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Run Workflow

OrganizationPopular
ginlix-ai
run-workflow

Orchestrate parallel subagent pipelines from a JavaScript workflow script. Fan out work across many items (tickers, filings, findings) then synthesize, or run a saved workflow by name. Unlocks the RunWorkflow tool.

Overview

Publisherginlix-ai
RepositoryLangAlpha
Skill namerun-workflow
Stars
1.8K
Forks
288
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 ginlix-ai on GitHub. Read the source before you install it.

Installation

Install the Run Workflow 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/ginlix-ai/LangAlpha.git /tmp/LangAlpha
mkdir -p .claude/skills
cp -r /tmp/LangAlpha/plugins/langalpha_service/skills/run-workflow .claude/skills/run-workflow
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Run Workflow 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 Run Workflow 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 Run Workflow 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.

Programmatic Workflows (RunWorkflow)

Use RunWorkflow when a deterministic pipeline should orchestrate multiple subagents — fan-out research then synthesize, classify then act per item, generate then verify. Prefer it over issuing many Task calls yourself when the dispatches are data-driven (one per ticker, per filing, per finding). Do NOT use it for a single subagent (use Task) or for code that dispatches nothing (use ExecuteCode).

The script

You write JavaScript (ES2020). It executes server-side: the script itself cannot touch the workspace filesystem — the subagents it dispatches can. The script must declare a pure object literal first:

js
export const meta = { name: 'ticker-briefs', description: 'Fan out research, synthesize' }

name (letters, digits, -, _) and description are required; no variables or function calls inside the literal. The rest of the body is free-form async JS — top-level await and return both work, and the return value (JSON-serializable) becomes the run result. Return a synthesis rather than the raw children: a large result is clipped for display, and a clipped object is unparseable.

Built-ins

  • await agent(prompt, opts?) — dispatch one subagent, resolve to its result text. The child starts blank: it sees nothing of this conversation, of the script, or of its sibling children, so the prompt must carry everything it needs — and its final text is the whole of what comes back. opts: agentType (default 'general-purpose'; same types as Task), label (display name), phase (progress group), schema (JSON Schema — the child answers as matching JSON and the resolved value is the parsed object, or null if it cannot).
  • await pipeline(items, ...stages)the default for multi-stage work. Each item flows through every stage independently, with NO barrier between stages: item A can be in stage 3 while item B is still in stage 1, so the run costs the slowest single chain rather than the sum of each stage's slowest item. Each stage receives (prevResult, originalItem, index); a throwing stage nulls that item and skips its remaining stages.
  • await parallel(thunks) — run an array of () => Promise thunks concurrently, resolving to results in order; already-started promises (parallel([agent(...), ...])) work too. Use it for a single fan-out, or where the next step genuinely needs the whole set at once — dedup across all results, an early exit when the count is zero, one child weighing the others. Needing to map/filter between stages is not such a case: do that inside a pipeline stage.
  • phase(title) / log(message) — progress markers streamed live to the user.
  • args — the params value passed to RunWorkflow, verbatim.

Failure semantics:

  • A failed slot resolves to null — the child errored, timed out, or the run had already spent its dispatch cap. Read null as "no result from this call", never as "the child ran and found nothing": a run whose children all return null has produced nothing, so check before reporting success and write the synthesis to survive partial results.
  • A call your script got wrong — unknown agentType, an oversized prompt or schema — is a bug rather than a failure, and so is an ordinary typo or a wrong shape handed to a helper. Those end the run with the real error, in a parallel slot or a pipeline stage too, instead of leaving you a silent list of nulls to explain.

Limits (defaults): 64 dispatches per run, 8 running at once — extra agent() calls queue, so fan out freely — and 30 minutes per child.

Examples

Single fan-out — one dispatch per item, synthesized in JS:

js
export const meta = { name: 'ticker-briefs', description: 'Research each ticker, then synthesize' }

phase('Research')
const briefSchema = {
  type: 'object',
  properties: { summary: { type: 'string' }, risks: { type: 'array', items: { type: 'string' } } },
  required: ['summary'],
}
const results = await parallel(args.tickers.map((t) => () =>
  agent(`Research ${t}: fundamentals, recent news, key risks.`, { agentType: 'research', label: t, schema: briefSchema })))

phase('Synthesize')
const briefs = {}
const failed = []
results.forEach((r, i) => { if (r !== null) briefs[args.tickers[i]] = r; else failed.push(args.tickers[i]) })
log(`${Object.keys(briefs).length} briefs, ${failed.length} failed`)
return { briefs, failed }

Two stages per item, no barrier — a slow filing never holds up the others:

js
export const meta = { name: 'filing-risk-sweep', description: 'Summarize each filing, then stress-test it' }

const reviewed = await pipeline(
  args.tickers,
  (ticker) => agent(`Summarize ${ticker}'s latest 10-Q: segment results, guidance changes, new risk language.`,
    { agentType: 'research', label: ticker, phase: 'Read' }),
  (summary, ticker) => summary === null ? null : agent(
    `Challenge this ${ticker} summary — what does it overstate, omit, or take on trust?\n\n${summary}`,
    { agentType: 'equity-analyst', label: `${ticker} review`, phase: 'Challenge' }),
)

log(`${reviewed.filter((r) => r !== null).length}/${args.tickers.length} reviewed`)
return Object.fromEntries(args.tickers.map((t, i) => [t, reviewed[i]]))

Set phase per dispatch rather than calling phase() inside a stage: items run concurrently, so a global marker set mid-pipeline reflects whichever item reached it last. Guard each stage on its input, and test against null rather than truthiness — 0, false and "" are answers a child succeeded with, and summary && agent(...) would drop them as failures.

Saved workflows

  • Workflows live at .agents/workflows/<name>.js — the file is the whole script, meta included, and meta.name must equal <name>. List what is already there with ls .agents/workflows/; run one with RunWorkflow(workflow="<name>", params={...}).
  • Write .agents/workflows/<name>.js to save a workflow you expect to run again; it stays available across threads.

Running

RunWorkflow(script=..., params={...}) (or script_path=..., or workflow="<name>") returns a task id immediately and runs in the background — continue other work, then poll TaskOutput(task_id="...") for progress or the final result (add timeout=120 to block). Each dispatched child is a real background task: drill into a truncated result with TaskOutput(task_id="<child task_id>"). Run artifacts (per-child records, result.json) land under .agents/threads/<thread>/workflows/<run-id>/.

Frequently asked questions

What does the Run Workflow AI skill do?

Orchestrate parallel subagent pipelines from a JavaScript workflow script. Fan out work across many items (tickers, filings, findings) then synthesize, or run a saved workflow by name. Unlocks the RunWorkflow tool.

Why use Run Workflow on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ginlix-ai/LangAlpha/tree/main/plugins/langalpha_service/skills/run-workflow. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Run Workflow?

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 Run Workflow?

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

Is the Run Workflow AI skill free?

Yes. It is published on GitHub by ginlix-ai 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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