SKILLEMALL.ai

AD facetory-build-watchfaces

连接 Facetory 内置 MCP 服务器,检查、创建、编辑、验证、保存和导出小米表盘项目,涵盖普通与息屏主题、动态数据绑定、本地图片/字体导入、图层顺序、可复用模板和导出排错。用于通过 facetory:// 资源或 facetory.* MCP 工具操作 Facetory、诊断内置服务器、续作已打开的 .facetory 项目、制作 Xiaomi/Redmi/Mi Band 表盘、修复 AOD 或导出错误,以及整理成品资源。

ClawHub Agent Skills author: 朝歌 v1.0.0 MIT-0 6 files body ≈ 443 tokens Open the sourceclawhub.ai analyzed 2 d ago

连接 Facetory 内置 MCP 服务器,检查、创建、编辑、验证、保存和导出小米表盘项目,涵盖普通与息屏主题、动态数据绑定、本地图片/字体导入、图层顺序、可复用模板和导出排错。用于通过 facetory:// 资源或 facetory.

As a process D 46/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

GeneratorAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
75
Run on models
none yet
Process rating
D
46/100
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 · 0

✓ No critical or high findings

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 46/100

  • 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
  • 60Tools and files. Uses tools (python) that frontmatter does not declare
  • 100Steps. 25 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 443 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
  • +4No input/output examples
  • +1No license
  • +2Single-language instructions
  • +3Description length 217: enough signal without eating the budget
  • +4Structure: 8 headings
  • +3Step-by-step instructions: 25 items
  • +4Reference files are cited in the instructions (2 of 2)
  • +3All 1 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.

External checks

ClawHub: clean
This skill is a coherent Facetory watchface workflow helper with disclosed local MCP editing and a narrowly scoped, permission-gated root fallback for asset import problems.
LLM: benign (high) · VirusTotal: · 5 Aug 2026