SKILLEMALL.ai
GitHub ↗ RU Sign in

BD dingtalk-dws

钉钉 CLI 技能 / 钉钉 dingding / 钉钉 dws skill / 管理钉钉全部产品：AI表格、日历、通讯录、群聊机器人、待办、审批、考勤、日报周报、DING消息、工作台。Manage DingTalk products (AI forms, calendar, contacts, bots, todos, approvals, attendance, reports, DING, workbench)

ClawHub Agent Skills author: 花渡 v1.0.12 MIT-0 32 files body ≈ 363 tokens Open the sourceclawhub.ai↗ analyzed 4 d ago

As a process D 36/100 · Unfinished process — weak spots: steps, result and completion, when it triggers

IntegrationPersonal productivityData and analyticsInfrastructuretype 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
82/100
safety, quality, tests
Safety 60%
99
Quality 40%
57
Run on models
none yet
Process rating
D
36/100
Unfinished process
Steps w 15
0
Result and completion w 14
0
Inputs and preconditions w 11
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 · 1

✓ No critical or high findings

Medium and low: 1
  • low Secrets in code secret-high-entropy-token scripts/upload_attachment.py:166
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    print('  python upload_attachment.py G1DK…YAn ./report.pdf')
    quoted

Files scanned: 32. 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 36/100

  • 0Steps. Prose only: no discrete steps
  • 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. 4 mutating operations with no state check
  • 100Tools and files. No external tools needed
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 363 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)
  • +3No numbered steps or checklist
  • +3Output format is not stated: the model decides each time
  • -5TODO / placeholder text left in the skill
  • -45 reference files, but SKILL.md never points to them: the model will not open them
  • -313 of 13 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +3Description length 210: enough signal without eating the budget
  • +4Structure: 5 headings
  • +4Has examples (2 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 57.

External checks

ClawHub: suspicious
This DingTalk skill is not clearly malicious, but it needs Review because it can read and change sensitive workplace data with broad routing and inconsistent confirmation guidance.
LLM: suspicious (high) · VirusTotal: · 29 May 2026