adr

adr is a skill for Claude Code from beevibe-ai/beevibe-cto. It costs 124 tokens per session (1,035 once invoked), scanned A, original, Apache-2.0.

A workflow for making architecture decisions by scanning a repository, drafting a PRD, researching options, comparing evidence, and preparing an implementation handoff.

In plain words
What is it for?
Use it to evaluate choices such as vector stores, message queues, retrieval patterns, or authentication providers.
Why use it?
It gives structure to decisions that might otherwise rely on incomplete code inspection or disconnected research.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter. Also seen: mentions Claude Code.

Good fit Use it to evaluate choices such as vector stores, message queues, retrieval patterns, or authentication providers.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/beevibe-ai/beevibe-cto/claude-code-skill
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add beevibe-ai/beevibe-cto --skill claude-code-skill
Clone the repo
git clone --depth 1 https://github.com/beevibe-ai/beevibe-cto

Made for: Claude Code.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for adr

README.md
[![agentmods](https://agentmods.dev/badge/skills/beevibe-ai/beevibe-cto/claude-code-skill.svg)](https://agentmods.dev/skills/beevibe-ai/beevibe-cto/claude-code-skill)
Your own site
<a href="https://agentmods.dev/skills/beevibe-ai/beevibe-cto/claude-code-skill"><img src="https://agentmods.dev/badge/skills/beevibe-ai/beevibe-cto/claude-code-skill.svg" alt="Measured on agentmods" height="20"></a>
Per session 124 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,035 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00124 $0.01035
Opus 5 $0.00062 $0.00517
Sonnet 5 $0.00025 $0.00207
Haiku 4.5 $0.00012 $0.00103

Measured 8d ago against content hash 82ccd4e7dfb6, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-07, from the pricing page.

Security

Grade A, and why

adr scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

examples/claude-code-skill/SKILL.md · 100 lines

How it starts

The opening of the file, as written. The whole thing — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.

/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:

  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

Read the full file on GitHub · 100 lines

Files

What ships with it

2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 8d ago First seen · 100 lines · 124 tokens per session scan A 82ccd4e7dfb6

Subscribe to this mod's changes

adr is a skill published in the GitHub repository beevibe-ai/beevibe-cto (105 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 124 tokens to every session and 1,035 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.