SKILLEMALL.ai

CD refactor

基于 Martin Fowler 方法论的系统化代码重构 skill。适用于用户请求重构代码、改进代码结构、减少技术债、清理旧代码、消除 code smell 或提升可维护性时。这个 skill 采用分阶段、带研究与计划的安全增量实施方式。

luongnv89/claude-howto Agent Skills author: luongnv89 MIT 4 files body ≈ 1 465 tokens Open the sourcegithub.com↗ analyzed 2 d ago

基于 Martin Fowler 方法论的系统化代码重构 skill。适用于用户请求重构代码、改进代码结构、减少技术债、清理旧代码、消除 code smell 或提升可维护性时。这个 skill 采用分阶段、带研究与计划的安全增量实施方式。

As a process D 38/100 · Unfinished process — References files that are not bundled: scripts/analyze-complexity.py, scripts/detect-smells.py

ProcedureSoftware developmenttype and topics are labelled automatically from the skill text
JSON
Technical rating
C
85/100
safety, quality, tests
Safety 60%
100
Quality 40%
62
Run on models
none yet
Process rating
D
38/100
Unfinished process
References files that are not bundled: scripts/analyze-complexity.py, scripts/detect-smells.py
Tools and files w 18
0
Result and completion w 14
0
Inputs and preconditions w 11
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.
  2. The text references files that are not there: add them or drop the references.
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: 4. 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")
  • warning missing-ref reference to a missing file: scripts/analyze-complexity.py
  • warning missing-ref reference to a missing file: scripts/detect-smells.py

Process rating: all ten parameters 38/100

Will not run. References files that are not bundled: scripts/analyze-complexity.py, scripts/detect-smells.py
  • 0Tools and files. 2 referenced file(s) missing: scripts/analyze-complexity.py, scripts/detect-smells.py
  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 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
  • 50Failures and branches. 0 branches, has a failure section
  • 100Steps. 139 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1465 tokens
  • low 13 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
  • -5TODO / placeholder text left in the skill
  • +1No license
  • +2Single-language instructions
  • +3Description length 120: enough signal without eating the budget
  • +4Structure: 44 headings
  • +3Step-by-step instructions: 139 items
  • +4Has examples (9 code blocks)
  • +4Reference files are cited in the instructions (2 of 2)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 62.