lark-task

lark-task is a skill for Claude Code, Codex from Pinvou/pinvou-agent. It costs 178 tokens per session (3,724 once invoked), scanned A, a copy of lark-task, MIT.

Instructions for a Lark task skill. Lark, also called Feishu, is a collaboration platform with tasks and task lists.

In plain words
What is it for?
Use it to create, find, update, split, assign, or attach files to Lark tasks, and to manage task-focused agents.
Why use it?
They prevent the agent from guessing unsupported commands or parameters when managing tasks.

Skill for Claude CodeCodex

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

Needs its repository: it reads a path above its own folder, which exists only inside the repository. The line is **用户身份识别**:在用户身份(user identity)场景下,如果用户提到了“我”(例如“分配给我”、“由我创建”),请默认获取当前登录用户的 `open_id` 作为对应的参数值(用 `lark-cli auth status` 获取,从输出 JSON 的 `identities.user.openId` 提.

Good fit Use it to create, find, update, split, assign, or attach files to Lark tasks, and to manage task-focused agents.

Compare 6 skills from other repositories ↓
About the project

Pinvou/pinvou-agent is an open-source desktop workspace where an AI agent helps with work, visual design, and software development. People use it to work with files and knowledge, create editable visual artifacts, connect coding agents to projects, and extend the workspace with tools, MCP servers, skills, and workflows; the catalogue skills add capabilities to that environment.

Pinvou/pinvou-agent · 1,712 stars · on GitHub · pinvou.com

Install

Getting it into your agent

It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.

Clone the repo
git clone --depth 1 https://github.com/Pinvou/pinvou-agent
agentmods
npx agentmods add skills/pinvou/pinvou-agent/lark-task

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-task

README.md
[![agentmods](https://agentmods.dev/badge/skills/pinvou/pinvou-agent/lark-task.svg)](https://agentmods.dev/skills/pinvou/pinvou-agent/lark-task)
Your own site
<a href="https://agentmods.dev/skills/pinvou/pinvou-agent/lark-task"><img src="https://agentmods.dev/badge/skills/pinvou/pinvou-agent/lark-task.svg" alt="Measured on agentmods" height="20"></a>
Per session 178 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,724 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.
Origin 88% copy Near-identical to another mod 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 $0.00178 $0.03724
Opus 5 $0.00089 $0.01862
Sonnet 5 $0.00036 $0.00745
Haiku 4.5 $0.00018 $0.00372

Measured 8d ago against content hash acc0f229e335, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

lark-task 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.

Origin

This is a copy

88% identical to lark-task — 19 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.

pinvou3-app/src-tauri/resources/common/bundle/lark-skills/lark-task/SKILL.md · 187 lines

How it starts

The opening of the file, as written. The whole thing — 187 lines — stays where its author put it; the contents beside it link to each section on GitHub.

task (v2)

CRITICAL — 开始前 MUST 先用 File(action="read") 读取 ../lark-shared/SKILL.md,其中包含认证、权限处理

命令选择与渐进式发现(必读)

执行任何 Task 命令前,必须先确认能力真实存在,禁止根据用户意图自行拼接或猜测 +<verb>

  1. 先将用户意图与下方 Shortcut 表精确匹配。只有表中明确列出的 shortcut 才可直接选择;参数不确定时读取对应 reference 或运行该 shortcut 的 --help
  2. 没有精确匹配、或无法确认当前版本是否支持时,先运行 lark-cli task --help,以当前 CLI 输出的命令列表为准。
  3. help 中存在匹配 shortcut 时,使用 help 列出的完整 shortcut token(例如 +create)运行 lark-cli task <shortcut> --help,再按真实 flag 执行。
  4. help 中没有匹配 shortcut 时,不得尝试相似的 +<verb>;从 help 中选择原生 resource,运行 lark-cli task <resource> --help 确认 method,再运行 lark-cli schema task.<resource>.<method> 获取参数结构,最后调用 lark-cli task <resource> <method> ...
  5. 遇到 unknown_subcommand 时必须停止猜测或尝试变体,回到第 2 步重新发现能力。

shortcut 名称只能来自本 Skill 的 Shortcut 表或 lark-cli task --help;原生 resource/method 以逐级 help 为准,参数名、类型和嵌套结构以 method schema 为准。

任务搜索技巧:先区分用户是否特地指定使用搜索 skill,以及是否真的提供了查询关键字(例如任务名称、关键词、片段描述)。如果用户特地指定使用搜索 skill,或明确给出了任务查询关键字,则目标是任务时优先使用 +search。如果用户没有特地指定使用搜索 skill,且意图里没有查询关键字,只有范围条件(例如“今年以来”“已完成”“由我创建”“我关注的”),并且使用 +search+get-related-tasks / +get-my-tasks 都能达到目的时,应优先使用列表型能力,而不是搜索型能力。其中,“与我相关 / 我关注的 / 由我创建”等优先考虑 +get-related-tasks;“我负责的 / 分配给我”的列表优先考虑 +get-my-tasks。不要把时间范围词(例如“今年以来”)本身误当成 query 去走搜索。 任务搜索相关性提示+search 当前不会自动判断搜索结果与搜索发起人的相关性。如果用户明确要求搜索“与我相关”的任务,必须先识别具体关系,获取当前用户的 open_id,并显式传入对应的 --assignee(负责人)、--creator(创建人)或 --follower(关注人)过滤条件;不能只依赖 query 期待自动返回与当前用户相关的任务。 任务清单搜索技巧:任务清单也遵循同样的判断逻辑。先区分用户是否特地指定使用搜索 skill,以及是否真的提供了清单查询关键字(例如清单名称、关键词、片段描述)。如果用户特地指定使用搜索 skill,或明确给出了清单查询关键字,则优先使用 +tasklist-search。如果用户没有特地指定使用搜索 skill,且意图里没有查询关键字,只有范围条件(例如“由我创建的任务清单”“今年以来创建的清单”),并且使用搜索或原生列取清单都能达到目的时,应优先使用原生 tasklists.list 接口列取清单(先 schema task.tasklists.list,再 lark-cli task tasklists list --as user ...),再按 creatorcreated_at 等字段做本地筛选和分页控制。 意图区分补充:像“搜索飞书中今年以来我关注的任务”这类表达,虽然字面带有“搜索”,但如果没有真正的查询关键字,且本质是在限定“与我相关 + 时间范围”,则应优先走 +get-related-tasks;像“搜索飞书中由我创建的任务清单”这类表达,如果没有清单关键字,且本质是在限定“清单范围 + 创建者”,则应优先走原生 tasklists.list 后筛选,而不是直接走搜索型 shortcut。 用户身份识别:在用户身份(user identity)场景下,如果用户提到了“我”(例如“分配给我”、“由我创建”),请默认获取当前登录用户的 open_id 作为对应的参数值(用 lark-cli auth status 获取,从输出 JSON 的 identities.user.openId 提取,见 ../lark-shared/SKILL.md 速查表)。 术语理解 — 待办 disambiguation(必读)

  • 用户提到「待办 / todo / 任务」时,先判断归属,不要默认走本 skill。
  • 走妙记(禁止本 skill):上下文含妙记/会议纪要/minute_token//minutes/ URL 时,直接用 lark-cli minutes +todo 系列命令,不要调任何 task 命令去“找清单再放任务”。
  • 走本 skill:任务清单、分配给我、截止日期/提醒、子任务;applink 含 client/todo/task?guid=;或明确说“飞书任务/任务中心/我的任务清单”。 友好输出:在输出任务(或清单)的执行结果给用户时,建议同时提取并输出命令返回结果中的 url 字段(任务链接),以便用户可以直接点击跳转查看详情。

Read the full file on GitHub · 187 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. 8d ago First seen · 187 lines · 178 tokens per session scan A acc0f229e335

Subscribe to this mod's changes

lark-task is a skill published in the GitHub repository Pinvou/pinvou-agent (1,712 stars, last pushed today), licensed MIT. It adds 178 tokens to every session and 3,724 once invoked, about $0.0009 per session on Opus 5. A static security scan graded it A with 0 findings. It is 88% identical to lark-task, differing in 19 lines, and is treated as a copy.

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