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Observal Registry

OrganizationPopular
Observal
observal-registry

Searches, recommends, bulk-submits, installs, edits, versions, archives, restores, transfers, and manages co-authors for Observal MCP servers, skills, hooks, prompts, and sandboxes. Use when the user wants to find components, publish one or many they control, install them into a harness, or manage their lifecycle.

Overview

PublisherObserval
RepositoryObserval
Skill nameobserval-registry
Stars
2.4K
Forks
472
Bundled files
3
LicenseApache-2.0
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.

  • 3 bundled files

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

  • Open source

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

Installation

Install the Observal Registry 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.
  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/Observal/Observal.git /tmp/Observal
mkdir -p .claude/skills
cp -r /tmp/Observal/observal_cli/skills/observal-registry .claude/skills/observal-registry
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Observal Registry 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 Observal Registry 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 Observal Registry 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.

Managing Registry Components

Execution contract

  1. Execute commands with a 60 second timeout.
  2. Use machine output by default: add --output json whenever supported. Parse list results from items and pagination fields.
  3. Run the leaf command's --help when any path, flag, enum, or payload shape is uncertain.
  4. Supply all required inputs and confirmation flags. Do not leave an agent waiting at a prompt.
  5. Reuse returned UUIDs and qualified_name values. Never automate with row numbers or ambiguous bare names.
  6. Verify installs, submissions, edits, versions, ownership changes, and lifecycle transitions.
  7. Submit or modify only components the user owns or is authorized to manage.
  8. Never expose environment values, headers, tokens, private source data, or submitted secret fields.
  9. Mutations are sent once. After an uncertain transport failure, read component state before retrying.
  10. Authentication is optional for approved public content when the server setting deployment.public_registry_enabled is enabled; it is disabled by default on self-hosted deployments. Public list, show, install, and prompt render commands use https://public.observal.io by default. Authenticate before submitting, editing, reviewing, rating, or accessing private team content.

Choose the workflow

User intentRead
Find, inspect, recommend, or install componentsDiscovery and installation
Submit one component or a mixed bulk fileComponent submission
Edit, version, archive, restore, transfer, or manage co-authorsRegistry lifecycle

Read only the selected reference, and read it completely before executing.

Registry rules

  • Search with the user's natural-language terms, then narrow by type, namespace, team, harness, or category only when useful.
  • Open-ended requests such as "what am I missing?" use personalized recommendations before keyword search.
  • personalized: false means popularity fallback, not a personal recommendation.
  • Team members can see authorized private teamspace items. Use TEAM_HANDLE/ITEM_SLUG for direct references.
  • Draft, pending, rejected, and approved items have different edit behavior. Read status before mutating.
  • A successful submit can still be pending review. Report the returned status instead of saying it is published.
  • Bulk files are structurally validated before mutation. Inspect every per-entry result and verify uncertain retries by canonical identity.
  • Prefer an existing installed dependency or native CLI path. Do not invent wrappers or telemetry variables.

Completion

Report component type, canonical identity, version, status, target harness or scope when applicable, warnings, and any review or setup step still required.

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 Observal Registry AI skill do?

Searches, recommends, bulk-submits, installs, edits, versions, archives, restores, transfers, and manages co-authors for Observal MCP servers, skills, hooks, prompts, and sandboxes. Use when the user wants to find components, publish one or many they control, install them into a harness, or manage their lifecycle.

Why use Observal Registry on TypingMind?

Because you install it once and use it with any model. Observal Registry 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 Observal Registry in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Observal/Observal/tree/main/observal_cli/skills/observal-registry. 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 Observal Registry?

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 Observal Registry?

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

Is the Observal Registry AI skill free?

Yes. It is published on GitHub by Observal under the Apache-2.0 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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