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BD ding-skills

钉钉操作助手。当用户提到以下任何场景时必须使用此技能：查人（查下某某、搜一下某人、找一下谁谁）、查部门、查手机号、查工号、约会议（预约会议、创建会议、安排会议、开个会）、发消息（给某人发消息、群里发个通知）、查审批（我的审批、待审批、审批状态）、发起审批、同意/拒绝审批、查日程、创建日程、查员工数、查离职、开视频会议、知识库（查知识库、创建文档、搜索文档、覆写文档）。Use when user mentions anything about DingTalk: looking up people, searching users/departments, scheduling meetings, creating conferences, sending messages, managing approvals, checking calendar events, querying employee info, knowledge base operations (list workspaces, create/search/overwrite documents), or any DingTalk-related operations.

LeoYeAI/openclaw-master-skills Agent Skills author: LeoYeAI MIT 48 files body ≈ 3 435 tokens Open the sourcegithub.com↗ analyzed 2 d ago

钉钉操作助手。当用户提到以下任何场景时必须使用此技能：查人（查下某某、搜一下某人、找一下谁谁）、查部门、查手机号、查工号、约会议（预约会议、创建会议、安排会议、开个会）、发消息（给某人发消息、群里发个通知）、查审批（我的审批、待审批、审批状态）、发起审批、同意/拒绝审批、查日程、创建日程、查员工数、查离职、开视频会议…

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

GeneratorPersonal productivityMedia and videotype 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
86/100
safety, quality, tests
Safety 60%
99
Quality 40%
66
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

How to improve

    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 · 1

    ✓ No critical or high findings

    Medium and low: 1
    • low Secrets in code secret-high-entropy-token SKILL.md:548
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      "eventId": "NXZC…z09",
      quoted

    Files scanned: 48. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • warning frontmatter-yaml SKILL.md: the frontmatter is not valid YAML (YAML parse error: Nested mappings are not allowed in compact mappings at line 2, column 14: description: 钉钉操作助手。当用户提到以下任何场景时必须使用此技能：查人（查下某某、搜一下某人、找一下谁谁）、查部门、查手机号、查工号、约会议（预… ^ ); fields were read line by line. The usual cause is a colon inside an unquoted value

    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. 9 mutating operations with no state check
    • 40Consistency. Frontmatter name (ding-skills) differs from the folder (dingtalk-skills)
    • 100Tools and files. No external tools needed
    • 100Steps. 13 steps
    • 100Execution cost. Instruction body is 3435 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
    • -5TODO / placeholder text left in the skill
    • -32 of 45 scripts are never mentioned in SKILL.md
    • +1No license
    • +2Single-language instructions
    • +3Description length 527: enough signal without eating the budget
    • +4Structure: 57 headings
    • +3Step-by-step instructions: 13 items
    • +4Has examples (92 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 66.