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Simulator Smart Forms Runtime

Organization
corezoid
simulator-smart-forms-runtime

Simulator.Company Smart Form RUNTIME driver — run/execute an existing Smart Form (CDU / Script / mini-app) on the user's behalf, conversationally, instead of the user clicking through its UI. Use when the user wants to GO THROUGH a smart-form flow: create/submit something an app produces (e.g. a smart contract), fill in a form, walk a multi-step wizard, or feed a document into an app. This is the execution counterpart to the `simulator-smart-forms` author skill (which BUILDS forms). Activate on: "run the app", "use the smart form", "fill the form", "create a smart contract", "submit", "go through the flow", "complete the wizard", "запусти застосунок", "пройди смартформу", "заповни форму", "створи смарт контракт", "оформи договір", "запусти приложение", "заполни форму", "создай смарт контракт".

Overview

Publishercorezoid
Repositorysimulator-ai-plugin
Skill namesimulator-smart-forms-runtime
Stars
61
Forks
13
Bundled files
Instructions only
LicenseMIT
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 corezoid on GitHub. Read the source before you install it.

Installation

Install the Simulator Smart Forms Runtime 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/corezoid/simulator-ai-plugin/tree/main/plugins/simulator/skills/simulator-smart-forms-runtime
  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/corezoid/simulator-ai-plugin.git /tmp/simulator-ai-plugin
mkdir -p .claude/skills
cp -r /tmp/simulator-ai-plugin/plugins/simulator/skills/simulator-smart-forms-runtime .claude/skills/simulator-smart-forms-runtime
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Simulator Smart Forms Runtime 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 Simulator Smart Forms Runtime 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 Simulator Smart Forms Runtime 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.

Simulator.Company Smart Form Runtime

You drive an existing Smart Form to completion on the user's behalf — gathering inputs from the conversation (and any attached document), filling each page's forms, and walking the flow step by step. You do not build or edit Smart Forms — that is the simulator-smart-forms author skill.

A Smart Form is a server-driven UI state machine. Corezoid (its backend process) supplies the data, validation, control flow, and side effects (creating actors, running transactions). Your job is the part Corezoid and the GUI can't do: turn an unstructured request into the right field values, ask only for what's genuinely missing, confirm before irreversible steps, and report the result.

Read these references when driving:

  • $CLAUDE_PLUGIN_ROOT/docs/user-flows/cdu-page-protocol.md — the get/send protocol, the Page → Grid → Form → Section → Item model, the component catalogue (§5), and the change protocol (§7). This is the contract you speak.
  • $CLAUDE_PLUGIN_ROOT/docs/user-flows/app-catalog.md — how to discover which app to run (match intent against each Smart Form's native title + description; tags / linked actors for scope). No custom catalog format.

Tools you use

StepTool(s)
Discover the appfilterActors (over the scripts system form) → match intent against each actor's title + description (tags / getRelatedActors to scope); or getActorByRef when the user names it
Render a pageappGetPage (accId, ref, envTitle, page, query)
Submit a formappSendForm (accId, ref, envTitle, page, formId, buttonId, data, query)
Read an attached documentreadAttachment
Report a resultbuildLink (deep-link to a created actor/record)

These two — appGetPage / appSendForm — are universal: the same two tools drive every Smart Form. Nothing about the app is hard-coded; you interpret what the page returns.


The drive loop

1. Discover → choose app → (accId, ref, env, entryPage)        # see app-catalog.md
2. page = appGetPage(accId, ref, env, entryPage)
3. loop until the flow ends:
     a. Read page.forms[].sections[].content[] → the items to fill (see §4–5 of the protocol).
        Read page.notifications[] → messages from the process.
     b. Fill a data{} map: itemId → value, from the conversation / extracted document.
        Only value-bearing, visible items (edit, select, multiselect, radio, check, toggle,
        slider, phone, otp, date, upload, table, …). Respect `required`; honour `options`.
     c. Ask the user ONLY for fields that are missing or ambiguous — never re-ask for what
        you already know. Use the item `title`/locale to know what each field means.
     d. CONFIRMATION GATE — if this submit has side effects (creates actors, moves money,
        sends something), show the user a plain-language summary and get an explicit "yes"
        BEFORE sending. Reads/navigation need no gate.
     e. resp = appSendForm(accId, ref, env, page.id, formId, buttonId, data)
     f. Handle resp by code (protocol §2.3):
          200 → apply the `changes[]` to your view of the page (§7), read notifications,
                continue on the same page (more fields, or a result).
          205 → re-render: page = appGetPage(... resp.pageId ...) — a fresh (maybe different) page.
          302 → navigate to resp.nextPage (internal page) or report resp.nextPage (external URL).
                CARRY resp.query FORWARD: page = appGetPage(..., resp.nextPage, query=resp.query)
     g. Self-correct: if notifications contain a validation error, read helperText, fix the
        offending field (re-ask the user if needed), and re-submit the SAME formId.
4. Report: summarise success notifications and give a buildLink to what was created.

Carry the query across navigation — otherwise the session is lost

A 302 answers {nextPage, query}, and that query is how a Smart Form carries per-session state: the page protocol is stateless, hidden carrier fields survive a submit but not a navigation, so a login handler typically returns the session in query and every later page reads it as body.query.*.

So: pass resp.query straight into the next appGetPage(query=…), and pass the query you rendered a page with into appSendForm(query=…) when you submit on it. Drop it and the next page renders as if the user had arrived cold — you will see empty fields, "—" placeholders, or an empty table, and it looks like a backend bug rather than a missing argument.

resp = appSendForm(..., page="index", buttonId="login_btn", data={…})
# → {code: 302, data: {nextPage: "home", query: {token: "…", cardCode: "…"}}}

page = appGetPage(..., page="home", query={"token": "…", "cardCode": "…"})   # ✅ logged in
page = appGetPage(..., page="home")                                          # ❌ renders cold

Both tools put the object in the URL query string (?token=…&cardCode=…, exactly as the renderer does) — including appSendForm, whose POST handler reads the query off the URL and forwards it to the process as body.query. You pass the same shape either way.

A session token in the query is visible in the page URL. That is the platform's own documented pattern and fine for a token, but it is the reason a password must never be put there — see app-generation.md §4.1a.

Detecting the end of a flow: the flow is done when a submit yields a terminal signal — a success notification with no further form to fill, a 302 redirect to a result/landing page, or a page with no submittable form. Don't loop forever; if a page repeats unchanged after a submit, stop and report what the process said.


Filling forms — reading the components

Each item carries id (the key you submit under), class (the component), value, visibility, required, and options. Map your data to the item id. Key components (full catalogue in protocol §5):

  • edit (text/email/int/float/phone/multiline/date/password) — a scalar value.
  • select / radio — one value from options[] (match by option value/label).
  • multiselect — an array of option values.
  • check / toggle — boolean.
  • upload / attachment / signature / file — file values.
  • button — carries the buttonId you submit with (and may carry buttonData).
  • label / divider / image / table (display) — read for context; usually not submitted.

If a field's meaning is unclear from its title/locale, ask the user rather than guessing.


Entry modes

  • Direct intent — "create a smart contract with Acme for $50k": discover the app, prefill what the user gave, ask for the rest, run the loop.
  • Named app — "open the onboarding app": resolve the ref directly and run.
  • From a document — "make smart contracts from this file": read the attachment, extract the structured records, then run the loop per record. (Detailed below.)

Document → many records (batch)

The high-value case: the user drops a document and says "create smart contracts from this". The document is unstructured; the app's forms are structured. You bridge them.

1. Read the document:
     getActorAttachments(<reaction or actor id>)  → fileName(s)
     readAttachment(fileName)                      → the document content
2. Extract structured records (this is YOUR job, not the platform's):
     parse the document into N records, each a {field: value} map aligned to the app's inputs.
     One document may describe several contracts → segment into N records. Keep a per-record
     note of what you could NOT find (missing/uncertain fields).
3. Discover the app ONCE (title/description match), then for EACH record run the drive loop
     above, prefilling the record's extracted values.
4. Gap-fill in BATCH: collect the missing/ambiguous fields across all records and ask the user
     once (e.g. "currency is missing for Acme and Globex — which?"), not record-by-record.
5. BATCH CONFIRMATION GATE: show a compact summary of all N records (the key fields you will
     submit) and get one explicit "yes" before sending the series. This is mandatory — each
     submit creates real actors / transactions.
6. Run the per-record submits. On a per-record error: read its notifications, fix if you can,
     otherwise SKIP that record and continue the rest — never abort the whole batch silently.
7. Aggregated report: "created N (links: …); M skipped (reason …)". Give a buildLink per
     created record.

Rules for extraction:

  • Show, don't assume. Surface the extracted values in the confirmation summary so the user can catch a misread before anything is created.
  • Never fabricate a required field. If you can't find it and can't infer it safely, ask.
  • Respect the form's options. Map extracted values onto the page's select/radio options (e.g. a currency string → the matching option), don't invent option values.

In the reaction → AI-agent context

When triggered from a reaction, you carry the reaction author's identity, so app access (sharedWith / appSettings.groups) self-scopes — no extra auth wiring. Conduct the dialog as reactions: ask clarifying questions and the confirmation gate as ai replies; the user's follow-up reaction continues the same flow. Finish with a clear result + buildLink.


Key rules

  • You run apps; you don't edit them. For building/editing pages, switch to simulator-smart-forms. To generate a whole new app from a set of existing Corezoid processes, switch to simulator-app-generator.
  • Never skip the confirmation gate before a side-effecting send (created actors, money). Show a summary; get an explicit yes.
  • Prefer production unless the user is testing develop.
  • Ask for the minimum. Prefill from context/document; only prompt for missing or ambiguous fields.
  • Trust the protocol, not assumptions. The forms a page returns ARE the source of truth for what to fill; follow code/nextPage rather than guessing the flow.
  • Surface what the process says. Relay notifications (success and error) to the user; self-correct on validation errors instead of silently retrying.
  • Respond in the user's language; keep these instructions' logic intact.

Frequently asked questions

What does the Simulator Smart Forms Runtime AI skill do?

Simulator.Company Smart Form RUNTIME driver — run/execute an existing Smart Form (CDU / Script / mini-app) on the user's behalf, conversationally, instead of the user clicking through its UI. Use when the user wants to GO THROUGH a smart-form flow: create/submit something an app produces (e.g. a smart contract), fill in a form, walk a multi-step wizard, or feed a document into an app. This is the execution counterpart to the `simulator-smart-forms` author skill (which BUILDS forms). Activate on: "run the app", "use the smart form", "fill the form", "create a smart contract", "submit", "go t...

Why use Simulator Smart Forms Runtime on TypingMind?

Because you install it once and use it with any model. Simulator Smart Forms Runtime 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 Simulator Smart Forms Runtime in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/corezoid/simulator-ai-plugin/tree/main/plugins/simulator/skills/simulator-smart-forms-runtime. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Simulator Smart Forms Runtime?

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 Simulator Smart Forms Runtime?

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

Is the Simulator Smart Forms Runtime AI skill free?

Yes. It is published on GitHub by corezoid under the MIT 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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