Agent skill

Review Shadow MCP

by speakeasy-api in speakeasy-api/gram

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

AGPL-3.0Auto-check passedAgent Workflows

Install Review Shadow MCP

skills CLI
$ npx skills add speakeasy-api/gram --skill review-shadow-mcp -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install speakeasy-api/gram review-shadow-mcp --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/speakeasy-api/gram.git skills-src && mkdir -p .claude/skills && cp -r skills-src/server/internal/plugins/platform_mcp_skills/review-shadow-mcp .claude/skills/review-shadow-mcp && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
review-shadow-mcp
GitHub stars
273
Token cost
~1.5k tokens
SKILL.md length
816 words
Files
1
Skills in repo
39
Repo updated
First seen
Licence
AGPL-3.0

At a glance

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

  • Works in 9 steps: Call list_projects. If its truncated… → Call list_shadow_mcp_inventory with that… → Establish the intended approval… → …
  • Tasks that involve MCP servers
  • SKILL.md covers Safety rules and Workflow
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Review Shadow MCP is an agent skill from speakeasy-api/gram. Review a Shadow MCP target, approve an explicit audience, and safely onboard and distribute it through the Speakeasy AI Control Plane Platform MCP.

Its SKILL.md is about 1.5k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Agent Workflows, covering MCP servers. It works with Model Context Protocol. The repository describes itself as: Securely scale AI usage across your organization. A single stack to Connect, Secure, Observe and Distribute agents, MCPs, and Skills within your company. The licence is AGPL-3.0.

When your agent uses it

  • Tasks that involve MCP servers

Example prompts

  • “/review-shadow-mcp”

Workflow steps

9 steps, taken from the first numbered list in SKILL.md.

  1. Call list_projects. If its truncated result is true, report that project discovery is incomplete and hand off to the AICP dashboard…
  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…
  3. Establish the intended approval audience. Call list_plugin_assignments for the exact project. If the user intends to match an existing…
  4. Refresh get_shadow_mcp_review and any audience references before presenting the bounded review evidence, every gap, the proposed allow or…
  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…
  6. After dashboard onboarding, call find_mcp with an explicit user-provided query, present the bounded configured MCP matches, and ask the…
  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…
  8. Present the exact plugin, its complete audience, the ready MCP, and the current admission and publication states. Confirm this exact…
  9. After distribution, call get_plugin and get_shadow_mcp_review again. Report the live attachment, distribution admission, and publication…

What it can do on your machine

Read from SKILL.md and the folder at commit 1378037. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Review Shadow MCP loads about 1.5k tokens when it runs. Until then it costs about 41 tokens; SKILL.md has 816 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~41
When it runs · the whole SKILL.md, loaded when a task matches
~1.5k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from speakeasy-api/gram at commit 1378037, republished under its AGPL-3.0 licence (© speakeasy-api). 816 words, ~1,486 tokens.

Download SKILL.mdSave it as .claude/skills/review-shadow-mcp/SKILL.md (or your agent's skills folder).
name
review-shadow-mcp
description
Review a Shadow MCP target, approve an explicit audience, and safely onboard and distribute it through the Speakeasy AI Control Plane Platform MCP.

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.
Show full SKILL.md (31 more words)Show less

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.

© speakeasy-api, AGPL-3.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in server/internal/plugins/platform_mcp_skills/review-shadow-mcp of speakeasy-api/gram.

Open the folder on GitHubat commit 1378037

Compare with similar skills

Review Shadow MCP next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Review Shadow MCP compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Review Shadow MCP this skillspeakeasy-api/gram273—~1.5kAutomated safety check: PassAGPL-3.0
MCP Server Builderanthropics/skills180k63 repos~2.3kAutomated safety check: PassApache-2.0
MCP Server BuildershareAI-lab/learn-claude-code78k5 repos~1.2kAutomated safety check: PassMIT
MCP Integration for Pluginsanthropics/claude-plugins-official38k11 repos~3.1kAutomated safety check: PassApache-2.0
Crush Configurationcharmbracelet/crush29k—~3.7kAutomated safety check: PassCustom licence
Context Mode Output Sandboxmksglu/context-mode26k—~4.1kAutomated safety check: PassCustom licence

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Categories

Questions about Review Shadow MCP

What does Review Shadow MCP do?

Review a Shadow MCP target, approve an explicit audience, and safely onboard and distribute it through the Speakeasy AI Control Plane Platform MCP. Review Shadow MCP is an agent skill from speakeasy-api/gram. Review a Shadow MCP target, approve an explicit audience, and safely onboard and distribute it through the Speakeasy AI Control Plane Platform MCP.

When should I use Review Shadow MCP?

Review Shadow MCP fits situations like: tasks that involve MCP servers.

How do I install Review Shadow MCP in Claude Code?

Run `npx skills add speakeasy-api/gram --skill review-shadow-mcp -a claude-code`. Or copy the skill folder (server/internal/plugins/platform_mcp_skills/review-shadow-mcp in speakeasy-api/gram) into .claude/skills/review-shadow-mcp in your project. Claude Code loads it when a task matches its description.

How do I install Review Shadow MCP in Codex?

Run `npx skills add speakeasy-api/gram --skill review-shadow-mcp -a codex`. Or copy the skill folder (server/internal/plugins/platform_mcp_skills/review-shadow-mcp in speakeasy-api/gram) into .agents/skills/review-shadow-mcp in your project. Codex loads it when a task matches its description.

Can I use Review Shadow MCP in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add speakeasy-api/gram --skill review-shadow-mcp -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/review-shadow-mcp, .gemini/skills/review-shadow-mcp, .github/skills/review-shadow-mcp and .opencode/skills/review-shadow-mcp in your project.

What does Review Shadow MCP need to run?

SKILL.md names no scripts, command-line tools or credentials: Review Shadow MCP is instructions for the agent only.

Does Review Shadow MCP access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Review Shadow MCP safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Review Shadow MCP use?

Review Shadow MCP is published under the AGPL-3.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Review Shadow MCP use?

About 1.5k tokens (SKILL.md is roughly 5.9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Review Shadow MCP?

Skills that share tags, products or a category with Review Shadow MCP: MCP Server Builder (anthropics/skills, 180k stars), MCP Server Builder (shareAI-lab/learn-claude-code, 78k stars), MCP Integration for Plugins (anthropics/claude-plugins-official, 38k stars) and Crush Configuration (charmbracelet/crush, 29k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Review Shadow MCP?

speakeasy-api (a GitHub organization) maintains it in speakeasy-api/gram, which has 273 GitHub stars. The repository holds 39 skills in this directory. The repository was last updated on October 9, 2026.

Source: speakeasy-api/gram on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.