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Long Task Coordinator

Community
zhaono1
long-task-coordinator

Coordinates multi-session, delegated, or long-running work with persistent state, recovery checks, and explicit status transitions. Use when a task spans multiple turns, multiple agents, background jobs, or scheduled loops, or when interrupted work must be resumed reliably.

Overview

Publisherzhaono1
Repositoryagent-playbook
Skill namelong-task-coordinator
Stars
79
Forks
12
Bundled files
2
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.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by zhaono1 on GitHub. Read the source before you install it.

Installation

Install the Long Task Coordinator 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/zhaono1/agent-playbook.git /tmp/agent-playbook
mkdir -p .claude/skills
cp -r /tmp/agent-playbook/skills/long-task-coordinator .claude/skills/long-task-coordinator
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Long Task Coordinator 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 Long Task Coordinator 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 Long Task Coordinator 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.

Long Task Coordinator

Keep long-running work recoverable, stateful, and honest.

When to Use This Skill

Use this skill when the work:

  • Spans multiple turns or multiple sessions
  • Involves handoffs to workers, subagents, or background jobs
  • Needs explicit waiting states instead of "still looking" updates
  • Must survive interruption and resume from a durable state file

Skip this skill for small, single-turn tasks. Use planning-with-files when simple planning is enough and recovery logic is not the main concern.

Related Skills

  • planning-with-files keeps multi-step work organized in files.
  • workflow-orchestrator chains follow-up skills after milestones.
  • long-task-coordinator makes long-running work resumable, auditable, and safe to hand off.

Core Rules

1. Create one source of truth

For any real long task, maintain one durable state file. Chat history is not a reliable state store.

The state file should capture at least:

  • Goal
  • Success criteria
  • Current status
  • Current step
  • Completed work
  • Next action
  • Next checkpoint
  • Blockers
  • Active owners or workers

2. Separate roles only when needed

Use the smallest role model that fits the task:

  • Origin: owns the goal and acceptance criteria
  • Coordinator: owns state, sequencing, and recovery
  • Worker: executes bounded sub-work
  • Watchdog: checks liveness and recovery only

Simple tasks can collapse these roles into one agent. Long or delegated tasks should make the split explicit.

3. Run every cycle in this order

For each coordination round:

text
READ -> RECOVER -> DECIDE -> PERSIST -> REPORT -> END

Do not report conclusions before the state file has been updated.

4. Treat awaiting-result as a valid state

If a worker or background job was dispatched successfully, the task is not failing just because the result is not back yet.

Valid transitions include:

  • running -> awaiting-result
  • awaiting-result -> running
  • running -> paused
  • running -> complete

5. Non-terminal rounds must create real progress

A coordination round is only valid if it does at least one of the following:

  • Dispatches bounded work
  • Consumes new results
  • Updates the current stage or decision
  • Persists a new next step or checkpoint
  • Performs explicit recovery

If nothing changed, do not pretend the task advanced.

6. Keep recovery separate from domain work

Recovery answers:

  • Did execution drift from the saved state?
  • Is the expected worker result still pending?
  • Do we need to wait, retry, or re-dispatch?

Domain work answers:

  • What should we build, analyze, or deliver next?

Recover first, then continue domain work.

Operating Workflow

Step 1: Decide whether the task needs coordination

Use this skill when at least one is true:

  • The task will outlive the current turn
  • The task will hand off work to another execution unit
  • The task needs checkpoints, polling, or scheduled follow-up
  • The task has enough complexity that loss of state would be expensive

Step 2: Create or load the state file

Prefer a path that is easy to rediscover, such as:

  • docs/<topic>-execution-plan.md
  • docs/<topic>-state.md
  • worklog/<topic>-state.md

If no durable state exists yet, create one from references/workflow.md.

Step 3: Recover before acting

At the start of every new round:

  1. Read the state file
  2. Check whether the recorded next step still makes sense
  3. Confirm whether any delegated work returned
  4. Repair stale assumptions before new action

Step 4: Persist before reporting

After deciding the next action:

  1. Update the state file
  2. Record new status, owners, blockers, and checkpoint
  3. Only then report progress to the user or caller

Step 5: Close the round honestly

End each round with one of these states:

  • running
  • awaiting-result
  • paused
  • blocked
  • complete

The reported status should match the persisted status exactly.

Output Expectations

When using this skill, produce updates that are grounded in saved state:

  • What status the task is in now
  • What changed this round
  • What is expected next
  • What would unblock or complete the task

Acceptance Criteria

Treat the coordination work as complete only when all relevant items below are true:

  • A durable state file exists in a predictable path
  • The saved status matches the real task state
  • Completed work, next action, and blockers are recorded explicitly
  • Any delegated work has a named owner and a return condition
  • The final report is derived from the persisted state, not from transient reasoning

If the task is not truly complete, end in running, awaiting-result, paused, or blocked rather than pretending the work is done

Anti-Patterns

Avoid:

  • Reconstructing progress from memory instead of the state file
  • Reporting a conclusion before saving it
  • Marking waiting as failure
  • Ending a round with no new action and no state change
  • Mixing recovery checks with domain decisions in one fuzzy step

References

  • references/workflow.md - Detailed workflow, state template, and recovery checklist

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 Long Task Coordinator AI skill do?

Coordinates multi-session, delegated, or long-running work with persistent state, recovery checks, and explicit status transitions. Use when a task spans multiple turns, multiple agents, background jobs, or scheduled loops, or when interrupted work must be resumed reliably.

Why use Long Task Coordinator on TypingMind?

Because you install it once and use it with any model. Long Task Coordinator 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 Long Task Coordinator in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/zhaono1/agent-playbook/tree/main/skills/long-task-coordinator. 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 Long Task Coordinator?

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 Long Task Coordinator?

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

Is the Long Task Coordinator AI skill free?

Yes. It is published on GitHub by zhaono1 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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