Tmux Agent Lane Orchestrator

Community

vincentkoc

tmux-agent-lane-orchestrator

Monitor and coordinate one tmux agent lane, reconstruct worker state from panes and recent Codex logs, classify progress and blockers, and produce concise manager summaries. Use when multiple coding-agent workers run in tmux windows named L1, L2, and similar lane identifiers.

Overview

Publisher	vincentkoc
Repository	`dotskills`
Skill name	`tmux-agent-lane-orchestrator`
Stars	112
Forks	9
Bundled files	5
License	MIT
Links	Skill source Repository

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.
5 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by vincentkoc on GitHub. Read the source before you install it.

Installation

Install the Tmux Agent Lane Orchestrator AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/vincentkoc/dotskills/tree/main/skills/tmux-agent-lane-orchestrator
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.

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/vincentkoc/dotskills.git /tmp/dotskills
mkdir -p .claude/skills
cp -r /tmp/dotskills/skills/tmux-agent-lane-orchestrator .claude/skills/tmux-agent-lane-orchestrator

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Tmux Agent Lane Orchestrator 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 Tmux Agent Lane Orchestrator 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 Tmux Agent Lane Orchestrator 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.

tmux Agent Lane Orchestrator

Purpose

Turn a tmux window of coding-agent workers into a visible, auditable lane with clear state and next actions.

When to use

Monitor or summarize a tmux worker lane.
Reconstruct worker state after a manager cold start.
Detect duplicated, idle, waiting, blocked, or risky work.
Coordinate several coding-agent panes without mutating them blindly.

Workflow

Establish lane scope.
- Prefer an explicit lane number.
- Otherwise infer it from a current tmux window named L<number>.
- Do not inspect other lanes unless the operator expands scope.
Capture lane state.
- python3 scripts/lane_snapshot.py --lane <number>
- Add --session <name> outside the active tmux session.
- If a descendant command does not contain its resumed thread ID, repeat --thread L<number>.<pane>=<thread-id> from separately verified evidence.
- Add --json for machine-readable output.
- Add --cursor-file <path> only when bounded incremental state is wanted; the helper writes no persistent state by default.
- Records larger than the smaller of --log-bytes and --total-log-bytes are skipped through their next newline. The cursor reports discarding_record while skipping; later events resume.
Cross-check panes and logs.
- Pane titles alone are weak evidence.
- Resolve pane ID to shell PID, descendant Codex PID, exact thread ID, the state-database rollout path, and the newest turn.
- Shared cwd and generic terms such as CI, failed, or running never establish ownership.
- Missing or conflicting identity is unknown, never a restore target.
Classify each pane.
- Use the newest exact turn and changing event/tool/token counters.
- A completed turn after an earlier error is completed, not blocked.
- Report bounded bytes read and truncation with the state.
- Include the evidence and next action, not only the label.
Intervene conservatively.
- Inspect before steering.
- Avoid duplicate heavy checks.
- Do not kill or mutate another pane without explicit scope.
- Prefer reversible actions and targeted commands.
Summarize in manager style.
- One line per pane: mission, state, evidence, blocker, next action.
- Add one short manager judgment about where attention belongs.

Read references/factory-model.md when designing lane responsibilities or escalation policy.

Inputs

tmux session name.
Lane number or L<number> window.
Optional log keywords and capture depth.
Optional exact pane/thread declarations and bounded cursor path.
Optional operator-provided worker mission map.

Outputs

Current pane ID, shell PID, descendant agent PID, exact thread, and rollout snapshot.
Per-pane state classification with evidence.
Per-file and total log bytes read, cursor mode, and truncation status.
Concise manager summary and safe next actions.

Flow

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Tmux Agent Lane Orchestrator AI skill do?

Why use Tmux Agent Lane Orchestrator on TypingMind?

Because you install it once and use it with any model. Tmux Agent Lane Orchestrator 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 Tmux Agent Lane Orchestrator in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/vincentkoc/dotskills/tree/main/skills/tmux-agent-lane-orchestrator. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Tmux Agent Lane Orchestrator?

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 Tmux Agent Lane Orchestrator?

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

Is the Tmux Agent Lane Orchestrator AI skill free?

Yes. It is published on GitHub by vincentkoc 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

tmux Agent Lane Orchestrator

Purpose

When to use

Workflow

Inputs

Outputs

Flow

Bundled files

Frequently asked questions

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