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BD feishu-smart-doc-writer

Feishu/Lark Smart Document Writer - 飞书智能文档写入器. Core Features / 核心功能： 1. Smart Chunk Writing / 智能分块写入 - Solve API limit blank doc issues / 解决长文档API限制导致的空白问题 2. Auto Ownership Transfer / 自动转移所有权 - Transfer to user after creation / 创建文档后自动转移给用户 3. Auto Index Management / 自动索引管理 - Update local index with search support / 自动更新本地文档索引，支持搜索 4. First-time Guide / 首次使用引导 - Auto guide for OpenID config / 自动引导配置OpenID

modbender/skill-library-mcp Agent Skills author: modbender MIT 12 files body ≈ 2 026 tokens Open the sourcegithub.com↗ analyzed 3 d ago

Feishu/Lark Smart Document Writer - 飞书智能文档写入器.

As a process D 44/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

GeneratorWordAI and agentsWriting and documentstype 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
88/100
safety, quality, tests
Safety 60%
100
Quality 40%
70
Run on models
none yet
Process rating
D
44/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
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.
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: 12. 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")

Process rating: all ten parameters 44/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Failures and branches. Linear process with no failure handling
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 60Tools and files. Uses tools (web) that frontmatter does not declare
  • 85Steps. 49 steps, 1 vague phrases
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2026 tokens
  • 100Running it twice. Mutating operations check current state

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
  • -226 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +3Description length 410: enough signal without eating the budget
  • +4Structure: 35 headings
  • +3Step-by-step instructions: 49 items
  • +4Has examples (15 code blocks)

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