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Writing Relayflows

Organization
AgentWorkforce
writing-relayflows

Use when authoring a Relayflows flow (@relayflows/surface / @relayflows/sdk, the journal-based v2 engine — the CLI is `flows`, package versions 2.0.x) in TypeScript or YAML/JSON. Covers the three-rung ladder (run/llm/agent), the resident verbs (human/dispatch/done), verification gates, TypeScript vs YAML authoring, per-step cli/model selection and its resolution order, flows.json, and `flows check`/`run`/`resume` with their real refusal shapes and exit codes. Not for the older, unrelated `@relayflows/core` WorkflowBuilder engine (`.pattern('dag')`/.agent()/.step() chains) that `writing-agent-relay-workflows` and `migrating-persona-to-relayflow` cover — that's a different product despite the similar name.

Overview

PublisherAgentWorkforce
Repositoryrelay
Skill namewriting-relayflows
Stars
875
Forks
73
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 AgentWorkforce on GitHub. Read the source before you install it.

Installation

Install the Writing Relayflows 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/AgentWorkforce/relay/tree/main/.agents/skills/writing-relayflows
  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/AgentWorkforce/relay.git /tmp/relay
mkdir -p .claude/skills
cp -r /tmp/relay/.agents/skills/writing-relayflows .claude/skills/writing-relayflows
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Writing Relayflows 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 Writing Relayflows 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 Writing Relayflows 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.

Overview

Relayflows turns a coding-agent task into steps a journal can inspect, verify, and resume. A flow is data (YAML/JSON) or code (TypeScript) that compiles to the same journal-backed kernel spec. Every effect is journaled before it's treated as real — a journal write that fails fails the step, with no silent fallback.

Name collision warning. This repo also has skills for an older, unrelated engine that is also casually called "Relayflow" (singular) — @relayflows/core's WorkflowBuilder, a chained builder (workflow('name').pattern('dag').agent(...).step(...).run()). That's writing-agent-relay-workflows and migrating-persona-to-relayflow's territory. This skill is the v2 engine: @relayflows/surface's flow() function and the YAML/JSON dialect compiled by @relayflows/sdk. If you see .pattern(, .agent( as a chained builder call, or ctx.workflow.run(), you're in the other engine — stop and use one of those skills instead.

When to use this skill

  • Writing a new .flow.ts or .flow.yaml/.flow.json for the flows CLI (package @relayflows/sdk, binary name flows).
  • Deciding whether a step needs run (shell), llm (bare model call), or agent (harnessed coding agent in a workspace).
  • Wiring up cli/model for an agent or llm step, in either language.
  • Debugging a REFUSED [...] message from flows check or flows run.
  • Choosing between TypeScript and YAML for a given flow.

The ladder

Three step verbs, one per rung — never more (packages/sdk/src/spec.ts, export type StepType = 'deterministic' | 'llm' | 'agent';):

  1. run / deterministic — a shell command. No model. Implicit gate is exit_code == 0.
  2. llm / llm — a bare model call. Prompt in, verified output out. No workspace, no tool use.
  3. agent / agent — a harnessed coding agent in a workspace. Returns { summary, artifacts }, not raw text.

Plus four resident verbs that aren't ladder rungs: human (durable approval), dispatch (hand off to a child flow), done (typed finish), and in YAML, on/triggers (event entry points — out of scope for this skill).

Most flows only need run and llm. Climb to agent once a step needs hands on a real workspace.

Two ways to author the same thing

TypeScript
ts
import { flow } from '@relayflows/surface';

export default flow('hello', async (f) => {
  const greeting = await f.run('echo "Hello from Relayflows"');
  console.log(greeting.trim());

  const answer = await f.agent('greeter', {
    task: 'Reply with one short hello sentence. Do not use tools or modify files.',
    cli: 'claude',
    model: 'claude-sonnet-4-6',
  });
  console.log(answer.summary);

  f.done('success');
});
YAML
yaml
version: '0.1.0'
name: hello
steps:
  - id: greeting
    type: deterministic
    command: 'echo "Hello from Relayflows"'
  - id: greeter
    type: agent
    dependsOn: [greeting]
    instruction: 'Reply with one short hello sentence. Do not use tools or modify files.'
    cli: claude
    model: claude-sonnet-4-6

The real Ctx contract (TypeScript)

packages/surface/src/context.ts, current as of origin/main@86a2ec2:
ts
export interface AgentResult {
  summary: string;
  artifacts: string[];
}

export interface AgentOptions {
  task: string;
  workspace?: string;
  cli?: string;
  model?: string;
}

export interface Ctx {
  run(command: string): Step<string>;
  llm(strings: TemplateStringsArray, ...values: unknown[]): Step<string>;
  llm(
    prompt: string,
    options: { output: Record<string, unknown>; cli?: string; model?: string }
  ): Step<unknown>;
  agent(name: string, options: AgentOptions): Step<AgentResult>;
  human(question: string, options: { to: string }): Promise<boolean>;
  dispatch<T>(flow: string, input: unknown): Promise<T>;
  done(reason: RunCompletionReason): void;
  cloud: CloudHelper;
  slack: SlackHelper;
}

The real step shapes (YAML/JSON, packages/sdk/src/spec.ts)

```ts
ts
interface DeterministicStepSpec {
  type: 'deterministic';
  id: string;
  command: string;
  dependsOn?: string[];
  timeoutMs?: number;
  verification?: VerificationSpec; // omit for implicit exit_code
}

interface LlmStepSpec {
  type: 'llm';
  id: string;
  prompt: string;
  dependsOn?: string[];
  verification?: OutputVerificationSpec;
  model?: string;
  cli?: string;
}

interface AgentStepSpec {
  type: 'agent';
  id: string;
  instruction: string;
  dependsOn?: string[];
  verification?: OutputVerificationSpec;
  agent?: string; // selects a named FlowSpec.agents entry
  cli?: string;
  model?: string;
  surfaces?: { workspace?: { surface: string }[]; streams?: { stream: string }[]; external?: string[] };
  recoveryMode?: 'reset' | 'inspect' | 'manual'; // default 'reset'
  permissions?: {
    fileGlobs?: string[];
    networkAllowlist?: string[];
    accessPreset?: 'readonly' | 'readwrite';
  };
}

interface FlowSpec {
  version: string; // required, e.g. '0.1.0' — not optional
  name?: string;
  cli?: string; // flow-level CLI default
  agents?: Record<string, { cli: string; model: string }>; // both fields required
  steps: StepSpec[];
  budget?: { maxTokensIn?: number; maxTokensOut?: number; maxDollars?: string };
}

Verification gates

Verification is control flow, not decoration — a gate decides whether a step actually completed, not just whether the process exited cleanly (packages/sdk/src/spec.ts, VerificationGateType):
yaml
- id: classify
  type: llm
  prompt: 'Classify this ticket as bug, feature, or question: "the export button does nothing"'
  cli: claude
  model: claude-sonnet-4-6
  verification:
    type: output_contains
    value: bug

cli / model: what a step actually runs on

Both YAML and TypeScript agent/llm steps can set cli and model directly (TypeScript since flows#310, AgentOptions.cli?/.model?). Resolution order for cli — checked once per step by preflight.ts's resolveCli (packages/sdk/src/preflight.ts:265-282), identical regardless of authoring language because both compile to the same StepSpec:
$ flows check hello.flow.yaml   # agent step, no cli anywhere
REFUSED [cli_unresolved] Step "greeter" has no CLI at step, flow, or project level. No flows.json was found from "..." to the filesystem root.
flows.json
json
{ "cli": "claude", "executors": ["cron"], "models": ["claude-sonnet-4-6"] }

Human approval and dispatch (TypeScript resident verbs)

```ts
ts
import { flow } from '@relayflows/surface';

export default flow('ship-feature', async (f) => {
  const plan = await f.agent('planner', {
    task: 'Research and plan: add OAuth2 support',
    workspace: 'acme/api: readonly', // compiles to relayauth path scopes
  });

  const ok = await f.human(`Ship this?\n${plan.summary}`, { to: 'khaliq' });
  if (!ok) return f.done('canceled');

  const pr = await f.dispatch('garden/implement', plan); // hands off to a child flow
  f.done('success');
});

Running it: flows check / run / resume

Real usage (packages/sdk/src/cli.ts):
flows check [--json] <flow.yaml|spec.json>
flows run [--json] [--no-spawn] [--no-observer-link] [--data-dir <dir>] [--local-agent] <flow.yaml|spec.json>
flows run [--json] [--no-spawn] [--no-observer-link] [--data-dir <dir>] [--local-agent] <flow.ts> --input <inline-json-or-file>
flows resume [--json] [--no-spawn] [--no-observer-link] [--data-dir <dir>] <run-id>

Common mistakes

  • Forgetting version in a YAML/JSON FlowSpec. It's required, not optional — flows check refuses a spec without it.
  • Adding agents: to a TypeScript flow() header. FlowHeader has no such field; it throws TypeError: flow header has unknown fields: agents at authoring time. Named-agent maps + agent: selector are YAML/JSON-only (flows#300 tracks TypeScript composition via use:, not yet shipped).
  • Assuming flows.json's models sets a default model. It only validates models already declared elsewhere; it never selects one.
  • Not awaiting a step, or manually .then()-chaining one. Both are refused (unawaited_step / unsupported_verb) rather than silently ignored — the executor closes every root operation's lifecycle explicitly.
  • Running a .flow.ts without --input. Required even for flows that don't use their input argument.
  • Expecting a fifth done() reason. The set is closed: success | step_failed | canceled | budget_exceeded. Don't invent partial or skipped.

What this skill does NOT cover

  • Named-agent maps in TypeScript (agents: { reviewer: { cli, model } } + reuse across steps by name) — YAML/JSON only today. Tracked for TS composition via use: at flows#300.
  • recoveryMode, permissions, surfaces, budget, memory on agent steps — real YAML/JSON fields with no TypeScript equivalent. Author that step in YAML and reach it from TypeScript with f.dispatch if you need them.
  • Cloud execution (flows run --cloud), triggers/webhooks, memory retrieval, and the f.mcp/f.slack helper namespaces — each is its own surface with its own gotchas; see the Relayflows product docs for what's shipped versus designed-but-not-yet-implemented.
  • The older @relayflows/core WorkflowBuilder engine — see writing-agent-relay-workflows and migrating-persona-to-relayflow in this repo.

Quick reference

Verb / fieldLanguageNotes
f.run(command) / type: deterministicbothshell command, implicit exit_code gate
f.llm(...) / type: llmbothbare model call, no workspace
f.agent(name, opts) / type: agentbothharnessed coding agent, returns {summary, artifacts}
f.human(question, {to})TS onlydurable approval; YAML has no equivalent yet
f.dispatch(flow, input)TS onlyhand off to a named child flow
f.done(reason) / —TS / kernelone of success | step_failed | canceled | budget_exceeded
options.cli / step.clibothper-call/step CLI override (TS: flows#310)
options.model / step.modelbothper-call/step model; no flow/project default
agent: <name> + agents: {...}YAML/JSON onlynamed cli/model pair, reused by selector
flows check <file>CLIpure validate + preflight, no daemon
flows run <file> [--input ...]CLIactually executes; .flow.ts needs --input
flows resume <run-id>CLIresume a parked/crashed run

Verified against

AgentWorkforce/flows@86a2ec2 (origin/main). Built packages/surface and packages/sdk from source in a clean worktree (published npm @relayflows/surface@2.0.8 is stale — it predates flows#310 and lacks cli/model on AgentOptions; local build was symlinked in instead), then ran the real CLI:
$ flows check hello.flow.yaml         # this skill's YAML example, cli/model added, flows.json models allowlist set
CHECK PASSED hello.flow.yaml            # exit 0

$ flows check hello.flow.ts            # this skill's TypeScript example
CHECK PASSED hello.flow.ts              # exit 0

$ flows check extract.flow.yaml        # this skill's output_contains example
CHECK PASSED extract.flow.yaml          # exit 0

$ flows check hello.flow.yaml           # same YAML, no flows.json anywhere
REFUSED [cli_unresolved] Step "greeter" has no CLI at step, flow, or project level. ...   # exit 2

$ flows check hello.flow.yaml           # step model not in flows.json's models[]
REFUSED [model_unknown] Step "greeter" declares model "claude-sonnet-4-6" ... not listed in project model registry ...   # exit 2

Frequently asked questions

What does the Writing Relayflows AI skill do?

Use when authoring a Relayflows flow (@relayflows/surface / @relayflows/sdk, the journal-based v2 engine — the CLI is `flows`, package versions 2.0.x) in TypeScript or YAML/JSON. Covers the three-rung ladder (run/llm/agent), the resident verbs (human/dispatch/done), verification gates, TypeScript vs YAML authoring, per-step cli/model selection and its resolution order, flows.json, and `flows check`/`run`/`resume` with their real refusal shapes and exit codes. Not for the older, unrelated `@relayflows/core` WorkflowBuilder engine (`.pattern('dag')`/.agent()/.step() chains) that `writing-agen...

Why use Writing Relayflows on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/AgentWorkforce/relay/tree/main/.agents/skills/writing-relayflows. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Writing Relayflows?

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 Writing Relayflows?

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

Is the Writing Relayflows AI skill free?

Yes. It is published on GitHub by AgentWorkforce 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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