aws-samples/sample-lark-mcp-on-agentcore is a hosted remote MCP service that lets AI agents use Feishu/Lark through lark-cli's tools and multi-step workflow skills. Teams can deploy one centrally managed service while individual members authorize their own Feishu identities, with the service running on AWS Bedrock AgentCore. The catalogue add-ons help agents perform Lark operations through this service.
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 aws-samples/sample-lark-mcp-on-agentcore --skill lark-sheetsgit clone --depth 1 https://github.com/aws-samples/sample-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/aws-samples/sample-lark-mcp-on-agentcore/lark-sheets)<a href="https://agentmods.dev/skills/aws-samples/sample-lark-mcp-on-agentcore/lark-sheets"><img src="https://agentmods.dev/badge/skills/aws-samples/sample-lark-mcp-on-agentcore/lark-sheets.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00258 | $0.13859 |
| Opus 5 | $0.00129 | $0.06929 |
| Sonnet 5 | $0.00052 | $0.02772 |
| Haiku 4.5 | $0.00026 | $0.01386 |
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 4d 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.
The source is not reproduced here
Licensed MIT-0
The repository is licensed MIT-0, which this catalogue does not treat as permission to reproduce the file. Read it at the source.
What ships with it
21 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 20 KB
- references/lark-sheets-changeset.md 5.0 KB
- references/lark-sheets-chart.md 45 KB
- references/lark-sheets-conditional-format.md 14 KB
- references/lark-sheets-filter-view.md 7.9 KB
- references/lark-sheets-filter.md 7.7 KB
- references/lark-sheets-float-image.md 11 KB
- references/lark-sheets-formula-translation.md 19 KB
- references/lark-sheets-formula-verify.md 6.7 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 26 KB
- references/lark-sheets-search-replace.md 5.3 KB
- references/lark-sheets-sheet-structure.md 14 KB
- references/lark-sheets-sparkline.md 7.9 KB
- references/lark-sheets-styles-put.md 7.8 KB
- references/lark-sheets-visual-standards.md 19 KB
- references/lark-sheets-workbook.md 26 KB
- references/lark-sheets-write-cells.md 60 KB
- scripts/sheets_df.py 2.0 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.
- 4d ago Changed · +10 lines 65a7a1d42445
- 8d ago First seen · 211 lines · 258 tokens per session scan A 72ed195b8594
lark-sheets is a skill published in the GitHub repository aws-samples/sample-lark-mcp-on-agentcore (11 stars, last pushed today), licensed MIT-0. It adds 258 tokens to every session and 13,859 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 2026-08-30.
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