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 seaworld008/Commonly-used-high-value-skills --skill lark-contactgit clone --depth 1 https://github.com/seaworld008/Commonly-used-high-value-skillsWrote 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/seaworld008/commonly-used-high-value-skills/lark-contact)<a href="https://agentmods.dev/skills/seaworld008/commonly-used-high-value-skills/lark-contact"><img src="https://agentmods.dev/badge/skills/seaworld008/commonly-used-high-value-skills/lark-contact/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/seaworld008/commonly-used-high-value-skills/lark-contact"><img src="https://agentmods.dev/badge/skills/seaworld008/commonly-used-high-value-skills/lark-contact.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Prompt Injection · line 82 Hidden instructions were detected in comments or invisible text. These could contain malicious directives. Manual review is recommended.Fix: Audit all comments and invisible characters. Remove any instructions that direct the agent to perform unauthorized actions. Use plain, reviewable content.
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 | $0.00120 | $0.01435 |
| Opus 5 | $0.00060 | $0.00718 |
| Sonnet 5 | $0.00024 | $0.00287 |
| Haiku 4.5 | $0.00012 | $0.00144 |
Grade A, and why
lark-contact 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.
How it starts
The opening of the file, as written. The whole thing — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.
选哪个命令
user 身份和 bot 身份是两条完全独立的路径。先确定当前身份,再按下表选命令:
| 想做什么 | user 身份 | bot 身份 |
|---|---|---|
| 按姓名 / 邮箱搜员工拿 open_id | +search-user |
不支持 |
| 按关键词搜索当前用户可见的机器人 / 智能体 | +search-bot |
不支持 |
| 已知 open_id 取他人资料 | +search-user --user-ids <id> |
+get-user --user-id <id> |
| 查看自己 | +get-user 或 +search-user --user-ids me |
不支持 |
| 查同事的个人状态 / 签名 | user_profiles batch_query |
不支持 |
已知 open_id 只是想发消息 / 排日程,不必经过 contact —— 直接 lark-im / lark-calendar。
名字没说清是人还是机器人 / 智能体
用户给的名字常常不表明类型。例如「和 reviewDuck 约个会」里的 reviewDuck 可能是同事昵称,也可能是机器人。
- 名字含 bot / agent / AI / 助手 / 机器人 / 智能体 / assistant 等明显特征时,反过来先搜机器人更快
- 不确定的话两边都搜一下
典型场景
找张三给他发消息:先搜,确认 open_id,再发:
lark-cli contact +search-user --query "张三" --has-chatted --as user
lark-cli im +messages-send --user-id ou_xxx --text "Hi!"
批量查同事的个人状态 / 个性签名(先用 schema 看参数)。
lark-cli schema contact.user_profiles.batch_query
lark-cli contact user_profiles batch_query \
--params '{"user_id_type":"open_id"}' \
--data '{"user_ids":["ou_xxx","ou_yyy"],"query_option":{"include_personal_status":true,"include_description":true}}' \
--as user
搜索命中多条且后续操作有副作用(发消息、邀请会议等),把候选列给用户挑;不要擅自选第一条。
搜索机器人 / 智能体
+search-bot 使用 user 身份按关键词搜索当前用户可见的机器人,返回 ou_ 开头的机器人 open_id。参数细节等见 lark-contact-search-bot.md。
lark-cli contact +search-bot --query '会议助手' --as user
lark-cli contact +search-bot --queries '会议助手,日报助手,审批助手' --as user
注意事项
- 41050 / Permission denied 受当前身份的可见范围限制(三条命令都可能遇到)。细节见
lark-shared。 - 跨租户用户(
is_cross_tenant=true)多数业务字段为空字符串,这是飞书可见性规则,下游做空值兜底。 - ID 类型:
+get-user可通过--user-id-type使用open_id、union_id或user_id;+search-user使用用户 open_id;+search-bot不支持按 ID 查询,它按关键词搜索并返回机器人 open_id。
不在本 skill 范围
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 · 110 lines · 120 tokens per session scan A 21fc8955c73e
lark-contact is a skill published in the GitHub repository seaworld008/Commonly-used-high-value-skills (70 stars, last pushed 5d ago), licensed MIT. It adds 120 tokens to every session and 1,435 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 2026-09-03.
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