Workflow Authoring logo

Workflow Authoring

Organization
VectorSpaceLab
workflow-authoring

Writes deterministic DisCo workflow scripts that coordinate subagents with stable coverage IDs, bounded recovery, explicit usage semantics, and structured environment handoffs.

Overview

PublisherVectorSpaceLab
RepositoryAREX-Skill
Skill nameworkflow-authoring
Stars
341
Forks
26
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 VectorSpaceLab on GitHub. Read the source before you install it.

Installation

Install the Workflow Authoring 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/VectorSpaceLab/AREX-Skill/tree/main/cli/packages/coding-agent/src/disco/skills/workflow-authoring
  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/VectorSpaceLab/AREX-Skill.git /tmp/AREX-Skill
mkdir -p .claude/skills
cp -r /tmp/AREX-Skill/cli/packages/coding-agent/src/disco/skills/workflow-authoring .claude/skills/workflow-authoring
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Workflow Authoring 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 Workflow Authoring 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 Workflow Authoring 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.

Workflow Authoring

Use the workflow tool only for genuinely decomposable work. The script is an orchestration program, not a container for the task's entire natural-language brief.

When another skill requires workflow orchestration, read this file completely before the first workflow call. Do not infer agent() option fields from a producer's private report schema.

Script and args boundary

  • Keep script short: metadata, phases, job mapping, agent()/parallel() calls, coverage accounting, and the final return value.
  • Put long or Markdown-rich prompts in args.jobs or args.briefs. This keeps Markdown backticks, code spans, quotes, and multiline content out of JavaScript template literals and prevents avoidable parser failures.
  • Do not put an unescaped Markdown backtick inside a script template literal. If a parser error does occur, use its source line and caret, then move that payload to args instead of escaping one occurrence and retaining the fragile structure.
  • parallel() receives functions, not already-started promises: await parallel(jobs.map((job) => () => agent(job.prompt, options))).

Stable coverage contract

Every job needs one canonical lowercase-hyphen id. Pass that same value as subSkill when it represents a generated sub-skill. A coordinated workflow should return a ledger shaped like:

js
const rows = await parallel(jobs.map((job) => async () => {
  const result = await agent(job.prompt, {
    label: job.id,
    subSkill: job.id,
    environment: args.environment,
  });
  return { id: job.id, ok: result !== null, result };
}));

const missing = rows
  .filter((row) => !row || row.ok !== true)
  .map((row, index) => row?.id ?? jobs[index].id);

return {
  rows,
  complete: missing.length === 0,
  missing,
  errors: rows
    .filter((row) => row && row.ok !== true)
    .map((row) => ({ id: row.id, error: row.error })),
};

results.length is only the number of returned positions; it is never the success count. null, a missing row, or an explicit ok: false is incomplete coverage.

Recovery

For recovery, create a new workflow call with:

  • args.jobs containing only the current missing IDs and their original briefs;
  • recoveryOfRunId set to the original run ID;
  • recoveryRound incremented for the new round;
  • maxRecoveryRounds left at the default 50 unless the user has a reason to choose another value.

Within one script, recoverMissing(jobs, worker, options) is also available. It only submits the current pending IDs, never reruns successful IDs, and stops with an explicit stoppedReason after the configured round cap or two consecutive rounds with no reduction in the missing set. An incomplete result must be shown to the main agent and resolved before integration; never silently merge the successful subset as if the batch were complete.

Timeouts, retries, and usage

  • Omit agentTimeoutMs unless the user explicitly asks for a hard time bound. The runtime default is no hard timeout.
  • A timeout attempt is aborted and drained before its default one retry begins. Do not design workflows that assume the timed-out agent can keep writing in the background.
  • Use tokenBudget only when the user asks for a spend cap.
  • Final usage is terminal usage when available. Live usage is observability only; it is not finalized accounting. Estimated fallback usage is labeled as such, and cacheRead is shown separately.

Prepared environment handoff

After prepare-repo-skill-env succeeds, use the report's canonical workflowEnvironment object. Pass its verified executable and expected distribution version structurally, for example:

js
args: {
  environment: {
    executable: preparedEnvironmentExecutable,
    cwd: repositoryPath,
    package: "target-package",
    version: "target-version",
  },
  jobs,
}

Every subagent must pass environment: args.environment to agent(). The runtime executes the assertion before creating the session. A missing executable or version mismatch is a hard failure; never replace it with ambient python, a different virtual environment, or an unverified package installation.

The private prepare-env report may also contain legacy evidence fields. Their only supported migration mapping is:

text
pythonExecutable       -> executable
expectedDistribution   -> package
expectedVersion        -> version
assertBeforeStartup    -> omitted; assertion is always required

Do not author new workflows with those legacy names. The runtime normalizes this known shape only as a visible deprecation safety net. Canonical and legacy values that disagree are a hard contract failure before subagent startup; no ambient runtime is guessed.

Integration gate

Only integrate generated files after complete === true. If the result is incomplete, preserve the original jobs and IDs, start a recovery workflow for the current missing set, and report any no-progress or safety-cap stop explicitly.

Frequently asked questions

What does the Workflow Authoring AI skill do?

Writes deterministic DisCo workflow scripts that coordinate subagents with stable coverage IDs, bounded recovery, explicit usage semantics, and structured environment handoffs.

Why use Workflow Authoring on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/VectorSpaceLab/AREX-Skill/tree/main/cli/packages/coding-agent/src/disco/skills/workflow-authoring. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Workflow Authoring?

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 Workflow Authoring?

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

Is the Workflow Authoring AI skill free?

Yes. It is published on GitHub by VectorSpaceLab 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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