# Beevibe AI Beevibe Cto Beevibe Cto

> Beevibe Ai Beevibe Cto Beevibe Cto

- Skill: `tomevault-io/beevibe-ai-beevibe-cto-beevibe-cto` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add tomevault-io/beevibe-ai-beevibe-cto-beevibe-cto`
- Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/beevibe-ai-beevibe-cto-beevibe-cto/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: tomevault-io (https://skillmd.com/u/tomevault-io)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/tomevault-io/beevibe-ai-beevibe-cto-beevibe-cto

---


# /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:

```json
{
  "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:

1. Scan the user's repo and draft a PRD (no network calls).
2. Run the full ADR pipeline against the draft (research, knowledge map, comparison matrix, synthesis, citation audit, evaluation pack).
3. 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.

---
> Source: [beevibe-ai/beevibe-cto](https://github.com/beevibe-ai/beevibe-cto) — distributed by [TomeVault](https://tomevault.io).
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