SKILLEMALL.ai
GitHub ↗ RU Sign in

BD onboarding

OpenClaw Diary 日记系统安装向导。引导用户完成日记系统的初始化设置，包括人设选择、用户身份建立、存储配置和授权管理。 **立即触发当**：用户说「setup my journal」「初始化日记」「配置日记系统」「journal setup」「开始设置日记」。 **主动触发当**：用户首次尝试使用 diary skill 但配置文件不存在时，主动提示「看起来你还没有初始化日记系统，要现在设置吗？」 完成 5 个阶段的设置：人设选择、用户身份建立（可选）、存储配置、授权收集、生成配置文件。 **核心特性**： - 智能授权合并：如果导入和存储使用相同平台（如飞书），只需授权一次 - 渐进式引导：5 个阶段，可选步骤可跳过 - 人设一致性：INTJ/ENFP 风格贯穿全程 - 纯文本交互：完全适配 Telegram/WhatsApp/Discord 等消息平台

LeoYeAI/openclaw-master-skills Agent Skills author: LeoYeAI MIT 6 files body ≈ 2 759 tokens Open the sourcegithub.com↗ analyzed 2 d ago

OpenClaw Diary 日记系统安装向导。引导用户完成日记系统的初始化设置，包括人设选择、用户身份建立、存储配置和授权管理。 立即触发当：用户说「setup my journal」「初始化日记」「配置日记系统」「journal setup」「开始设置日记」。 主动触发当：用户首次尝试使用 diary…

As a process D 41/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

IntegrationDiscordTelegramWhatsAppNotionPersonal productivitytype 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%
72
Run on models
none yet
Process rating
D
41/100
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

  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 importers/feishu_importer.md:59
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    使用 `mcp_…ent` 读取文档内容。
    quoted
  • low Secrets in code secret-high-entropy-token importers/feishu_importer.md:327
    High-entropy token-like string (may be an id, hash or a credential)
    content_result = mcp_…ent({

Files scanned: 6. 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 41/100

  • 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
  • 40Consistency. Frontmatter name (onboarding) differs from the folder (openclaw-diary-setup)
  • 60Tools and files. Uses tools (bash) that frontmatter does not declare
  • 100Steps. 59 steps
  • 100Execution cost. Instruction body is 2759 tokens
  • 100Running it twice. No mutating operations

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
  • +1No license
  • +2Single-language instructions
  • +3Description length 392: enough signal without eating the budget
  • +4Structure: 46 headings
  • +3Step-by-step instructions: 59 items
  • +4Has examples (52 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 72.