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Zernio Inbox Integration

Community
Enriquemarq1
zernio-inbox-integration

Standalone guide to integrating Zernio messaging and inbox capabilities into any existing application, including channel connections, conversations, history, sending, replies and messaging events. Use for documentation-led integrations and guided implementation prompts; no white-label blueprint dependency.

Overview

PublisherEnriquemarq1
Repositoryzernio-library-skills
Skill namezernio-inbox-integration
Stars
50
Forks
13
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 Enriquemarq1 on GitHub. Read the source before you install it.

Installation

Install the Zernio Inbox Integration 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/Enriquemarq1/zernio-library-skills/tree/main/.claude/skills/zernio-inbox-integration
  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/Enriquemarq1/zernio-library-skills.git /tmp/zernio-library-skills
mkdir -p .claude/skills
cp -r /tmp/zernio-library-skills/.claude/skills/zernio-inbox-integration .claude/skills/zernio-inbox-integration
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Zernio Inbox Integration 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 Zernio Inbox Integration 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 Zernio Inbox Integration 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.

Zernio inbox integration

This is a standalone integration skill, not a stage of the white-label blueprint. It works with existing apps regardless of how they were built. Its instructions and official-documentation links are self-contained; the library relationship below is optional compatibility context.

Use Map → Connect → Integrate → Verify to add Zernio's messaging capabilities to the user's own application. The app already exists by default. This is an implementation framework, not a software package or a guarantee of instant channel activation. Follow the agent brief and prompt sequence.

Start from official documentation, not Enrique's legacy Zernio skills. Adapt to the application's existing framework, authentication, data model and design. Do not scaffold a new app or replace its stack unless requested. If no repository is available, produce an integration plan and ask for the target app before making architectural commitments.

Use the API contracts and integration pitfalls while implementing; it records the documentation audit and points to the exact sources. Use the Claude Code testing handoff for a realistic end-to-end evaluation. Neither reference substitutes for live verification.

Guide the user through the process: identify the next action, implement everything possible autonomously, and ask only for account-owner actions the agent cannot perform, such as completing consent. Explain where to click and how readiness will be checked. Keep an integration progress record in the target project with stage, completed evidence, blocked prerequisites and next action. Never label a stage complete just because its code exists.

Relationship to Enrique's library

The white-label-blueprint already covers creating a branded social app: account connection, client workspaces, publishing and account-health webhooks. Its stage 7 already sketches an inbox tab with history, replies, read state and comments. This skill develops that messaging integration in depth and can also operate on any existing app; the blueprint is not a required dependency. Reuse existing connections, profile mappings, routes and components instead of rebuilding them. Verify current API contracts and authorization requirements independently; the starter's unlisted client links and UI profile filters are not production access control. See library relationship when integrating into a blueprint-derived app.

Decisions that matter

  • Identify the target repository, selected channels, intended users and live versus fixture mode. Infer established answers from context rather than repeating discovery.
  • Map the requested messaging capabilities against current documentation and channel support. The baseline is connection/status, conversation list, history, sending and replies. Where requested and supported, include attachments, templates, read/delivery events, conversation status, reactions, typing indicators and other messaging operations. Record channel differences and unsupported combinations; a shared API does not make every feature universal.
  • Implement a small working slice first, then extend to the selected capability set. Do not silently restrict this reusable skill to three video-demo channels, or automatically implement every endpoint. Publishing, analytics, autonomous agents and voice calls require their own scope decision.
  • Read the current endpoint definitions and platform rules before writing requests. Record the documentation checked and unresolved requirements. Do not invent SDK methods or API fields from this skill.
  • Distinguish starting a conversation from replying, initial history import from live events, and accepted sends from delivery/read confirmation. These are separate contracts; test each requested behavior.
  • Keep Zernio credentials on the server. Authenticate app users and enforce ownership of profiles, accounts and conversations on every server route. A profile filter or browser-provided account ID is not authorization. If the app has no user login, keep it on localhost, say it cannot be deployed, and treat tenant ID checks as defence in depth.
  • Before any live test, check what else shares the accounts, using read-only calls:
    • other active webhook subscriptions on message.* (for example auto-reply agents);
    • the same social account connected under several tenants;
    • selected accounts that the conversation list skips;
    • test channels that exist only on profiles outside the app's tenants. Report each as a setup decision. Never remap another profile's account, or change another integration's subscription, to make a test work.
  • One inbox means multiple channel threads in one list. Do not silently merge identities across platforms or promise that one HTTP request sends to every channel.
  • Account approvals, number registration, permissions and channel messaging windows remain relevant. Show setup-pending states instead of claiming Zernio removes all requirements.
  • Begin with human-controlled replies. Adding an autonomous reply agent is a separate scope choice.

Completion

Verify the selected messaging capabilities, isolation between users if applicable, failure states and duplicate-send handling. Clearly distinguish fixture checks, live verified channels and channels still pending setup. A mock UI is not a verified integration. Deliver integration instructions, environment variable names without secrets, code locations, a capability coverage report and remaining requirements. Include a recording sequence when the task is a video demo.

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 Zernio Inbox Integration AI skill do?

Standalone guide to integrating Zernio messaging and inbox capabilities into any existing application, including channel connections, conversations, history, sending, replies and messaging events. Use for documentation-led integrations and guided implementation prompts; no white-label blueprint dependency.

Why use Zernio Inbox Integration on TypingMind?

Because you install it once and use it with any model. Zernio Inbox Integration 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 Zernio Inbox Integration in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Enriquemarq1/zernio-library-skills/tree/main/.claude/skills/zernio-inbox-integration. 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 Zernio Inbox Integration?

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 Zernio Inbox Integration?

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

Is the Zernio Inbox Integration AI skill free?

Yes. It is published on GitHub by Enriquemarq1 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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