Agent Action Handshake logo

Agent Action Handshake

Organization
friday-platform
agent-action-handshake

Canonical reference for the contract between an FSM action and the agent it invokes. Loads when authoring or debugging any combination of: `type: llm` inline action, `type: agent` action with `outputTo`, `type: agent` action with `inputFrom`, agent prompts that need to coordinate with the action invoking them. Covers what the runtime auto-injects per invocation kind (platform tools, `complete` when `outputTo` is set), `outputTo` semantics, `inputFrom` resolution, the four invocation kinds.

Overview

Publisherfriday-platform
Repositoryfriday-studio
Skill nameagent-action-handshake
Stars
102
Forks
5
Bundled files
Instructions only
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 friday-platform on GitHub. Read the source before you install it.

Installation

Install the Agent Action Handshake 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/friday-platform/friday-studio/tree/main/packages/system/skills/agent-action-handshake
  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/friday-platform/friday-studio.git /tmp/friday-studio
mkdir -p .claude/skills
cp -r /tmp/friday-studio/packages/system/skills/agent-action-handshake .claude/skills/agent-action-handshake
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Agent Action Handshake 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 Agent Action Handshake 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 Agent Action Handshake 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.

Agent ↔ FSM-action handshake

This is the contract between an FSM action and the agent it invokes. Most "platform bug" misdiagnoses live in the gap between "how the FSM authors the action" and "what the agent prompt expects." Read this once, then ground every job/agent edit in it.

The four invocation kinds

FSM actionReferenced agentRuntime path
type: llm inlinenone — the action carries provider, model, prompt, tools directlyfsm-engine.ts case "llm"
type: agentagents.<id> block with type: llmfsm-engine.ts case "agent" → runAgent → from-llm.ts
type: agentagents.<id> with type: atlascase "agent" → runAgent → bundled-agent handler
type: agentagents.<id> with type: usercase "agent" → runAgent → ProcessAgentExecutor (NATS subprocess)

Each path has the same outputTo contract but different tool surfaces and prompt-composition rules.

What the runtime auto-injects

Regardless of invocation kind, the LLM-side agent (any agent that's type: llm) sees:

  • Platform tools — full PLATFORM_TOOL_NAMES set from packages/agent-sdk/src/platform-tools.ts (~35 entries spanning memory, artifacts, fs, shell/data, state, HITL, and flow-control like delegate). These inject on top of the agent's config.tools allowlist regardless of what's in tools:. See source for the exact set.
  • complete tool — injected when the action declares outputTo. The LLM MUST call this to surface output (see "The complete contract" below).
  • failStep tool — injected for inline type: llm FSM actions (see fsm-engine.ts case "llm"). The LLM calls this to deliberately fail the step with a structured reason. NOT injected on the type: agent → type: llm path that goes through from-llm.ts; an agent reachable through both shapes can't assume the tool is present.

What's NOT auto-injected:

  • MCP server tools — those come from agents.<id>.config.tools (workspace-scope MCP only) or from the inline type: llm action's tools: array.

The complete contract

The single most important rule: if an action has outputTo, the agent's prompt MUST instruct the LLM to call complete with the structured output.

When the action is:

yaml
- type: agent
  agentId: stale-report-agent
  outputTo: response

The runtime injects a complete tool with input schema {response: string} (or richer if outputType is declared). The agent's prompt should end with:

When done, call the `complete` tool with:
  { "response": "<the full final text>" }
This is how the FSM captures your output. Do not stream the text as
your final assistant message — the runtime reads it from `complete`.

Without this instruction, the LLM streams prose, never calls complete, and the runtime throws:

LLM action with outputTo 'response' did not call complete

outputType and structured output

When you set outputType: SomeDoc, the runtime looks up fsm.documentTypes.SomeDoc for a JSON Schema and feeds it to the complete tool's input schema. The LLM must produce data matching the schema.

yaml
documentTypes:
  EmailDraft:
    type: object
    properties:
      to: { type: string }
      subject: { type: string }
      body: { type: string }
    required: [to, subject, body]

states:
  drafting:
    entry:
      - type: agent
        agentId: drafter
        outputTo: draft
        outputType: EmailDraft

The agent's prompt should know about to, subject, body — either from the prompt body or from a worked example.

inputFrom and document chaining

inputFrom reads a prior step's outputTo document and feeds it into the next step's input. String form chains one prior; array form concatenates multiple labeled.

yaml
states:
  draft:
    entry:
      - type: agent
        agentId: drafter
        outputTo: draft-doc
      - type: emit
        event: DONE
    on: { DONE: { target: review } }
  review:
    entry:
      - type: agent
        agentId: reviewer
        inputFrom: draft-doc       # reads from draft-doc

Bulky inputs are passed as artifactRefs plus a compact summary — the agent reads the full data via parse_artifact(<id>) if needed.

Pattern A vs Pattern B

Two job shapes:

  • Pattern A (execution.strategy: sequential): list of agent IDs that run in order. Final agent's assistant text becomes the session output natively. Use when the work is a single LLM conversation, no document chaining needed, output is plain text.
  • Pattern B (fsm:): state machine with named states, entry actions, and explicit outputTo documents. Required when you need branching, multi-step document flow, parallel actions, or HITL elicitation.

Don't migrate Pattern A → Pattern B "to get outputTo." The migration introduces the complete injection contract, which is the single most common source of empty-output failures. If a Pattern A job is producing empty output, fix the agent's prompt first.

Cross-references

  • @friday/writing-workspace-jobs — FSM authoring shapes.
  • @friday/workspace-api — agent CRUD.
  • @friday/delegate-handoff — sibling contract for the parent ↔ delegate-child boundary.
  • @friday/debugging-empty-output — what happens when this contract is violated.
  • @friday/debugging-runtime-errors — did not call complete and related errors.

Frequently asked questions

What does the Agent Action Handshake AI skill do?

Canonical reference for the contract between an FSM action and the agent it invokes. Loads when authoring or debugging any combination of: `type: llm` inline action, `type: agent` action with `outputTo`, `type: agent` action with `inputFrom`, agent prompts that need to coordinate with the action invoking them. Covers what the runtime auto-injects per invocation kind (platform tools, `complete` when `outputTo` is set), `outputTo` semantics, `inputFrom` resolution, the four invocation kinds.

Why use Agent Action Handshake on TypingMind?

Because you install it once and use it with any model. Agent Action Handshake 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 Agent Action Handshake in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/friday-platform/friday-studio/tree/main/packages/system/skills/agent-action-handshake. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Agent Action Handshake?

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 Agent Action Handshake?

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

Is the Agent Action Handshake AI skill free?

It is published on GitHub by friday-platform. Check the repository for licensing terms. 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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