lark-wiki

lark-wiki is a skill for Claude Code, Codex from ddpie/lark-mcp-on-agentcore. It costs 169 tokens per session (2,483 once invoked), scanned A, original, MIT.

A Feishu/Lark knowledge-base skill for managing knowledge spaces, members, document nodes, and their hierarchy. A knowledge base is an organized collection of linked documents.

In plain words
What is it for?
Creating and browsing knowledge spaces, managing members, arranging document nodes, and working with wiki links or tokens.
Why use it?
It helps locate, organize, move, copy, and manage access to documents in Lark's knowledge spaces while routing document editing and file uploads to their dedicated tools.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Creating and browsing knowledge spaces, managing members, arranging document nodes, and working with wiki links or tokens.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/ddpie/lark-mcp-on-agentcore/lark-wiki
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 ddpie/lark-mcp-on-agentcore --skill lark-wiki
Clone the repo
git clone --depth 1 https://github.com/ddpie/lark-mcp-on-agentcore

Made for: Claude Code, Codex.

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 lark-wiki

README.md
[![agentmods](https://agentmods.dev/badge/skills/ddpie/lark-mcp-on-agentcore/lark-wiki/github.svg)](https://agentmods.dev/skills/ddpie/lark-mcp-on-agentcore/lark-wiki)
Your own site
<a href="https://agentmods.dev/skills/ddpie/lark-mcp-on-agentcore/lark-wiki"><img src="https://agentmods.dev/badge/skills/ddpie/lark-mcp-on-agentcore/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.

agentmods 80×15 button for lark-wiki

Your own site · 80×15
<a href="https://agentmods.dev/skills/ddpie/lark-mcp-on-agentcore/lark-wiki"><img src="https://agentmods.dev/badge/skills/ddpie/lark-mcp-on-agentcore/lark-wiki.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 169 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,483 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. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
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 $0.00169 $0.02483
Opus 5 $0.00084 $0.01241
Sonnet 5 $0.00034 $0.00497
Haiku 4.5 $0.00017 $0.00248

Measured 9d ago against content hash 7bf72f3e5f0f, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

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 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.

docker/skills/lark-wiki/SKILL.md · 106 lines

How it starts

The opening of the file, as written. The whole thing — 106 lines — stays where its author put it; the contents beside it link to each section on GitHub.

wiki (v2)

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。
  • bot identity 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 + bot identity"时,禁止先调用 lark_wiki_member_add 试错;直接说明该路径不可行。
  • ⚠️ bot identity 相关操作不可通过 MCP server 执行(MCP server 始终使用 user identity)。

身份说明

知识空间和节点都是用户的个人资源。MCP server 始终使用 user identity(authentication is handled automatically by the MCP server)。

快速决策

  • 用户要整理 / 盘点 / 归类 / 重构知识库、个人文档库、文档库目录或 Wiki 节点结构,或要生成整理方案、目标目录树、移动计划时,不要只使用 Wiki 节点 API。必须先调用 lark_get_skill(domain="drive", section="workflow"),再按其中 Workflow Registry 进入 knowledge_organize workflow(调用 lark_get_skill(domain="drive", section="workflow-knowledge-organize"));该 workflow 负责 Drive / Wiki / 个人文档库的统一入口解析、资源盘点、分类计划、写前确认和结果验证。
  • 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_invoke(tool_name="lark_wiki_spaces_get_node", args={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(lark_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_invoke(tool_name="lark_wiki_spaces_get_node", args={params: {"token": "<wiki_token>"}, format: "json"}),读 data.node.space_id
    • 只知名称:lark_wiki_space_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(name trim 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_wiki_delete_space(space_id="<ID>", _confirm=true)(高风险写操作)。
    • 反例:不要把 wiki URL / 名称直接当 space_id(如 space_id="https://.../wiki/<wiki_token>");务必先用 lark_invoke(tool_name="lark_wiki_spaces_get_node", ...) 解析出 data.node.space_id 再传。
  • 用户要在知识库中创建新节点,优先使用 lark_wiki_node_create
  • 用户要列出 Wiki 节点:先用 lark_wiki_space_list 拿数字 space_id,再用 lark_wiki_node_list(space_id="<space_id>")。不要把 wiki URL、node token、doc token、名称直接当 space_id。钻子节点时 parent_node_token 必须是 wiki node token;如果用户给的是 docx/sheet/base URL,先用 lark_wiki_node_get(node_token="<url>") 解析出 node_token
  • lark_wiki_node_list 命中 invalid_parametersnot_foundpermission_denied 时,不要重复调用同一参数;按 hint 修 space_id / parent_node_token / 权限。只有 rate_limit 才做退避重试。
  • 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 / 应用"四类之一,再决定 member_type,不要先调 lark_wiki_member_add 再根据报错反推类型。
  • 用户说"部门 + bot":这是已知不支持路径。⚠️ This operation requires bot identity and is not available via the MCP server.
  • 用户说"用户 / 群 / 应用 + 添加成员":先解析对应 ID,再执行 lark_wiki_member_add
  • 用户说"查看 / 列出空间成员":用 lark_wiki_member_list;该 shortcut 默认只取一页,多成员场景显式加 page_all=true
  • 用户说"移除 / 删除空间成员":用 lark_wiki_member_remove,必须传齐原始授予时的 member_typemember_role(不知道就先 lark_wiki_member_list 查一下)。

Read the full file on GitHub · 106 lines

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. 9d ago First seen · 106 lines · 169 tokens per session scan A 7bf72f3e5f0f

Subscribe to this mod's changes

lark-wiki is a skill published in the GitHub repository ddpie/lark-mcp-on-agentcore (8 stars, last pushed 12d ago), licensed MIT. It adds 169 tokens to every session and 2,483 once invoked, about $0.0008 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 2026-08-31.