AB biomedical-reference-verifier
Verify or normalize biomedical and life-science reference lists, when the task is about AI-caused reference errors. Checks identifiers and bibliographic fields, preserves source evidence, and produces a searchable offline report. 生物医学/生命科学参考文献真实性验证skill，可以对参考文献列表（引文列表）进行多轮核查和错误修复，附带引文格式整理功能，能统一规范化所有引文为AMA、APA、GB/T 7714等格式。
Verify or normalize biomedical and life-science reference lists, when the task is about AI-caused reference errors.
As a process B 71/100 · Nearly there — weak spots: inputs and preconditions, running it twice, progress reporting
How to improve
- 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: 21. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- note
edit-residuethe text marks something as outdated (lines 105): check that old rules are not kept next to new ones — the full check reads the text for contradictions
Process rating: all ten parameters 71/100
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Progress reporting. Says nothing while it works
- 30Running it twice. 12 mutating operations with no state check
- 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
- 60Result and completion. Output format stated, no completion criterion
- 100Steps. 39 steps
- 100When it triggers. States when to use and when not to
- 100Failures and branches. 1 branches, has a failure section
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 3734 tokens
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- low 11 top-level sections: this looks like several domains in one skill
- low The skill ranks results itself: that belongs to the system behind the tool, not the model
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)
- -35 of 13 scripts are never mentioned in SKILL.md
- +2Single-language instructions
- +3Description length 324: enough signal without eating the budget
- +4Structure: 12 headings
- +3Step-by-step instructions: 39 items
- +3Output format is stated explicitly
- +4Has examples (4 code blocks)
- +4Reference files are cited in the instructions (1 of 1)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 89.