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Review Shadow Mcp

Organization
speakeasy-api
review-shadow-mcp

Review a Shadow MCP target, approve an explicit audience, and safely onboard and distribute it through the Speakeasy AI Control Plane Platform MCP.

Overview

Publisherspeakeasy-api
Repositorygram
Skill namereview-shadow-mcp
Stars
269
Forks
33
Bundled files
Instructions only
LicenseAGPL-3.0
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by speakeasy-api on GitHub. Read the source before you install it.

Installation

Install the Review Shadow Mcp 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/speakeasy-api/gram.git /tmp/gram
mkdir -p .claude/skills
cp -r /tmp/gram/server/internal/plugins/platform_mcp_skills/review-shadow-mcp .claude/skills/review-shadow-mcp
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Review Shadow Mcp 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 Review Shadow Mcp 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 Review Shadow Mcp 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.

Review and distribute a Shadow MCP

Use this workflow only through an authenticated Speakeasy AI Control Plane (AICP) Platform MCP connected as an external administrator. It follows the guarded AICP dashboard outcome: review one observed target, make an explicit audience decision, optionally onboard an approved remote target, and distribute the ready MCP to one exact plugin. It is not available to managed project assistants or the member-safe catalogue. Installing this package grants no organization access or authority.

Safety rules

  • Keep the project, opaque Shadow MCP target, approval audience, rationale, registered MCP, and plugin explicit. Never infer or silently substitute them.
  • Treat server-returned evidence as bounded. Report every gap and incomplete result. Do not recover raw target values, people, principals, evidence, or research traces.
  • Never widen an audience to Everyone to make approval or distribution succeed. Everyone is valid only when the user deliberately selects and confirms the server-returned Everyone reference.
  • Secrets never enter chat. Present only setup, provider, and authorization URLs returned by AICP, and wait for the user to complete secure setup in the browser.
  • Registration is private and separate from approval and distribution. Do not claim an atomic promotion from observed target to distributed MCP.
  • A review decision or receipt does not prove current distribution admission. Registration re-inspects the target, and distribution rechecks the current approval and complete plugin audience.
  • A denial, unavailable admission result, or version conflict returns to fresh reads and user review. Never approve again, change the audience, or retry a mutation automatically.

Workflow

  1. Call list_projects. If its truncated result is true, report that project discovery is incomplete and hand off to the AICP dashboard; otherwise present the eligible projects and ask the user to select one exact project. Retain both its returned ID for Shadow and plugin inventory tools and its slug for readiness and distribution tools.
  2. Call list_shadow_mcp_inventory with that exact project ID. Present only its bounded summaries and ask the user to select one exact opaque target reference. Call get_shadow_mcp_review with the same project ID and target reference.
  3. Establish the intended approval audience. Call list_plugin_assignments for the exact project. If the user intends to match an existing plugin, ask them to name it and call get_plugin; use only a complete, untruncated assignment set. Ask the user to select exact server-returned audience references and provide a bounded rationale. Stop and use the AICP dashboard if the required assignments are truncated or incomplete.
  4. Refresh get_shadow_mcp_review and any audience references before presenting the bounded review evidence, every gap, the proposed allow or deny decision, the complete selected audience, and the rationale. Ask for explicit confirmation of that exact fresh project, target, decision, audience, and rationale, then immediately call decide_shadow_mcp_access with the immediately preceding expected_version, a fresh idempotency key, and confirmed: true. If it conflicts or a reference expires, re-read and re-present the changed state, then obtain confirmation again. An allow requires one or more selected audience references; a deny has none.
  5. Call get_shadow_mcp_review again and report the committed live review. The review result does not expose a raw remote URL or an onboarding action, so never reconstruct either. To onboard an approved target, hand off to the AICP dashboard and stop until the user confirms dashboard setup is complete.
  6. After dashboard onboarding, call find_mcp with an explicit user-provided query, present the bounded configured MCP matches, and ask the user to select one exact result. Call get_mcp for that exact MCP and continue only when it returns a Platform-managed registration ID. Call get_mcp_readiness with the exact project slug and registration ID and force: true; continue only when fresh evidence says the MCP is ready.
  7. Call list_plugins, present the exact project plugins, and ask the user to choose one. Call get_plugin for that exact plugin and require its complete, untruncated assignment set. Compare the whole set with the approved audience and current distribution_admission; keep publication state separate. If assignments must change, refresh list_plugin_assignments, present the complete replacement and state that it changes who receives every MCP server in that plugin, not only this target. Ask for explicit confirmation, then call set_plugin_assignments with the immediately preceding assignment_version as expected_assignment_version, a fresh idempotency key, and confirmed: true. Re-read get_plugin after the mutation.
  8. Present the exact plugin, its complete audience, the ready MCP, and the current admission and publication states. Confirm this exact distribution with the user before calling distribute_mcp_to_plugin. If it returns a denial or conflict, re-read get_shadow_mcp_review and get_plugin and return to user review without automatically changing or renewing the approval.
  9. After distribution, call get_plugin and get_shadow_mcp_review again. Report the live attachment, distribution admission, and publication evidence separately. Do not claim that users have the MCP unless the returned live state supports that conclusion.

The approval, registration, assignment, and distribution confirmations are separate decisions. Browser setup and authorization are separate secure handoffs. Preserve each boundary even when the user wants to complete the whole workflow.

Frequently asked questions

What does the Review Shadow Mcp AI skill do?

Review a Shadow MCP target, approve an explicit audience, and safely onboard and distribute it through the Speakeasy AI Control Plane Platform MCP.

Why use Review Shadow Mcp on TypingMind?

Because you install it once and use it with any model. Review Shadow Mcp 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 Review Shadow Mcp in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/speakeasy-api/gram/tree/main/server/internal/plugins/platform_mcp_skills/review-shadow-mcp. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Review Shadow Mcp?

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 Review Shadow Mcp?

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

Is the Review Shadow Mcp AI skill free?

Yes. It is published on GitHub by speakeasy-api under the AGPL-3.0 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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