SKILLEMALL.ai

AC paper-revision-sop

学术论文润色修改全流程SOP。从审稿意见解析、论文全息诊断、可视化批注生成、分级改写执行到终审自检的完整闭环。当用户需要根据期刊审稿意见修改论文、需要论文润色、需要去AI味、需要学术写作规范检查时触发。典型触发:"修改这篇论文""根据审稿意见润色""去AI味""按期刊要求压缩字数""帮我看看这篇论文能不能通过"。

ClawHub Agent Skills author: 刘文琦 v1.0.0 MIT-0 8 files body ≈ 1 063 tokens Open the sourceclawhub.ai analyzed 2 d ago

学术论文润色修改全流程SOP。从审稿意见解析、论文全息诊断、可视化批注生成、分级改写执行到终审自检的完整闭环。当用户需要根据期刊审稿意见修改论文、需要论文润色、需要去AI味、需要学术写作规范检查时触发。典型触发:"修改这篇论文""根据审稿意见润色""去AI味""按期刊要求压缩字数""帮我看看这篇论文能不能通过"。

As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureWordSoftware developmentResearchSecuritytype and topics are labelled automatically from the skill text
JSON
Technical rating
A
93/100
safety, quality, tests
Safety 60%
100
Quality 40%
82
Run on models
none yet
Process rating
C
53/100
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: 8. 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 53/100

  • 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
  • 100Tools and files. No external tools needed
  • 100Steps. 83 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1063 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

  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • -222 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 4 example trigger phrases
  • +3Description length 157: enough signal without eating the budget
  • +4Structure: 26 headings
  • +3Step-by-step instructions: 83 items
  • +4Has examples (2 code blocks)
  • +4Reference files are cited in the instructions (2 of 2)
  • +3All 3 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 82.

External checks

ClawHub: suspicious
The skill fits its paper-revision purpose, but its generated reports use an undeclared remote chart script and may present hard-coded generic advice as document-specific analysis.
LLM: suspicious (high) · 13 Jun 2026