SKILLEMALL.ai

CC build-product-and-tool-interfaces

为浏览器内需要持续操作对象、推进多步状态并产生可检查结果的产品与专业工具设计、实现、重构或诊断完整界面。用于运营与审核工作台、任务流业务产品、创作与编辑工具、分析与管理工具、资产或文件处理器、监控处置台及其目标环境工作流;核心覆盖对象模型、状态机、真实输入输出、专业引擎、错误恢复、信息密度与任务连续性。不用于主要供阅读的内容网站、只展示组件与 token 的设计系统样张、单张数据图或以理解模型为目的的交互演示。

EverMind-AI/Raven Agent Skills author: EverMind-AI Apache-2.0 2 files body ≈ 2 406 tokens Open the sourcegithub.com↗ analyzed 5 d ago

为浏览器内需要持续操作对象、推进多步状态并产生可检查结果的产品与专业工具设计、实现、重构或诊断完整界面。用于运营与审核工作台、任务流业务产品、创作与编辑工具、分析与管理工具、资产或文件处理器、监控处置台及其目标环境工作流;核心覆盖对象模型、状态机、真实输入输出、专业引擎、错误恢复、信息密度与任务连续性。不用于主要供阅…

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

GeneratorSoftware developmentDesignData and analyticstype and topics are labelled automatically from the skill text
JSON
Technical rating
C
89/100
safety, quality, tests
Safety 60%
100
Quality 40%
72
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: 2. 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 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. 12 mutating operations with no state check
  • 100Tools and files. No external tools needed
  • 100Steps. 67 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2406 tokens
  • low 11 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
  • +4No input/output examples
  • +1No license
  • +2Single-language instructions
  • +3Description length 207: enough signal without eating the budget
  • +4Structure: 12 headings
  • +3Step-by-step instructions: 67 items
  • +4Reference files are cited in the instructions (1 of 1)

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