Global Chat Agent Discovery

Overview

This public intake copy packages

plugins/antigravity-awesome-skills-claude/skills/global-chat-agent-discovery

https://github.com/sickn33/antigravity-awesome-skills

Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.

This intake keeps the copied upstream files intact and uses

metadata.json

ORIGIN.md

Global Chat Agent Discovery

Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: How It Works, Common Pitfalls, Limitations.

When to Use This Skill

Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.

• Use when you need to find an MCP server for a specific capability (e.g., database access, file conversion, API integration)
• Use when evaluating which agent registries carry tools for your use case
• Use when you want to search across multiple protocols (MCP, A2A, agents.txt) simultaneously
• Use when setting up agent-to-agent communication and need to discover available endpoints
• Use when the request clearly matches the imported source intent: Discover and search 18K+ MCP servers and AI agents across 6+ registries using Global Chat's cross-protocol directory and MCP server.
• Use when the operator should preserve upstream workflow detail instead of rewriting the process from scratch.

Operating Table

metadata.json

ORIGIN.md

SKILL.md

SKILL.md

## Related Skills

Workflow

This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.

1. Confirm the user goal, the scope of the imported workflow, and whether this skill is still the right router for the task.
2. Read the overview and provenance files before loading any copied upstream support files.
3. Load only the references, examples, prompts, or scripts that materially change the outcome for the current request.
4. Execute the upstream workflow while keeping provenance and source boundaries explicit in the working notes.
5. Validate the result against the upstream expectations and the evidence you can point to in the copied files.
6. Escalate or hand off to a related skill when the work moves out of this imported workflow's center of gravity.
7. Before merge or closure, record what was used, what changed, and what the reviewer still needs to verify.

Imported Workflow Notes

Imported: Overview

Global Chat is a cross-protocol AI agent discovery platform that aggregates MCP servers and AI agents from 6+ registries into a single searchable directory. This skill helps you find the right MCP server, A2A agent, or agents.txt endpoint for any task by searching across 18,000+ indexed entries. It also provides an MCP server (

@global-chat/mcp-server

Imported: How It Works

Option 1: Use the MCP Server (Recommended for Agents)

Install the Global Chat MCP server to search the directory programmatically from Claude Code, Cursor, or any MCP client.

npm install -g @global-chat/mcp-server

Add to your MCP client configuration:

{
  "mcpServers": {
    "global-chat": {
      "command": "npx",
      "args": ["-y", "@global-chat/mcp-server"]
    }
  }
}

Then ask your agent to search for tools:

Search Global Chat for MCP servers that handle PostgreSQL database queries.

Option 2: Use the Web Directory

Browse the full directory at https://global-chat.io:

1. Visit the search page and enter your query
2. Filter by protocol (MCP, A2A, agents.txt)
3. Filter by registry source
4. View server details, capabilities, and installation instructions

Option 3: Validate Your agents.txt

If you maintain an

agents.txt

1. Go to https://global-chat.io/validate
2. Enter your domain or paste your agents.txt content
3. Get instant feedback on format compliance and discoverability

Examples

Example 1: Ask for the upstream workflow directly

Use @global-chat-agent-discovery to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.

Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.

Example 2: Ask for a provenance-grounded review

Review @global-chat-agent-discovery against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.

Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.

Example 3: Narrow the copied support files before execution

Use @global-chat-agent-discovery for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.

Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.

Example 4: Build a reviewer packet

Review @global-chat-agent-discovery using the copied upstream files plus provenance, then summarize any gaps before merge.

Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.

Imported Usage Notes

Imported: Examples

Example 1: Find MCP Servers for a Task

You: "Find MCP servers that can convert PDF files to text"
Agent (via Global Chat MCP): Searching across 6 registries...
  - @anthropic/pdf-tools (mcpservers.org) — PDF parsing and text extraction
  - pdf-converter-mcp (mcp.so) — Convert PDF to text, markdown, or HTML
  - ...

Example 2: Discover A2A Agents

You: "What A2A agents are available for code review?"
Agent (via Global Chat MCP): Found 12 A2A agents for code review across 3 registries...

Example 3: Check Agent Protocol Coverage

You: "How many registries list tools for Kubernetes management?"
Agent (via Global Chat MCP): 4 registries carry Kubernetes-related agents (23 total entries)...

Best Practices

Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.

• Use the MCP server for automated workflows and agent-to-agent discovery
• Use the web directory for manual exploration and comparison
• Validate your agents.txt before publishing to ensure maximum discoverability
• Check multiple registries — coverage varies significantly by domain
• Keep the imported skill grounded in the upstream repository; do not invent steps that the source material cannot support.
• Prefer the smallest useful set of support files so the workflow stays auditable and fast to review.
• Keep provenance, source commit, and imported file paths visible in notes and PR descriptions.

Imported Operating Notes

Imported: Best Practices

• Use the MCP server for automated workflows and agent-to-agent discovery
• Use the web directory for manual exploration and comparison
• Validate your agents.txt before publishing to ensure maximum discoverability
• Check multiple registries — coverage varies significantly by domain

Troubleshooting

Problem: The operator skipped the imported context and answered too generically

Symptoms: The result ignores the upstream workflow in

plugins/antigravity-awesome-skills-claude/skills/global-chat-agent-discovery

metadata.json

ORIGIN.md

Problem: The imported workflow feels incomplete during review

Symptoms: Reviewers can see the generated

SKILL.md

Problem: The task drifted into a different specialization

Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better. Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.

Related Skills

• @github-issue-creator

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @github-workflow-automation

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @gitlab-automation

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @gitlab-ci-patterns

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

Additional Resources

Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.

references

references/n/a

examples

examples/n/a

scripts

scripts/n/a

agents

agents/n/a

assets

assets/n/a

Imported Reference Notes

Imported: Common Pitfalls

• Problem: Search returns too many results Solution: Add protocol or registry filters to narrow the scope

• Problem: MCP server not connecting Solution: Ensure

  npx

  is available and run

  npx -y @global-chat/mcp-server

  manually first to verify

Imported: Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.