SKILLEMALL.ai

AB product-4a-architecture

基于产品需求,先生成一张完整的 4A 分层主图,再采用苏格拉底追问法逐步澄清业务架构、应用架构、数据架构与技术架构,最终输出 1 张 4A 总览分层主图、4 张分层架构图以及相关文档,并结合外部权威资料校验增强质量。所有架构图默认输出为可导入 draw.io 的 XML 代码块。Use when the user asks to 梳理产品架构、业务架构、应用架构、数据架构、技术架构,或希望按“埃森哲 4A / 四横五纵”思路,从 0 到 1 访谈式产出结构化架构蓝图,并希望以 draw.io XML 形式沉淀完整的分层架构图和配套文档。

ClawHub Agent Skills author: doertiger v1.0.0 MIT-0 8 files body ≈ 1 162 tokens Open the sourceclawhub.ai analyzed 2 d ago

As a process B 68/100 · Nearly there — weak spots: inputs and preconditions, consistency, progress reporting

ProcedureInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
84
Run on models
none yet
Process rating
B
68/100
Nearly there
Inputs and preconditions w 11
0
Progress reporting w 2
0
Consistency w 8
40
the three weakest of ten parameters · all ten

How to improve

    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: 8. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 68/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 40Consistency. Frontmatter name (product-4a-architecture) differs from the folder (4architecher)
    • 50Failures and branches. 0 branches, has a failure section
    • 60Result and completion. Output format stated, no completion criterion
    • 70When it triggers. States when to use, but not when not to
    • 100Tools and files. No external tools needed
    • 100Steps. 117 steps
    • 100Execution cost. Instruction body is 1162 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 271: enough signal without eating the budget
    • +4Structure: 17 headings
    • +3Step-by-step instructions: 117 items
    • +4Reference files are cited in the instructions (5 of 5)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.

    External checks

    ClawHub: clean
    This is an instruction-only architecture-diagramming skill that asks for product context and may validate claims against public sources, with no executable code or hidden data handling found.
    LLM: benign (high) · VirusTotal: · 29 May 2026