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Taskflow

OrganizationPopular
spinabot
taskflow

Use when work should span one or more detached tasks but still behave like one job with a single owner context. TaskFlow is the durable flow substrate under authoring layers like Lobster, ACPX, plugins, or plain code. Keep conditional logic in the caller; use TaskFlow for flow identity, child-task linkage, waiting state, revision-checked mutations, and user-facing emergence.

Overview

Publisherspinabot
Repositorybrigade
Skill nametaskflow
Stars
4.4K
Forks
50
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 spinabot on GitHub. Read the source before you install it.

Installation

Install the Taskflow 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/spinabot/brigade.git /tmp/brigade
mkdir -p .claude/skills
cp -r /tmp/brigade/skills/taskflow .claude/skills/taskflow
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Taskflow 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 Taskflow 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 Taskflow 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.

TaskFlow

Use TaskFlow when a job needs to outlive one prompt or one detached run, but you still want one owner session, one return context, and one place to inspect or resume the work.

When to use it

  • Multi-step background work with one owner
  • Work that waits on detached ACP or subagent tasks
  • Jobs that may need to emit one clear update back to the owner
  • Jobs that need small persisted state between steps
  • Plugin or tool work that must survive restarts and revision conflicts cleanly

What TaskFlow owns

  • flow identity
  • owner session and requester origin
  • currentStep, stateJson, and waitJson
  • linked child tasks and their parent flow id
  • finish, fail, cancel, waiting, and blocked state
  • revision tracking for conflict-safe mutations

It does not own branching or business logic. Put that in Lobster, acpx, or the calling code.

Current runtime shape

Canonical plugin/runtime entrypoint:

  • api.runtime.tasks.flow
  • api.runtime.taskFlow still exists as an alias, but api.runtime.tasks.flow is the canonical shape

Binding:

  • api.runtime.tasks.flow.fromToolContext(ctx) when you already have trusted tool context with sessionKey
  • api.runtime.tasks.flow.bindSession({ sessionKey, requesterOrigin }) when your binding layer already resolved the session and delivery context

Managed-flow lifecycle:

  1. createManaged(...)
  2. runTask(...)
  3. setWaiting(...) when waiting on a person or an external system
  4. resume(...) when work can continue
  5. finish(...) or fail(...)
  6. requestCancel(...) or cancel(...) when the whole job should stop

Design constraints

  • Use managed TaskFlows when your code owns the orchestration.
  • One-task mirrored flows are created by core runtime for detached ACP/subagent work; this skill is mainly about managed flows.
  • Treat stateJson as the persisted state bag. There is no separate setFlowOutput or appendFlowOutput API.
  • Every mutating method after creation is revision-checked. Carry forward the latest flow.revision after each successful mutation.
  • runTask(...) links the child task to the flow. Use it instead of manually creating detached tasks when you want parent orchestration.

Example shape

ts
const taskFlow = api.runtime.tasks.flow.fromToolContext(ctx);

const created = taskFlow.createManaged({
  controllerId: "my-plugin/inbox-triage",
  goal: "triage inbox",
  currentStep: "classify",
  stateJson: {
    businessThreads: [],
    personalItems: [],
    eodSummary: [],
  },
});

const classify = taskFlow.runTask({
  flowId: created.flowId,
  runtime: "acp",
  childSessionKey: "agent:main:subagent:classifier",
  runId: "inbox-classify-1",
  task: "Classify inbox messages",
  status: "running",
  startedAt: Date.now(),
  lastEventAt: Date.now(),
});

if (!classify.created) {
  throw new Error(classify.reason);
}

const waiting = taskFlow.setWaiting({
  flowId: created.flowId,
  expectedRevision: created.revision,
  currentStep: "await_business_reply",
  stateJson: {
    businessThreads: ["slack:thread-1"],
    personalItems: [],
    eodSummary: [],
  },
  waitJson: {
    kind: "reply",
    channel: "slack",
    threadKey: "slack:thread-1",
  },
});

if (!waiting.applied) {
  throw new Error(waiting.code);
}

const resumed = taskFlow.resume({
  flowId: waiting.flow.flowId,
  expectedRevision: waiting.flow.revision,
  status: "running",
  currentStep: "finalize",
  stateJson: waiting.flow.stateJson,
});

if (!resumed.applied) {
  throw new Error(resumed.code);
}

taskFlow.finish({
  flowId: resumed.flow.flowId,
  expectedRevision: resumed.flow.revision,
  stateJson: resumed.flow.stateJson,
});

Keep conditionals above the runtime

Use the flow runtime for state and task linkage. Keep decisions in the authoring layer:

  • business → post to Slack and wait
  • personal → notify the owner now
  • later → append to an end-of-day summary bucket

Operational pattern

  • Store only the minimum state needed to resume.
  • Put human-readable wait reasons in blockedSummary or structured wait metadata in waitJson.
  • Use getTaskSummary(flowId) when the orchestrator needs a compact health view of child work.
  • Use requestCancel(...) when a caller wants the flow to stop scheduling immediately.
  • Use cancel(...) when you also want active linked child tasks cancelled.

Examples

  • See skills/taskflow/examples/inbox-triage.lobster
  • See skills/taskflow/examples/pr-intake.lobster
  • See skills/taskflow-inbox-triage/SKILL.md for a concrete routing pattern

Frequently asked questions

What does the Taskflow AI skill do?

Use when work should span one or more detached tasks but still behave like one job with a single owner context. TaskFlow is the durable flow substrate under authoring layers like Lobster, ACPX, plugins, or plain code. Keep conditional logic in the caller; use TaskFlow for flow identity, child-task linkage, waiting state, revision-checked mutations, and user-facing emergence.

Why use Taskflow on TypingMind?

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

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

Which AI models can use Taskflow?

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 Taskflow?

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

Is the Taskflow AI skill free?

Yes. It is published on GitHub by spinabot 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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