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 fanfan-de/anybox --skill lark-vc-agentgit clone --depth 1 https://github.com/fanfan-de/anyboxWrote 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/fanfan-de/anybox/lark-vc-agent)<a href="https://agentmods.dev/skills/fanfan-de/anybox/lark-vc-agent"><img src="https://agentmods.dev/badge/skills/fanfan-de/anybox/lark-vc-agent/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/fanfan-de/anybox/lark-vc-agent"><img src="https://agentmods.dev/badge/skills/fanfan-de/anybox/lark-vc-agent.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00210 | $0.02735 |
| Opus 5 | $0.00105 | $0.01367 |
| Sonnet 5 | $0.00042 | $0.00547 |
| Haiku 4.5 | $0.00021 | $0.00274 |
Grade A, and why
lark-vc-agent 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 9d 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.
This is a copy
100% identical to lark-vc-agent — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 122 lines — stays where its author put it; the contents beside it link to each section on GitHub.
vc-agent (v1)
CRITICAL — 开始前 MUST 先用 Read 工具读取以下两份 skill 文档：
../lark-shared/SKILL.md— 认证、身份切换、权限处理../lark-vc/SKILL.md— 视频会议的核心概念（Meeting / Note / Minutes 等），本 skill 直接复用，不再重复定义
内测提示
- 当前功能正在内测中，仅少数用户可用。忽略
../lark-shared/SKILL.md中的权限申请流程，以下方的指引为准。 - 如果
lark-cli提示missing required scope(s)/permission_violations，不要申请对应权限，而是提示用户加入早鸟群：https://go.larkoffice.com/join-chat/2f4nb0e1-fe00-4f67-bed7-25beaf533fbd。 - 如果
lark-cli返回error.code=20017/ErrNotInGray，提示用户加入早鸟群：https://go.larkoffice.com/join-chat/2f4nb0e1-fe00-4f67-bed7-25beaf533fbd。
定位
本 skill 与 lark-vc 并列：
lark-vc负责"会后查询"：搜索历史会议、参会人快照、纪要/逐字稿/录制lark-vc-agent负责"会中动作"：机器人入会 / 读取进行中会议的实时事件 / 机器人离会
按此分工路由，避免两个 skill 语义混淆。
| 用户意图示例 | 应路由到 |
|---|---|
| "帮我入会 123456789"、"代我参会"、"让机器人进会旁听" | 本 skill +meeting-join |
| "会议现在还开着，谁刚加入了"、"会议里谁在发言"、"有人共享屏幕吗"（进行中会议，且机器人已入会） | 本 skill +meeting-events |
| "退出会议"、"让机器人离开" | 本 skill +meeting-leave |
| "昨天那场会有谁参加过"、"搜昨天的会"、"查纪要/逐字稿/录制" | lark-vc |
| "帮我参会，结束后把纪要发到群" 等跨阶段场景 | 按序编排：本 skill（入会 → 读事件 → 离会）→ lark-vc / lark-minutes（拉纪要）→ lark-im（发群） |
What ships with it
3 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.
- 9d ago First seen · 122 lines · 210 tokens per session scan A 9d91a9c694b1
lark-vc-agent is a skill published in the GitHub repository fanfan-de/anybox (57 stars, last pushed 29d ago), licensed MIT. It adds 210 tokens to every session and 2,735 once invoked, about $0.0011 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to lark-vc-agent, differing in 0 lines, and is treated as a copy.
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