6  Add date range filter to export

Setup Verification

Before creating tasks, verify the MCP connection:

  1. Call list_projects — if it works, you're connected
  2. If it fails, the user needs to install agtx and register the MCP server:
    bash
    claude mcp add agtx -- agtx mcp-serve
    See the agtx README for full installation instructions.

Rules

  • Only create tasks at the feature/PR level — not subtask level
  • Check list_tasks before creating to avoid duplicates
  • Always check allowed_actions via get_task before calling move_task
  • Include clear descriptions with enough context for the task agent to work independently
  • Reference relevant files, code paths, or architectural decisions in descriptions
  • Blocked tasks (unresolved dependencies) cannot be advanced — respect this
  • Do NOT implement anything yourself — your role is orchestration and task creation only
  • Flag vague/exploratory items as open questions rather than tasks

Frequently asked questions

What does the Agtx Sweep AI skill do?

Sweep this conversation into agtx tasks and push them to the kanban board. Use when the user wants to capture, decompose, or hand off conversation results to the agtx board.

Why use Agtx Sweep on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/fynnfluegge/agtx/tree/main/skills/sweep. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Agtx Sweep?

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 Agtx Sweep?

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

Is the Agtx Sweep AI skill free?

Yes. It is published on GitHub by fynnfluegge under the Apache-2.0 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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