SKILLEMALL.ai

AD code-analyzer

深度代码分析工具。分析代码架构、执行流程、数据流、业务规则、外部依赖、数据模型,支持 DDD 模式识别(聚合根、实体、值对象、领域服务、仓储、领域事件、限界上下文)。使用场景:新代码库熟悉、架构文档生成、代码审查准备、技术债务评估、知识传承、DDD 模式识别。支持 Python、JavaScript、TypeScript、Rust、Java、Go 等 20+ 语言。

ClawHub Agent Skills author: jerry-guo-mys v1.0.0 5 files body ≈ 627 tokens Open the sourceclawhub.ai analyzed 2 d ago

深度代码分析工具。分析代码架构、执行流程、数据流、业务规则、外部依赖、数据模型,支持 DDD 模式识别(聚合根、实体、值对象、领域服务、仓储、领域事件、限界上下文)。使用场景:新代码库熟悉、架构文档生成、代码审查准备、技术债务评估、知识传承、DDD 模式识别。支持…

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

AnalyzerSoftware developmenttype and topics are labelled automatically from the skill text
JSON
Technical rating
A
91/100
safety, quality, tests
Safety 60%
100
Quality 40%
77
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: 5. 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. 22 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 627 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
  • -217 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +3Description length 184: enough signal without eating the budget
  • +4Structure: 17 headings
  • +3Step-by-step instructions: 22 items
  • +4Has examples (8 code blocks)
  • +4Reference files are cited in the instructions (1 of 1)
  • +3All 2 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 77.

External checks

ClawHub: suspicious
The skill is a local code-report generator, but one analyzer can follow project symlinks outside the selected folder and the DDD report path can produce a report without actually analyzing files.
LLM: suspicious (high) · VirusTotal: · 11 Sept 2026