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BC larksuite-wiki

Manage and export Lark Suite (Feishu) Wiki/Knowledge Base documents. Read, search, sync with subdocuments, and incremental export to local Markdown files.

modbender/skill-library-mcp Agent Skills author: modbender MIT 4 files body ≈ 752 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Manage and export Lark Suite (Feishu) Wiki/Knowledge Base documents.

As a process C 64/100 · Has gaps — weak spots: when it triggers, failures and branches, running it twice

ReferenceSoftware developmenttype 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
87/100
safety, quality, tests
Safety 60%
98
Quality 40%
70
Run on models
none yet
Process rating
C
64/100
Has gaps
Failures and branches w 10
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.
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 · 2

✓ No critical or high findings

Medium and low: 2
  • low Secrets in code secret-high-entropy-token larksuite-wiki.py:20
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    self.app_secret = app_secret or os.getenv("LARK_APP_SECRET") or "xtSo…Wgo"
    quoted
  • low Secrets in code secret-high-entropy-token references/api-reference.md:7
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    export LARK_APP_SECRET="xtSo…Wgo"
    quoted

Files scanned: 4. 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")
  • note frontmatter-key unknown frontmatter key "homepage"

Process rating: all ten parameters 64/100

  • 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
  • 30Running it twice. 3 mutating operations with no state check
  • 60Result and completion. Output format stated, no completion criterion
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 85Steps. 20 steps, 1 vague phrases
  • 100Tools and files. No external tools needed
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 752 tokens
  • high The skill tells the model to perform an irreversible action with no human approval

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)
  • -41 reference files, but SKILL.md never points to them: the model will not open them
  • +1No license
  • +2Single-language instructions
  • +3Description length 154: enough signal without eating the budget
  • +4Structure: 19 headings
  • +3Step-by-step instructions: 20 items
  • +3Output format is stated explicitly
  • +4Has examples (9 code blocks)

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