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 ddpie/lark-mcp-on-agentcore --skill lark-sheetsgit clone --depth 1 https://github.com/ddpie/lark-mcp-on-agentcoreWrote 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/ddpie/lark-mcp-on-agentcore/lark-sheets)<a href="https://agentmods.dev/skills/ddpie/lark-mcp-on-agentcore/lark-sheets"><img src="https://agentmods.dev/badge/skills/ddpie/lark-mcp-on-agentcore/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.
<a href="https://agentmods.dev/skills/ddpie/lark-mcp-on-agentcore/lark-sheets"><img src="https://agentmods.dev/badge/skills/ddpie/lark-mcp-on-agentcore/lark-sheets.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 | $0.00260 | $0.10890 |
| Opus 5 | $0.00130 | $0.05445 |
| Sonnet 5 | $0.00052 | $0.02178 |
| Haiku 4.5 | $0.00026 | $0.01089 |
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 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.
How it starts
The opening of the file, as written. The whole thing — 214 lines — stays where its author put it; the contents beside it link to each section on GitHub.
sheets
（认证由 MCP server 自动处理。）
术语约定
下列词在本 skill 各文档中可能交替出现，但指同一对象；解析用户口语时按此映射，不要当成不同概念：
| 标准用语 | 同义 / 口语（均指同一对象） | 说明 |
|---|---|---|
| 工作表（sheet） | 子表、tab、标签页 | spreadsheet 内的单张表；sheet_id 是其稳定标识 |
| 电子表格（spreadsheet） | 工作簿、表格 | 顶层容器；由 url 或 spreadsheet_token 定位 |
| reference_id | id | 表内对象的稳定标识，即各对象主键参数接受的值（见下表）。⚠️ 与 float-image 的 image_uri（图片上传句柄）不是一回事，后者不属于 reference_id |
每类对象用各自的主键参数定位（命名不统一，按此表对照，不要凭直觉拼）：
| 对象 | 主键参数 | 对象 | 主键参数 |
|---|---|---|---|
| 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）
下列准则横切所有飞书表格任务，动手前先过一遍——即使你是被索引直接路由进某个工具参考也一律生效。每条只给一句话纲要，展开与边界见括注的 reference。
- 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。
- 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
lark_sheets_csv_get/lark_sheets_cells_get/lark_sheets_<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"。 - 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_get_skill(domain="sheets", section="read-data")）。 - 公式优先于硬编码：能用公式表达的计算（总计 / 占比 / 增长率 / 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就用公式，即使用户没说"联动 / 自动更新"；写任何飞书公式前先读
lark_get_skill(domain="sheets", section="formula-translation")，而且只要公式真实写入表格，收尾默认就要继续跑lark_get_skill(domain="sheets", section="formula-verify")的lark_sheets_formula_verify，直到status='success'。 - 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_get_skill(domain="sheets", section="write-cells")，四边框最易漏）。 - 多步写入合并
lark_sheets_batch_update：多个连续写入、或同一工具对多区域重复调用，合并为单次原子lark_sheets_batch_update（语义见lark_get_skill(domain="sheets", section="batch-update")）。 - 分组汇总用透视表："按 X 统计 Y / 分组汇总 / 各类数量金额"用
lark_sheets_pivot_{create|update|delete}，禁止用 SUMIF / 本地脚本拼一张假透视表。 - 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。 - 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。
What ships with it
20 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.
- references/lark-sheets-batch-update.md 16 KB
- references/lark-sheets-changeset.md 5.0 KB
- references/lark-sheets-chart.md 20 KB
- references/lark-sheets-conditional-format.md 11 KB
- references/lark-sheets-filter-view.md 7.9 KB
- references/lark-sheets-filter.md 7.4 KB
- references/lark-sheets-float-image.md 11 KB
- references/lark-sheets-formula-translation.md 14 KB
- references/lark-sheets-formula-verify.md 6.1 KB
- references/lark-sheets-history.md 5.3 KB
- references/lark-sheets-pivot-table.md 15 KB
- references/lark-sheets-range-operations.md 21 KB
- references/lark-sheets-read-data.md 24 KB
- references/lark-sheets-search-replace.md 5.3 KB
- references/lark-sheets-sheet-structure.md 12 KB
- references/lark-sheets-sparkline.md 7.7 KB
- references/lark-sheets-visual-standards.md 18 KB
- references/lark-sheets-workbook.md 26 KB
- references/lark-sheets-write-cells.md 56 KB
- scripts/sheets_df.py 1.1 KB runs code
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.
- 8d ago First seen · 214 lines · 260 tokens per session scan A b9196111d739
lark-sheets is a skill published in the GitHub repository ddpie/lark-mcp-on-agentcore (8 stars, last pushed 11d ago), licensed MIT. It adds 260 tokens to every session and 10,890 once invoked, about $0.0013 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.
Other skills, from other repositories
lark-cli
A command-line tool for working with Lark, also called Feishu, a workplace collaboration platform. It covers services such as calendars, meetings, documents, spreadsheets, messaging, tasks, approvals, and shared files.
sheets
Read and query Lark Sheets (spreadsheets) - list sheets in a spreadsheet, read cell data. Use when user asks about a spreadsheet, wants to read data from a Lark sheet, or mentions a spreadsheet URL/ID.
feishu-doc
Fetch content from Feishu (Lark) Wiki, Docs, Sheets, and Bitable. Automatically resolves Wiki URLs to real entities and converts content to Markdown.
lark
Read and write Lark (Feishu international) docs, spreadsheets, drive files, and messages from Python. Supports both tenant access tokens (app-only, no login) and OAuth user tokens with auto-refresh. Trigger phrases: "lark sheet", "lark doc", "push to lark", "/lark".
recipe-collect-data
Collect data from Lark spreadsheet and convert to Notion database.
feishu-doc
Fetch content from Feishu (Lark) Wiki, Docs, Sheets, and Bitable. Automatically resolves Wiki URLs to real entities and converts content to Markdown.