BD academic-paper-refinement
综合多评审系统的学术论文精修技能。适用于学术论文从初稿到终稿的全流程修订，包括多轮评审、意见整合、结构优化和语言润色。
As a process D 43/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
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: 3. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-no-whendescription does not say WHEN to use the skill (no "use when") - warning
body-longSKILL.md body ≈ 12117 tokens (recommended < 5000); move details to references/ - note
frontmatter-keyunknown frontmatter key "dependencies" - note
frontmatter-keyunknown frontmatter key "ccf_reference"
Process rating: all ten parameters 43/100
- 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
- 40Execution cost. Instruction body is 12117 tokens: crowds the task out of the window
- 60Tools and files. Uses tools (bash, node) that frontmatter does not declare
- 100Steps. 70 steps
- 100Consistency. Name and required fields are in place
- 100Running it twice. No mutating operations
- low 19 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)
- +3Description length 59: 120–800 characters recommended
- +3Output format is not stated: the model decides each time
- -265 emoji in the instructions: noise for the model
- -31 of 1 scripts are never mentioned in SKILL.md
- +2Single-language instructions
- +4Structure: 129 headings
- +3Step-by-step instructions: 70 items
- +4Has examples (51 code blocks)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 53.
External checks
ClawHub: suspicious
This academic refinement skill is mostly a manuscript-editing workflow, but it needs review because it makes AI-detection reduction a required academic writing step and runs broad external review/editing tools over user papers.
LLM: suspicious (high) · VirusTotal: · 29 May 2026