SKILLEMALL.ai
GitHub ↗ RU Sign in

BC huo15-dingtalk-agent-isolation

火一五钉钉智能体隔离 - 为每个钉钉用户创建独立 Agent 工作空间、记忆和会话历史。支持手动绑定和动态创建两种模式。触发词：钉钉Agent隔离、钉钉用户隔离、动态Agent创建、dingtalk agent isolation。

ClawHub Agent Skills author: Job Zhao v1.0.0 MIT-0 9 files · 2 scripts body ≈ 2 199 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

火一五钉钉智能体隔离 - 为每个钉钉用户创建独立 Agent 工作空间、记忆和会话历史。支持手动绑定和动态创建两种模式。触发词：钉钉Agent隔离、钉钉用户隔离、动态Agent创建、dingtalk agent isolation。

As a process C 51/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureAI and agentstype 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
84/100
safety, quality, tests
Safety 60%
100
Quality 40%
60
Run on models
none yet
Process rating
C
51/100
Has gaps
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: 9. 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 "displayName"
  • note frontmatter-key unknown frontmatter key "dependencies"
  • note frontmatter-key unknown frontmatter key "aliases"

Process rating: all ten parameters 51/100

  • 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. 4 mutating operations with no state check
  • 100Tools and files. No external tools needed
  • 100Steps. 32 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2199 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)
  • +3Description length 116: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • -41 reference files, but SKILL.md never points to them: the model will not open them
  • -31 of 3 scripts are never mentioned in SKILL.md
  • +2Single-language instructions
  • +4Structure: 31 headings
  • +3Step-by-step instructions: 32 items
  • +4Has examples (14 code blocks)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 60.

External checks

ClawHub: suspicious
The skill is mostly coherent for DingTalk agent isolation, but it asks users to patch connector code and enable automatic persistent OpenClaw config changes with weaker safeguards than the documentation claims.
LLM: suspicious (high) · 1 Aug 2026