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integrations

Official integrations and installable doctrine for AI Design Blueprint: MCP, IDE rules, prompt files, and agent runtimes.

Install / Use

claude mcp add aidesignblueprint -- npx -y github:aidesignblueprint/integrations

If the server publishes to npm under a different name, use that package instead — check the repo README.

About this skill
🔌

MCP Server

Model Context Protocol server

Quality Score

76/100

Supported Platforms

Claude Code
Claude Desktop
Cursor

Our assessment of integrations

integrations scores 76/100 on our quality scale, 619th of 762 AI & Machine Learning skills we index.

Its MCP Server is 5.0 KB long, well organised into 12 sections with 2 code examples: a solid amount of guidance for an agent.

It has 3 GitHub stars, so there is little community track record yet; judge it on its content.

Substance
26/30
Structure
18/20
Description
15/15
Adoption
3/20
Freshness
15/15

Maintenance, license and trust

  • The repository was last updated 2 days ago, so integrations is actively maintained.
  • It is released under the MIT license, a permissive license that allows use, modification and commercial use with attribution.
  • Its trust signals score 92/100, with 1 caution from licensing, adoption, age or documentation. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.

integrations compared with similar skills

All 4 of these similar skills score higher than integrations; compare them before choosing.

SkillScoreStarsUpdatedFormat
integrations (this skill)by aidesignblueprint7632d agoMCP Server
claude-memby thedotmack10094.8k1d agoCLAUDE.md
Agent-Reachby Panniantong10085.7k12d agoCLAUDE.md
Understand-Anythingby Egonex-AI10084.3k15d agoCLAUDE.md
headroomby headroomlabs-ai10073.9ktodayCLAUDE.md

Frequently asked questions

How do I install integrations?
Run claude mcp add aidesignblueprint -- npx -y github:aidesignblueprint/integrations. The install tabs above show the steps for each supported agent.
Which AI agents does integrations work with?
It is written for Claude Code, Claude Desktop and Cursor, as a MCP Server file. Other agents that read the same format can often use it too.
Is integrations safe to use?
It is MIT-licensed and scores 92/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
Is integrations still maintained?
The repository was last updated 2 days ago, so integrations is actively maintained.

AI Design Blueprint Integrations

smithery badge integrations MCP server

Official integrations and installable doctrine for AI Design Blueprint across MCP, IDE rules, prompt files, and agent runtimes.

What is in this repo

  • shared/: cross-tool doctrine files
  • mcp/: public MCP configuration and usage notes
  • docs/setup/: copy-first setup guides by tool
  • cursor/, windsurf/, github-copilot/, gemini/: provider-specific instruction files
  • open-weights/: static prompt packs for open-weight and local model workflows
  • exports/: structured doctrine export

Public contract

Canonical public endpoints:

  • Site: https://aidesignblueprint.com
  • MCP: https://aidesignblueprint.com/mcp
  • Developer docs: https://aidesignblueprint.com/en/for-agents

Quick start

  1. Pick a setup guide in docs/setup/.
  2. Add the relevant file or MCP config to your own repository or client.
  3. If using MCP, initialize against https://aidesignblueprint.com/mcp.
  4. Run the first proof call:
    • clusters.list()
  5. Then run a second proof call:
    • examples.search(query="orchestration visibility steering", limit=3)

Public MCP tools

Public retrieval tools (anonymous-allowed, read-only)

  • principles.list(cluster?)
  • clusters.list()
  • principles.get(slug)
  • clusters.get(slug)
  • examples.get(slug)
  • principles.search(query, limit?)
  • examples.search(query, principle_ids?, difficulty?, library?, limit?)
  • assets.list()
  • guides.list()
  • guides.get(slug)
  • guides.search(query, limit?)

Public signal tools (anonymous-allowed, opt-in write)

  • signals.report(event_type, surface_used?, brief_context?, perceived_value?, workflow_stage?, would_recommend?, team_size?) — records a value moment; only offer after the user clearly expresses something was useful; never call automatically or silently
  • signals.feedback(task_type?, surface?, rating_clarity?, rating_usefulness?, what_helped?, what_missing?, would_use_again?, contact_email?, permission_to_follow_up?) — explicit qualitative feedback; only call when the user explicitly asks to leave feedback

Signal tools write only the structured fields you pass. No prompts, no code, no file contents are stored. See the privacy policy for full data-handling details.

Protected tools (authenticated, not part of anonymous setup path)

  • me.learning_path()
  • me.coaching_context()
  • architect.validate(implementation_context, ..., private_session?) — Pro/Teams; scores agentic code against the 10 principles; set private_session=true to skip the stored run for that call
  • design.validate(implementation_context, ..., private_session?) — Pro/Teams; the surface mirror: scores a rendered frontend artefact against the 8 experience-design laws (own weekly bucket)
  • spec.validate(implementation_context, ..., private_session?) — Pro/Teams; the what-to-build lens: scores a written specification against the 8 spec-quality laws (own weekly bucket)
  • team.summarize(days_back?, private_session?) — Pro/Teams; usage reflection and recommended next assets across all three validator lenses
  • me.add_evidence(course_slug, stage_id, note)

Feedback and value signal rules

  • Only call signals.report after the user has clearly expressed that something was useful. Never call automatically or silently. Offer at most once per session after a clear success signal.
  • Only call signals.feedback when the user explicitly asks to leave feedback. Never prompt for it proactively.
  • Never include proprietary code, file contents, or secrets in brief_context.

Governance badges

Show that your agent or repo follows the Blueprint doctrine.

Free badge — paste into your README.md (no account required):

[![AI Design Blueprint](https://aidesignblueprint.com/api/badge/free.svg)](https://aidesignblueprint.com)

Pro badge — run architect.validate() via the MCP. The response includes run_id, badge_url, and review_url:

[![AI Design Blueprint](https://aidesignblueprint.com/api/badge/run/<run_id>.svg)](https://aidesignblueprint.com/en/readiness-review/<run_id>)

The Pro badge displays your tier (Governed · X/Y or Reviewed · X/Y) and links to a public readiness review page. Requires a Pro or Beta account.

What is intentionally not here yet

  • no public OpenAPI schema
  • no public HTTP API contract beyond MCP and static assets
  • no CLI installer
  • no speculative partner-specific distributions

Source of truth

This repo is intended to mirror the canonical public contract already shipped on aidesignblueprint.com.

Before publishing changes here, verify:

  • /mcp
  • /llms.txt
  • /agent-assets/[slug]
  • /en/for-agents

remain consistent with the files committed in this repo.

Related Skills

View on GitHub
GitHub Stars3
CategoryAI
Updated2d ago
Forks0

Languages

Python

Trust signals

92/100

From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.

1 low