lark-okr

lark-okr is a skill for Claude Code, Codex from aws-samples/sample-lark-mcp-on-agentcore. It costs 87 tokens per session (3,210 once invoked), scanned A, original, MIT-0.

Goal and key-result management for Feishu (Lark), a workplace collaboration platform. OKRs are objectives paired with measurable results used to track progress toward goals.

In plain words
What is it for?
Viewing or creating OKRs, editing goal cycles and key results, recording progress, managing measurable targets, and checking how goals are aligned.
Why use it?
It keeps goals, measurements, progress updates, and links between related goals in one place.

Skill for Claude CodeCodex ✓ vendor

Which agent this was written for is unclear — body not stored (licence); the path alone says nothing.

Good fit Viewing or creating OKRs, editing goal cycles and key results, recording progress, managing measurable targets, and checking how goals are aligned.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/aws-samples/sample-lark-mcp-on-agentcore/lark-okr
About the project

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.

aws-samples/sample-lark-mcp-on-agentcore · 11 stars · on GitHub

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 aws-samples/sample-lark-mcp-on-agentcore --skill lark-okr
Clone the repo
git clone --depth 1 https://github.com/aws-samples/sample-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-okr

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

Your own site · 80×15
<a href="https://agentmods.dev/skills/aws-samples/sample-lark-mcp-on-agentcore/lark-okr"><img src="https://agentmods.dev/badge/skills/aws-samples/sample-lark-mcp-on-agentcore/lark-okr.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 87 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,210 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 unknown 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 $0.00087 $0.03210
Opus 5.5 $0.00035 $0.01284
Sonnet 5.5 $0.00017 $0.00642
Haiku 4.5 $0.00009 $0.00321

Measured 1mo ago against content hash 4d1e04fe769f, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

lark-okr 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 1mo 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-okr/SKILL.md · 160 lines

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.

Read it on GitHub

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. 1mo ago Changed · +9 lines 4d1e04fe769f
  2. 1mo ago First seen · 151 lines · 87 tokens per session scan A c7d47b258a18

Subscribe to this mod's changes

lark-okr is a skill published in the GitHub repository aws-samples/sample-lark-mcp-on-agentcore (11 stars, last pushed 10d ago), licensed MIT-0. It adds 87 tokens to every session and 3,210 once invoked, about $0.0003 per session on Opus 5.5. 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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