SKILLEMALL.ai
GitHub ↗ RU Sign in

BC desktop-autopilot

桌面自发驾驶为 AI Agent 包含基于视觉的智能 GUI 自发化能力。它不依赖固定坐标，而是通过图像识别、OCR 文本定位、智能等待元素出现来操控界面，内置工作流编排、录制回放、。Use when 需要AI模型调用、智能对话、Agent编排、LLM应用时使用。不适用于需要100%确定性的关键决策。 功能涵盖: desktop, autopilot。

ClawHub Hermes author: 天轰穿 v1.0.2 MIT-0 2 files body ≈ 3 315 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

桌面自发驾驶为 AI Agent 包含基于视觉的智能 GUI 自发化能力。它不依赖固定坐标，而是通过图像识别、OCR 文本定位、智能等待元素出现来操控界面，内置工作流编排、录制回放、。Use when 需要AI模型调用、智能对话、Agent编排、LLM应用时使用。不适用于需要100%确定性的关键决策。 功能涵盖…

As a process C 51/100 · 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
Runs in: OpenClaw Hermes Agent
JSON
Technical rating
B
86/100
safety, quality, tests
Safety 60%
100
Quality 40%
66
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. 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 frontmatter-yaml SKILL.md: the frontmatter is not valid YAML (YAML parse error: Nested mappings are not allowed in compact mappings at line 9, column 14: description: 桌面自发驾驶为 AI Agent 包含基于视觉的智能 GUI 自发化能力。它不依赖固定坐标，而是通过图像识别、OCR 文本定位、智能… ^ ); fields were read line by line. The usual cause is a colon inside an unquoted value
  • warning description-long-hermes description is 177 chars; the Hermes authoring standard requires ≤ 60 (one sentence, ending with a period)
  • 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 "tools"
  • note frontmatter-key unknown frontmatter key "pricing_tier"

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. 2 mutating operations with no state check
  • 100Tools and files. Tools declared in frontmatter
  • 100Steps. 27 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 3315 tokens
  • low 20 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 177: enough signal without eating the budget
  • +4Structure: 62 headings
  • +3Step-by-step instructions: 27 items
  • +4Has examples (34 code blocks)
  • +1License stated

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

External checks

ClawHub: suspicious
This desktop automation skill is mostly aligned with its purpose, but it needs review because it can automate destructive GUI actions and persist raw typed user data in logs.
LLM: suspicious (high) · 22 Aug 2026