lark-sheets

lark-sheets is a skill for Claude Code, Codex from seaworld008/Commonly-used-high-value-skills. It costs 56 tokens per session (11,295 once invoked), scanned A, a copy of lark-sheets, MIT.

A skill for working with Lark spreadsheets, including their sheets, cells, formulas, formatting, charts, filters, and pivot tables.

In plain words
What is it for?
Use it to create and edit spreadsheets, enter or calculate data, reshape sheets, build summaries and charts, and create financial models.
Why use it?
It supports careful spreadsheet changes while preserving existing data and checking that online edits actually took effect.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: positional $N argument.

Needs its repository: it reads a path above its own folder, which exists only inside the repository. The line is **审批协议**:先 `--dry-run` 预览、向用户展示将执行的操作与影响范围,**获得用户明确同意后**再在原命令追加 `--yes` 执行。未经用户同意不得带 `--yes`,也不得在 exit 10 后静默补 `--yes` 重试——那等于禁用门禁。完整协议见 [`../lark-shared/SKILL..

Good fit Use it to create and edit spreadsheets, enter or calculate data, reshape sheets, build summaries and charts, and create financial models.

Compare 6 skills from other repositories ↓
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/seaworld008/Commonly-used-high-value-skills
agentmods
npx agentmods add skills/seaworld008/commonly-used-high-value-skills/lark-sheets

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-sheets

README.md
[![agentmods](https://agentmods.dev/badge/skills/seaworld008/commonly-used-high-value-skills/lark-sheets/github.svg)](https://agentmods.dev/skills/seaworld008/commonly-used-high-value-skills/lark-sheets)
Your own site
<a href="https://agentmods.dev/skills/seaworld008/commonly-used-high-value-skills/lark-sheets"><img src="https://agentmods.dev/badge/skills/seaworld008/commonly-used-high-value-skills/lark-sheets/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-sheets

Your own site · 80×15
<a href="https://agentmods.dev/skills/seaworld008/commonly-used-high-value-skills/lark-sheets"><img src="https://agentmods.dev/badge/skills/seaworld008/commonly-used-high-value-skills/lark-sheets.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 56 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 11,295 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 $0.00056 $0.11295
Opus 5 $0.00028 $0.05647
Sonnet 5 $0.00011 $0.02259
Haiku 4.5 $0.00006 $0.01129

Measured 5d ago against content hash f50fbd4f88c8, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

lark-sheets 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 5d ago.

The scan reads SKILL.md. This mod also ships 6 executable files (scripts/lark_detect_subtables.py, scripts/lark_inspect_workbook.py, scripts/lark_profile_table.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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-sheets — 178 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.

openclaw-skills/lark-sheets/SKILL.md · 275 lines

How it starts

The opening of the file, as written. The whole thing — 275 lines — stays where its author put it; the contents beside it link to each section on GitHub.

sheets

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。

术语约定

同一对象的交替说法,按此映射解析用户口语:工作表(sheet)= 子表 / tab / 标签页(sheet_id 是稳定标识);电子表格(spreadsheet)= 工作簿 / 表格(顶层容器,由 --url--spreadsheet-token 定位);reference_id = 表内对象的稳定标识,即各对象主键 flag 接受的值(与 --image-uri 图片上传句柄不是一回事)。

每类对象用各自的主键 flag 定位(命名不统一,按此表对照,不要凭直觉拼):

对象 主键 flag 对象 主键 flag
工作表 sheet --sheet-id 条件格式规则 --rule-id
图表 chart --chart-id 筛选视图 --view-id
透视表 pivot --pivot-table-id 迷你图(按组) --group-id
浮动图片 --float-image-id

飞书表格编辑准则(动手前必守,所有编辑类任务一律生效)

下列准则横切所有任务,动手前先过一遍——被索引直接路由进某个工具参考也一律生效;展开与边界见括注的 reference。

  1. 最小改动:除任务要改的单元格 / 列外,原表其它单元格、行列结构、Sheet 名、合并区、格式 1:1 保持;中间结果放原数据右侧或新建空白 Sheet,禁止删 / 改名 / 隐藏 / 移动已存在 Sheet;改写类任务精确圈定行列,不该转的原值 1:1 保留。
  2. 真实写回 + 回读校验:交付必须是对在线表格的真实写入,写完用 +csv-get / +cells-get / +<对象>-list 回读确认实际生效——写操作返回 ok 只代表请求被接受、不代表结果符合预期;写公式后查错误码、筛选 / 排序后核对前几行、删除 / 清空后确认已空。禁止只在文本里声称"已完成"。
  3. 读全再写:批量填充 / 补齐 / 修正类任务先确认真实数据末行再写,只探前 N 行会漏写表尾(确定末行流程见 lark-sheets-read-data)。
  4. 公式优先于硬编码:能用公式表达的计算(总计 / 占比 / 提取 / 查找)一律写公式而非静态值——凡可由表内其它单元格推导的派生值默认用公式,即使用户没说"联动";写公式前先读 lark-sheets-formula-translation公式落表后收尾必跑 +formula-verify 直到 status='success'
  5. 续写 / 扩展继承样式:续写、补齐、复制区块、新增行列时禁止只读值只写值,必须连带 cell_styles + border_styles + 合并 + 行高一起继承(清单见 lark-sheets-write-cells,四边框最易漏)。
  6. 多步写入分流:美化收尾(样式 / 合并 / 行高列宽 / 冻结的任意组合)→ 一次 +styles-put 声明式规格交付(见 lark-sheets-styles-put);同一个写操作打多个区域 → 用该命令自身的复数形态(--ranges / map 入参);只有跨类型、有顺序依赖的操作链(如插列 → 写表头 → 回填数据)才用 +batch-update(high-risk-write:按下方审批协议先获用户同意再带 --yes;fail-fast 不回滚,语义见 lark-sheets-batch-update)。
  7. 分组汇总用透视表:"按 X 统计 Y / 分组汇总 / 各类数量金额"用 +pivot-{create|update|delete},禁止用 SUMIF / 本地脚本拼一张假透视表。
  8. 拆成可验证 checklist:落地前把指令拆成所有"独立可验证子要点",逐点 assert 全过才交付(多维排序每维一点、多目标每目标一点、范围类核起 / 末 / 边界);只做第一个要点属违规。
  9. 全量处理前置断言条数:翻译 / 打标 / 批量公式落地等逐条任务,先把预期条数硬编码再 assert actual == expected,禁止输出"已完成前 N 条,剩余继续"的半成品。
  10. 缺失值不编造:补齐 / 扩展 / 按原表格式续填时,查不到或无法确定的值一律留空 + 备注注明("暂未发布 / 未知 / 待核实"),禁止用推算值 / 估算值 / 凭空数据充数;原表若已示范缺失值写法(空值 + 备注),照抄该约定。宁可留空标注,不填不可靠的数。

Read the full file on GitHub · 275 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. 5d ago Changed · +1 lines · -201 tokens per session f50fbd4f88c8
  2. 9d ago First seen · 274 lines · 257 tokens per session scan A a68dd63e8864

Subscribe to this mod's changes

lark-sheets is a skill published in the GitHub repository seaworld008/Commonly-used-high-value-skills (70 stars, last pushed 5d ago), licensed MIT. It adds 56 tokens to every session and 11,295 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 88% identical to lark-sheets, differing in 178 lines, and is treated as a copy.

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