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Corezoid Connector Create

Organization
corezoid
corezoid-connector-create

API connector builder. Use when the user wants a Corezoid process that calls ONE endpoint of ANY HTTP API — external (Stripe, OpenWeather, Google, banks, CRMs), internal (Simulator.Company, own services) or Corezoid itself — built from a text description, a documentation link, an OpenAPI/Swagger file, or a list of endpoints produced while reasoning about a task. Produces an atomic connector: input contract, validation when needed, a single API Call, Reply to Process on every outcome, unified response format, test, and Smart API registration. Activate on: "create a connector", "API connector", "integrate with <API>", "connect to <API>", "wrap this endpoint", "connector from this openapi.yaml", "сделай коннектор", "коннектор к API", "интеграция с", "подключи API", "обёртка над API", "зроби конектор", "конектор до API", "інтеграція з", "підключи API". NOT for processes with business logic or several calls — use /corezoid-create.

Overview

Publishercorezoid
Repositorycorezoid-ai-plugin
Skill namecorezoid-connector-create
Stars
74
Forks
26
Bundled files
5
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.

  • 5 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by corezoid on GitHub. Read the source before you install it.

Installation

Install the Corezoid Connector Create 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/corezoid-ai-plugin/tree/main/plugins/corezoid/skills/corezoid-connector-create
  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/corezoid-ai-plugin.git /tmp/corezoid-ai-plugin
mkdir -p .claude/skills
cp -r /tmp/corezoid-ai-plugin/plugins/corezoid/skills/corezoid-connector-create .claude/skills/corezoid-connector-create
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Corezoid Connector Create 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 Corezoid Connector Create 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 Corezoid Connector Create 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.

Create an API Connector Process

You build connector processes: a Corezoid process that atomically calls one endpoint (method + path) of a Target API and always answers its caller.

A connector is called by other processes through api_rpc, so it MUST reply on every path. After a successful test it is registered as a Smart API Connector in Simulator.Company.

Routing — check first

The request is…Use
the user explicitly called /corezoid-api-connectorthat skill — do not intercept
a process with business logic, several calls, orchestration/corezoid-create — stop here
one endpoint of any HTTP API, Corezoid API included (or several endpoints → several connectors)this skill
an endpoint that needs files, git or a protocol the API Call node does not supportthis skill, the call node is git_call (read /corezoid-gitcall first)

Hard rules (every connector, no exceptions)

  1. One process = one endpoint. Several operations → several processes, one by one.
  2. Input contract in params. Every business parameter is declared with type, required flag and regex where the format is known. Output fields are declared too (flag output).
  3. No secrets, hosts or keys in task data or hardcoded. Host, API keys, tokens — stage variables only: {{env_var[@<name>]}} (create with cz-variables).
  4. Validation when the contract needs it (required fields, formats, ranges) — before the API Call, with its own Reply. A Code node is allowed for validation.
  5. Exactly one call node: api, or git_call only by the exception in Routing.
  6. debug_info: true on the API Call node — the connector reads __conveyor_api_debug__ (HTTP code, timings) to build its response.
  7. Reply to Process before EVERY final — success, validation error, API error, connection error, timeout. A path that reaches a final without api_rpc_reply hangs the caller.
  8. Unified response format — see references/reply-format.md. Result fields are named after the endpoint (customer, forecast, items + total), never a generic data / response.
  9. No Sum nodes, no metric sub-processes. Metrics are collected outside the connector (HTTP-worker logs → Elasticsearch).
  10. A connector is ready only after a successful positive test (Step 9).

Workflow

1 Intake → 2 Contract → 3 Duplicate check → 4 Create process, params, variables
→ 5 Build nodes → 6 Layout → 7 Lint → 8 Push → 9 Test → 10 Register → 11 Report

Do the steps in order. Do not skip Contract confirmation, Test or Register.


Step 1: Intake

Accept any of:

  • a text description of the API / endpoint;
  • a documentation link or an OpenAPI / Swagger URL — fetch and read it;
  • an OpenAPI / Swagger / Postman file — read it;
  • a list of connectors produced earlier in the conversation ("which connectors does this task need").

Output of the step — a numbered list of endpoints: METHOD path — purpose. Show it to the user. If there are several, confirm which to build and build them one at a time (Steps 2–11 per endpoint).

Also ask (if not obvious): target folder for the process, Corezoid stage (default: develop).

Step 2: Contract

Fill the contract table from references/contract-template.md for the chosen endpoint: endpoint, provider + base host, input, output, expected errors, auth, test data.

Rules:

  • Take everything you can from the documentation; ask the user only for what is missing.
  • Auth goes to variables, never to input params (e.g. stripe-api-key variable, header Authorization: Bearer {{env_var[@stripe-api-key]}}).
  • Decide "validation needed?": yes if there are required fields, formats (email, UUID, dates, enums) or ranges that the Target API would reject.
  • Show the contract to the user and get explicit confirmation before building.

Keep the confirmed contract — Steps 9 and 10 use it.

Step 3: Duplicate check

Search the exported .conv.json files of the project (run pull-folder if needed) for an api node with the same method and the same host + path (after resolving variables). If found — tell the user and offer: reuse it, update it (→ /corezoid-edit), or build a new one anyway.

Step 4: Create the process, params and variables

  1. Variables. For host, keys and tokens: list existing ones (cz-variables list-variables), create missing ones (create-variable; secrets as secret variables). Naming: <provider>-api-host, <provider>-api-key.
  2. Process. Call create-process with process_name = <Provider>: <METHOD> <path> (e.g. Stripe: GET /v1/customers/{id}) and the target folder_path. Check scheme.nodes of the created file — do not add a second Start.
  3. Description. 1–2 sentences, starting with a verb: what the endpoint does and what it returns (see Description Update Rule in corezoid/SKILL.md).
  4. Params. Declare input and output params in the full shape:
json
{"name": "customer_id", "type": "string", "descr": "Stripe customer id",
 "flags": ["required", "input"], "regex": "^cus_[A-Za-z0-9]+$",
 "regex_error_text": "customer_id must look like cus_..."}

Output params: same shape with "flags": ["output"], one per result field of the reply.

Step 5: Build the nodes

Use the skeleton in references/process-skeleton.md. Main path:

Start
→ [Validate input]            (Code node, only if validation is needed)
→ [Check validation]          (Condition: invalid → Reply validation error)
→ [Prepare request]           (Code / Set Parameters, only if the body/query needs building)
→ API Call                    (single api node, debug_info: true, time semaphore)
→ Reply ok                    (result = ok + named result fields + http_code)
→ Final

Error paths (each with its own Reply and a named Error final):

FailureHow it is caughtReply
input invalidCondition after validationerror_type: validation + invalid_params
Target API answered non-2xxerr_node_id → Condition on __conveyor_api_return_type_tag__ = api_bad_answer (also api_bad_answer_format)error_type: api
connection / DNS / TLSsame Condition, tag api_connection_errorerror_type: connection
Target API call itself timed out (hardware)same Condition, tag api_timeout — routes to the same Reply as the semaphore belowerror_type: timeout
no answer within the process-level waittime semaphore of the API Call (default 30 s)error_type: timeout
anything else from the API Callsame Condition, default brancherror_type: api

Node rules — follow the Core rules of /corezoid-create (Step 4 there), especially: 24-hex temporary node IDs; connect only via go; every err_node_id / semaphore target is obj_type: 3; never mix an action logic with go_if_const in one node; error clusters collapsed and named after the failure (Customer Not Found Error, not Error); a dedicated error cluster per failing node (validation Code node, prepare node, API Call).

API Call node — author the full canonical api logic (see docs/nodes/api-call-node.md, "Required node shape"); the reference shape is in references/process-skeleton.md. debug_info MUST be true.

Step 6: Layout

Leave coordinates at x: 0, y: 0 while building, then call layout-process.

Step 7: Lint

Call lint-process (with profile: "connector" once the plugin supports it). Fix every error and re-run until clean. In addition, check by hand until the profile exists:

  • exactly one api (or git_call) node;
  • debug_info: true on it;
  • every final is reachable only through an api_rpc_reply;
  • no hardcoded http:// / https:// host in the URL, no keys in params;
  • no api_sum nodes.

Step 8: Push

Call push-process. Then pull-process to get canonical node IDs (needed for nodeId in Step 10).

Step 9: Test

Follow references/test-rules.md. In short:

  1. Build test data from the contract (docs examples; ask the user for what is missing).
  2. Positive test via run-task with fields naming result, http_code and the output params — expect result = ok, the expected http_code, result fields matching the output params by name and type.
  3. Negative validation test (if validation exists) — invalid input → error_type = validation + invalid_params, no API call made.
  4. Unsafe methods (POST / PUT / PATCH / DELETE) — only after the user confirms, or against a sandbox host variable.
  5. On failure — show the node where the task stopped and the reply, propose a fix, re-test.
  6. Record the test result: conv_id, task ref, time, result.

No successful positive test → the connector is not ready. Do not register it.

Step 10: Register the Smart API Connector

Follow references/registration.md. In short:

  1. Find the receiver in the current workspace by names: project short_name "smart-api" → its stage "production" → alias "api-gw-create-smart-api" → the process it points to. A missing project/stage/alias is an expected outcome, not an error — the user may lack access, or the resource may have been renamed/removed; don't guess which. Stop here and finish: the connector is ready, Smart API was not registered (not found). Never block.
  2. run-task into the receiver process, exactly ONCE, with the task data in references/registration.md and mode: "full" — the task carries the whole connector contract, so by default its data would be left out of the summary. Task ref = conv_id of the connector.
  3. Report whatever comes back, verbatim, and stop there: a successful Reply → show the user the Smart API actor id; any error, rejection, timeout, or no answer → tell the user Smart API was not registered and why, quoting the receiver's own error. Never retry, guess a different payload and resend, or pull/inspect the receiver process or any of its sub-processes to "fix" a rejection — that is debugging someone else's production system, not this skill's job. One attempt, one honest report, done.

Step 11: Report

Tell the user, briefly: process name and link, contract summary (method, path, inputs, outputs), test result, Smart API id (or why it was not registered), variables created.

Then do the Final Step: Update Git Context exactly as in /corezoid-create.


References

PathWhen
references/contract-template.mdStep 2 — contract table and examples
references/process-skeleton.mdStep 5 — node skeleton, API Call shape, error routing
references/reply-format.mdSteps 5, 9 — response format, error mapping
references/test-rules.mdStep 9
references/registration.mdStep 10
${CLAUDE_PLUGIN_ROOT}/skills/corezoid-create/SKILL.mdCore rules for nodes and error clusters
${CLAUDE_PLUGIN_ROOT}/docs/nodes/api-call-node.mdAPI Call fields, error tags, semaphores
${CLAUDE_PLUGIN_ROOT}/docs/nodes/reply-to-process-node.mdReply formats, stringification
${CLAUDE_PLUGIN_ROOT}/docs/process/process-with-parameters.mdparams shape
${CLAUDE_PLUGIN_ROOT}/docs/variables-guide.mdVariables
${CLAUDE_PLUGIN_ROOT}/samples/api-post.jsonHTTP POST example (note: its debug_info: false and generic response reply do NOT meet the rules above)

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Corezoid Connector Create AI skill do?

API connector builder. Use when the user wants a Corezoid process that calls ONE endpoint of ANY HTTP API — external (Stripe, OpenWeather, Google, banks, CRMs), internal (Simulator.Company, own services) or Corezoid itself — built from a text description, a documentation link, an OpenAPI/Swagger file, or a list of endpoints produced while reasoning about a task. Produces an atomic connector: input contract, validation when needed, a single API Call, Reply to Process on every outcome, unified response format, test, and Smart API registration. Activate on: "create a connector", "API connector...

Why use Corezoid Connector Create on TypingMind?

Because you install it once and use it with any model. Corezoid Connector Create 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 Corezoid Connector Create in TypingMind?

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

Which AI models can use Corezoid Connector Create?

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 Corezoid Connector Create?

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

Is the Corezoid Connector Create 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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