SKILLEMALL.ai

BC email-163

163邮箱助手专业版面向企业用户与高效能个人用户,在免费版基础能力之上扩展批量操作、 高级搜索过滤、定时任务调度、邮件归档审计、模板化发送与多账户管理等企业级特性。核心能力: - 批量邮件发送与群发通知(支持变量替换与模板) - 高级搜索:多维度组合过滤、正则匹配、结果导出 - 邮件归档与审计日志,满足合规要求 - 定时任务调度,支持周期性邮件处理 - 多账户统一管理,租户隔离配置 - 邮件模板系统...

ClawHub Hermes author: 天轰穿 v1.0.0 MIT-0 2 files body ≈ 2 899 tokens Open the sourceclawhub.ai analyzed 28 h ago

163邮箱助手专业版面向企业用户与高效能个人用户,在免费版基础能力之上扩展批量操作、 高级搜索过滤、定时任务调度、邮件归档审计、模板化发送与多账户管理等企业级特性。核心能力: - 批量邮件发送与群发通知(支持变量替换与模板) - 高级搜索:多维度组合过滤、正则匹配、结果导出 - 邮件归档与审计日志,满足合规要求 -…

As a process C 58/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

IntegrationAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
B
86/100
safety, quality, tests
Safety 60%
100
Quality 40%
64
Run on models
none yet
Process rating
C
58/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
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.
  2. For Hermes the description must be one sentence under 60 characters; move the conditions to a "When to Use" section.
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: 0. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-long-hermes description is 204 chars; the Hermes authoring standard requires ≤ 60 (one sentence, ending with a period)
  • warning description-no-when neither description nor a "## When to Use" section says when to use the skill
  • note frontmatter-key unknown frontmatter key "slug"
  • note frontmatter-key unknown frontmatter key "displayName"
  • note frontmatter-key unknown frontmatter key "summary"
  • note frontmatter-key unknown frontmatter key "summary_zh"
  • note frontmatter-key unknown frontmatter key "edition"
  • note frontmatter-key unknown frontmatter key "tools"
  • note frontmatter-key unknown frontmatter key "homepage"

Process rating: all ten parameters 58/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 50Failures and branches. 0 branches, has a failure section
  • 100Tools and files. Tools declared in frontmatter
  • 100Steps. 99 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2899 tokens
  • 100Running it twice. No mutating operations
  • low 17 top-level sections: this looks like several domains in one skill

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
  • +2Single-language instructions
  • +3Description length 204: enough signal without eating the budget
  • +4Structure: 56 headings
  • +3Step-by-step instructions: 99 items
  • +4Has examples (17 code blocks)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 64.

External checks

ClawHub: suspicious
This email automation skill is broadly aligned with its purpose, but it needs review because it can send, delete, archive, and schedule mailbox actions with weak guardrails around secrets and destructive operations.
LLM: suspicious (high) · 2 Aug 2026