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.
npx skills add beevibe-ai/beevibe-cto --skill claude-code-skillgit clone --depth 1 https://github.com/beevibe-ai/beevibe-ctoWrote 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.
[](https://agentmods.dev/skills/beevibe-ai/beevibe-cto/claude-code-skill)<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>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.
| Model | Per session | Once 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 |
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.
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:
- 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.jsonso 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 familyhandoff.required_invariants— non-negotiable constraintshandoff.forbidden_topologies— what NOT to dohandoff.critique_summary.recommend_human_review— if true, the kernel is telling you the decision is borderlinehandoff.comparison_matrix_summary— candidate count, empty cellshandoff.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.mdfor the full human-readable decision record - Walk through the comparison matrix
- Implement using
execution-handoff.jsonas the contract
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.
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.
- 8d ago First seen · 100 lines · 124 tokens per session scan A 82ccd4e7dfb6
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.
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feishu
Work with Feishu or Lark bots, docs, sheets, bitables, approval flows, and OpenAPI/MCP setup without hardcoding credentials.
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recipe-create-meet-space
Create a Google Meet meeting space and share the join link.
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Measure and interpret PR rework rate — the emerging 5th DORA metric.