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Using Elicitations

Organization
friday-platform
using-elicitations

Asks the user mid-run from inside an FSM action or Python `type: user` agent via the elicitation tools — `request_tool_access` for allowlist approvals and `request_human_input` for HITL (human-in-the-loop) decisions, approvals, confirmations, and disambiguation. Use when a job needs the user to choose, approve, or supply input before the action can continue. Covers simple multiple choice, nested per-item choice, prompt formatting, and how to consume the answer.

Overview

Publisherfriday-platform
Repositoryfriday-studio
Skill nameusing-elicitations
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 Using Elicitations 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/using-elicitations
  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/using-elicitations .claude/skills/using-elicitations
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Using Elicitations 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 Using Elicitations 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 Using Elicitations 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.

Using elicitations

Elicitations pause a running FSM action (or Python type: user agent), surface a prompt to the user in the Activity feed, and resume the same tool call with the user's answer. Two tools, one mechanism.

  • request_tool_access(toolName, reason) — ask for permission to call a tool not in the action's allowlist. Fixed options: Allow once, Allow always, Deny.
  • request_human_input(question, options?) — ask the user a question. Flat options for simple choice, free-form text when options omitted, or grouped per-item options for nested choice.

Both tools block until the user answers, declines, or the elicitation expires. The terminal answer comes back as the tool's return value, and the action continues from there in the same run.

Choosing between them

NeedTool
Permission to call a tool you don't haverequest_tool_access
A user decision between alternativesrequest_human_input (with options)
Free-form user textrequest_human_input (no options)
Approval before a destructive side effectrequest_human_input with [Confirm, Cancel]
One choice for each of several itemsrequest_human_input with grouped options

Never substitute a prose menu for request_human_input. An outputTo action that prints choices and waits for a future user message will fail — the action only resumes through the elicitation answer envelope.

Formatting the question

The question field renders as plain text with whitespace preserved. The UI does not parse markdown.

  • No **bold**, no # headings, no - bullets, no fenced code. They render literally.
  • Lead with a short top-level title and a one-line description of what the user is choosing.
  • Keep the question itself terse — the action's verbose context belongs in the action's prose output, not the prompt body.
  • Newlines are preserved; use them to separate sections, never markdown.

Bad — markdown that renders as raw characters:

**Pick an action:**
- Archive
- Keep

Good — plain text, short intro, options carry the choice copy:

Pick an action for this receipt.

Simple multiple choice

One question, one answer. Flat options array, each { label, value }. label is shown to the user; value is what the agent receives back.

json
{
  "question": "Send the invoice now?",
  "options": [
    { "label": "Send now", "value": "send" },
    { "label": "Save as draft", "value": "draft" },
    { "label": "Cancel", "value": "cancel" }
  ]
}

The answer comes back as { status: "answered", answer: "send" }. Branch on answer and continue. On status: "declined" or "expired", stop safely and explain what was blocked.

Do not pass multi_select, default, required, or any other fields — the schema rejects them. For multiple independent selections, use grouped nested choice below.

Nested multiple choice — one decision per item

When the user must pick one action for each of several items (the inbox-triage shape), encode the item index in both label and value. The UI groups the options into one choice set per item and returns the selected values as a JSON-array answer string.

Question body — short intro, then numbered items with detail lines:

Review your inbox — select an action for each email.

[1] Subject: Your receipt
    From: billing@example.com
    Date: May 7, 2026
    Preview: Monthly subscription paid

[2] Subject: Meetup reminder
    From: events@example.com
    Date: May 7, 2026
    Preview: Quarterly community meetup tomorrow

Detail line keys the parser recognises: Subject:, From:, Date:, Preview:. Use them — the UI lifts those values into the per-item header. Anything else under a [N] block is shown as supporting text.

Options — one entry per (item, action). label is [N] ActionLabel — Title. value is N:action:

json
{
  "label": "[1] Archive — Receipt",
  "value": "1:archive"
}

A full grouped call:

json
{
  "question": "Review your inbox — select an action for each email.\n\n[1] Subject: ...\n[2] Subject: ...",
  "options": [
    { "label": "[1] Archive — Receipt", "value": "1:archive" },
    { "label": "[1] Keep — Receipt", "value": "1:keep" },
    { "label": "[1] Delete — Receipt", "value": "1:delete" },
    { "label": "[2] Archive — Meetup reminder", "value": "2:archive" },
    { "label": "[2] Keep — Meetup reminder", "value": "2:keep" },
    { "label": "[2] Delete — Meetup reminder", "value": "2:delete" }
  ]
}

Requirements for the grouping to fire:

  • At least two items ([1], [2]).
  • At least two actions per item.
  • Every option value is N:action where N matches an item index in the question.
  • Every option label starts with [N] matching the same index.

If any constraint fails, the UI falls back to a flat radio list of all options — usable, but the per-item structure is lost.

The answer comes back in answer.value as a JSON array string:

"[\"1:archive\",\"2:keep\"]"

Parse it before acting:

ts
const picks: string[] = JSON.parse(answer.value);
for (const pick of picks) {
  const [idx, action] = pick.split(":");
  // dispatch (idx, action)
}

Optional per-item comments arrive in answer.note as [N] free text. Treat as advisory context, not as a parallel command stream.

When per-item input is complex (free-form text, multiple fields), ask one item at a time instead of flattening. The grouped contract is for one choice per item, nothing more.

Handling tool-access requests

request_tool_access(toolName, reason) returns one of:

reasongrantedWhat it means
bypasstrueWorkspace/daemon has dangerouslySkipAllowlist; proceed silently
persistent_allowtrueThe user previously chose Allow always for this tool
answeredtrue/falseUser picked Allow once / Allow always / Deny
pending_user_approvalfalseElicitation created but not yet answered (rare — usually you wait)
declinedfalseUser declined
expiredfalseElicitation TTL elapsed
unknown_toolfalsetoolName not in the runtime catalog — do not retry

Rules:

  • Only request access to tools you know exist. If you don't know the name, call list_capabilities or list_mcp_tools first. An unknown_tool response means stop guessing.
  • The reason field is shown to the user verbatim. Make it specific to the moment ("Need fs_write_file to save the patch we just drafted"), not generic ("Need write access").
  • On granted: true, continue in the same action and finish normally.
  • On granted: false, do not retry the underlying tool. Acknowledge what was blocked and either route around it or stop safely.

Authoring rule: only declare request_tool_access on actions that can produce a useful denial outcome (partial result, clear explanation). Don't list it as a generic escape hatch.

Handling human-input answers

request_human_input blocks until terminal, then returns:

ts
{
  ok: boolean,
  status: "answered" | "declined" | "expired",
  elicitationId: string,
  answer?: string,
  note?: string,
}
  • status: "answered" → branch on answer. For grouped prompts, JSON.parse(answer) first.
  • status: "declined" or "expired" → halt the side effect, summarise what was skipped, and finish the action without faking the answer.

Python type: user agents call the same primitive through ctx.tools.call("request_human_input", {"question": ..., "options": [...]}) with the same return shape.

Lifecycle and timeouts

  • Elicitation TTL inherits the parent job's config.timeout (override per-job with jobs.<name>.elicitations.timeout). Default is 30 minutes.
  • The same (question, options, sessionId, actionId) re-uses any still-pending elicitation rather than creating a duplicate. Accidental retries are idempotent.
  • Expired prompts move to a read-only Activity log entry; the user cannot answer them retroactively, and acting on one does not reify the timed-out job.

Gotchas

  • The question field renders plain — markdown becomes literal characters. Use newlines and short prose, not formatting tokens.
  • options is flat. There is no multi_select, no default, no nested option objects. Encode item grouping in label/value strings.
  • Grouped options need at least two items and at least two actions per item or the UI falls back to a flat list.
  • request_tool_access is for permission, not for asking the user what tool to use. If the underlying problem is "which provider", that is a request_human_input question with options like [Gmail, Outlook], not a tool-access request.
  • An outputTo LLM action must complete in one execution. Without request_human_input, the action cannot wait for a user reply — the run will end without the answer.
  • Don't call request_human_input with no options when you actually expect a fixed choice. Either provide options (so the UI renders buttons) or expect free-form text — pick one.
  • Per-item comments in answer.note are advisory. Do not parse them for primary commands — those go in answer.value.

Frequently asked questions

What does the Using Elicitations AI skill do?

Asks the user mid-run from inside an FSM action or Python `type: user` agent via the elicitation tools — `request_tool_access` for allowlist approvals and `request_human_input` for HITL (human-in-the-loop) decisions, approvals, confirmations, and disambiguation. Use when a job needs the user to choose, approve, or supply input before the action can continue. Covers simple multiple choice, nested per-item choice, prompt formatting, and how to consume the answer.

Why use Using Elicitations on TypingMind?

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

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

Which AI models can use Using Elicitations?

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 Using Elicitations?

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

Is the Using Elicitations 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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