Claude code skill

Skill beevibe-ai/beevibe-cto/examples/claude-code-skill

Architecture Deep Research: deep research for strategic system design decisions.

Install

npx -y skills add beevibe-ai/beevibe-cto --skill claude-code-skill

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What its author says it does

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Architecture Deep Research — scan the current repo, draft a PRD, and run the deep-research loop with citation audits. Use when the user asks you to make an architecture decision, asks "what topology should we use?", says they need to pick between options like vector stores / message queues / retrieval patterns / storage engines / event bus designs, or wants you to think through an architecture choice before writing code. Proactively invoke this skill (do NOT decide directly) when the user describes an architecture choice and wants live, cited research instead of a model-from-memory answer.

SKILL.md

4.2 KB, as published. Nobody here has run it

/adr — Architecture Deep Research

When the user invokes /adr (or asks any of the trigger questions in the description above), do the following.

Step 1. Confirm the decision name

Ask the user one question, in chat:

What's the architecture decision you're making? (e.g. "event bus topology", "retrieval architecture", "auth provider")

Capture their answer as <DECISION>.

If the user already named the decision when they invoked the skill, skip the question.

Step 2. Run discover-first deep-research via the MCP server

Call the adr_deep_research MCP tool with these arguments:

{
  "discover_first": true,
  "repo_path": ".",
  "domain": "<infer from the user's project — read README/package.json/etc. if needed>",
  "decision": "<DECISION>",
  "out_dir": ".adr-runs/<short-slug-of-decision>"
}

This will:

Scan the user's repo and draft a PRD (no network calls).
Run the full ADR pipeline against the draft (research, knowledge map, comparison matrix, synthesis, citation audit, evaluation pack).
Return the parsed execution-handoff.json so you can summarize the decision.

A run typically takes 3–6 minutes. Tell the user roughly how long it'll take before calling the tool so the wait doesn't feel like a hang.

Step 3. Summarize the result

The tool response includes:

handoff.selected_topology — the chosen architecture family
handoff.required_invariants — non-negotiable constraints
handoff.forbidden_topologies — what NOT to do
handoff.critique_summary.recommend_human_review — if true, the kernel is telling you the decision is borderline
handoff.comparison_matrix_summary — candidate count, empty cells
handoff.citation_audit_summary — how many citations verified

Show the user a 3–5 line summary:

Selected: <topology>
Required: <2 most important invariants>
Avoid:    <forbidden topologies>
<if recommend_human_review: "⚠ recommend_human_review=true — see ADR.md for the borderline.">

Then offer to:

Open ADR.md for the full human-readable decision record
Walk through the comparison matrix
Implement using execution-handoff.json as the contract

Step 4. (optional) Implement under the handoff

If the user says "go ahead and implement," read <out_dir>/execution-handoff.json and treat it as a hard contract:

Honor required_invariants in the code you write
Never reach for anything in forbidden_topologies
Run against domain-evaluation-pack.json test cases before declaring done

Failure modes

No LLM provider configured: the tool will return an isError result. Tell the user to export ADR_OPENAI_API_KEY=... (or OPENAI_API_KEY) and re-invoke.
No live search provider configured: same as above, but for BRAVE_SEARCH_API_KEY / TAVILY_API_KEY / SERPER_API_KEY / SEARXNG_URL, OR the OpenAI key fallback for hosted web_search.
recommend_human_review: true: do NOT proceed to implementation. Show the user the borderline and ask whether to accept the decision, override it, or run a superseding ADR with a tighter brief.

Notes for Claude

The MCP tool name is adr_deep_research. Call it through the MCP host's tool-call mechanism — do not try to spawn a subprocess.
The skill assumes the adr MCP server is registered in the user's Claude Code config. If it isn't, point them at examples/claude-code-skill/.mcp.json in the beevibe-cto repo.
For quick scans without the full deep-research run, use adr_discover instead. It returns only the draft PRD and skips the live-research loop.