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 appleweiping/WEIPING_WIKI --skill lark-wikigit clone --depth 1 https://github.com/appleweiping/WEIPING_WIKIWrote 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/appleweiping/weiping_wiki/lark-wiki)<a href="https://agentmods.dev/skills/appleweiping/weiping_wiki/lark-wiki"><img src="https://agentmods.dev/badge/skills/appleweiping/weiping_wiki/lark-wiki/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/appleweiping/weiping_wiki/lark-wiki"><img src="https://agentmods.dev/badge/skills/appleweiping/weiping_wiki/lark-wiki.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00081 | $0.01951 |
| Opus 5 | $0.00041 | $0.00975 |
| Sonnet 5 | $0.00016 | $0.00390 |
| Haiku 4.5 | $0.00008 | $0.00195 |
Grade A, and why
lark-wiki 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 6d 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 — 107 lines — stays where its author put it; the contents beside it link to each section on GitHub.
wiki (v2)
CRITICAL — 开始前 MUST 先用 Read 工具读取 ../lark-shared/SKILL.md,其中包含认证、权限处理
成员管理硬限制:
- 如果目标是“部门”,先判断身份,再决定是否继续。
--as bot对应tenant_access_token。官方限制:这种身份下不能使用部门 ID (opendepartmentid) 添加知识空间成员。- 遇到“部门 + --as bot”时,禁止先调用
lark-cli wiki members create试错;直接说明该路径不可行。- 如果用户明确要求“以 bot 身份运行”,且目标是部门,必须停下说明 bot 路径无法完成,不要静默切到
--as user。
快速决策
- 用户给的是知识库 URL(
.../wiki/<token>),且后续要查成员/加成员/删成员:先调用lark-cli wiki spaces get_node --params '{"token":"<wiki_token>"}'获取space_id,后续成员接口统一使用space_id。 - 用户要删除知识空间(
wiki +delete-space)但只给了名称或 URL:不能把名称 / URL 原样传给--space-id,必须先解析出真实space_id。解析方式:- URL(
.../wiki/<token>):lark-cli wiki spaces get_node --params '{"token":"<wiki_token>"}' --format json,读data.node.space_id。 - 只知名称:
lark-cli wiki spaces list --format json,边翻页边收集 items 并按name精确匹配;一旦任一页累计到至少 1 条精确匹配就停止翻页。只有当翻完所有页(has_more=false)仍无精确匹配时,才对已收集的全量 items 做宽松匹配(nametrim 空格、大小写不敏感、子串包含)。 - 关键安全约束:无论精确还是模糊,无论命中 1 条还是多条,发起删除前都必须把候选(
name+space_id+description+space_type)列给用户,由用户明确选定一个space_id再执行。不要因为"只命中一条"就自动执行删除。 - 命中 0 条:停下来问用户是名称拼错了还是调用方无权限;不要自行改名字重试。
- 用户明确选定后再执行
lark-cli wiki +delete-space --space-id <ID> --yes(高风险写操作,必须显式--yes)。
- URL(
- 用户要在知识库中创建新节点,优先使用
lark-cli wiki +node-create。 - 用户说“给知识库添加成员/管理员”:先把目标解析成“用户 / 群 / 部门”三类之一,再决定
member_type,不要先调wiki members create再根据报错反推类型。 - 用户说“部门 + bot”:这是已知不支持路径。不要继续尝试
wiki members create --as bot;直接提示必须改成--as user,或明确告知当前要求无法完成。 - 用户说“用户 / 群 + 添加成员”:先解析对应 ID,再执行
wiki members create。
成员添加流程
- 调用
lark-cli wiki members create前,先把自然语言里的“人 / 群 / 部门”解析成正确的member_id,不要猜格式。 - 用户场景默认优先
member_type=openid:用lark-cli contact +search-user --query "<姓名/邮箱/手机号>" --format json获取open_id。 - 群组场景使用
member_type=openchat:用lark-cli im +chat-search --query "<群名关键词>" --format json获取chat_id。 userid/unionid只在下游明确要求时才使用;先拿到open_id,再调用lark-cli api GET /open-apis/contact/v3/users/<open_id> --params '{"user_id_type":"open_id"}' --format json读取user_id/union_id。- 部门场景使用
member_type=opendepartmentid:当前 CLI 没有 shortcut,需调用lark-cli api POST /open-apis/contact/v3/departments/search --as user --params '{"department_id_type":"open_department_id"}' --data '{"query":"<部门名>"}'获取open_department_id。 - 只有在目标类型和身份都已确认可行后,才调用
lark-cli wiki members create。对于部门场景,这意味着必须是--as user。
What ships with it
6 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.
- 6d ago First seen · 107 lines · 81 tokens per session scan A 10010c3134b0
lark-wiki is a skill published in the GitHub repository appleweiping/WEIPING_WIKI (122 stars, last pushed 15d ago), licensed MIT. It adds 81 tokens to every session and 1,951 once invoked, about $0.0004 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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