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BD feishu-lark

Comprehensive Feishu/Lark integration skill for OpenClaw. Covers messaging, group management, Bitable (multi-dimensional tables), documents, calendar, video conferencing (vc.v1), meeting minutes (minutes.v1), task management, approval workflows, contacts, cloud drive, wiki/knowledge base. Requires the official Lark MCP server to be configured and running.

ClawHub Agent Skills author: lucas v1.0.1 2 files body ≈ 10 581 tokens Open the sourceclawhub.ai↗ analyzed 4 d ago

As a process D 48/100 · Unfinished process — weak spots: result and completion, when it triggers, consistency

IntegrationAI and agentsPersonal productivityInfrastructuretype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
B
84/100
safety, quality, tests
Safety 60%
100
Quality 40%
59
Run on models
none yet
Process rating
D
48/100
Unfinished process
Result and completion w 14
0
Progress reporting w 2
0
When it triggers w 12
20
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
  2. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
For the model run — optional
  • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
  • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

Guard findings · 0

✓ No critical or high findings

Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • warning body-long SKILL.md body ≈ 10581 tokens (recommended < 5000); move details to references/
  • note frontmatter-key unknown frontmatter key "homepage"
  • note frontmatter-key unknown frontmatter key "source"
  • note frontmatter-key unknown frontmatter key "requires"

Process rating: all ten parameters 48/100

  • 0Result and completion. Does not say what the result is
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 71 mutating operations with no state check
  • 40Consistency. Frontmatter name (feishu-lark) differs from the folder (lark-mcp)
  • 40Execution cost. Instruction body is 10581 tokens: crowds the task out of the window
  • 50Failures and branches. 0 branches, has a failure section
  • 60Tools and files. Uses tools (bash, node) that frontmatter does not declare
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 100Steps. 16 steps
  • low 17 top-level sections: this looks like several domains in one skill

Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

Quality signals

  • +5Description has no quoted example phrases that should trigger the skill
  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +3Description length 357: enough signal without eating the budget
  • +4Structure: 170 headings
  • +3Step-by-step instructions: 16 items
  • +4Has examples (147 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 59.

External checks

ClawHub: suspicious
This appears to be a legitimate Lark integration, but it gives an agent broad access to sensitive workplace data and destructive actions without enough safety guidance.
LLM: suspicious (high) · VirusTotal: · 29 May 2026