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BD dingtalk-docs

管理钉钉云文档中的文档、文件夹和内容。当用户想要创建文档、搜索文档、读取或写入文档内容、创建文件夹整理文档时使用。也适用于用户提到云文档、在线文档、钉钉文档、钉文档等关键词的场景。不要在用户需要操作多维表、管理日程、发消息或处理审批流时触发。

ClawHub Agent Skills author: chan v1.0.0 13 files body ≈ 1 021 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process D 46/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureSoftware 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
88/100
safety, quality, tests
Safety 60%
98
Quality 40%
73
Run on models
none yet
Process rating
D
46/100
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
This is a copy of a skill from another catalog; the rating counts the canonical one: dingtalk-docs (ClawHub)

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 references/api-reference.md:21
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    "dentryUuid": "DnRL…Ye1",
    quoted
  • low Secrets in code secret-high-entropy-token references/api-reference.md:56
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    "rootDentryUuid": "DnRL…Ye1"
    quoted

Files scanned: 11. 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 46/100

  • 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
  • 30Running it twice. 15 mutating operations with no state check
  • 40Consistency. Frontmatter name (dingtalk-docs) differs from the folder (dingtalk-docs-0-3-1)
  • 100Tools and files. No external tools needed
  • 100Steps. 34 steps
  • 100Execution cost. Instruction body is 1021 tokens

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
  • -34 of 4 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +3Description length 121: enough signal without eating the budget
  • +4Structure: 10 headings
  • +3Step-by-step instructions: 34 items
  • +4Has examples (1 code blocks)
  • +4Reference files are cited in the instructions (2 of 2)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 73.

External checks

ClawHub: clean
This skill is a coherent DingTalk Docs integration, but users should notice that it uses a token-bearing DingTalk MCP URL and can read, create, and overwrite cloud documents when directed.
LLM: benign (medium) · VirusTotal: · 29 May 2026