AC protein-key-fragment-analysis
蛋白质关键序列片段预测分析。对任意蛋白质家族的多物种FASTA序列执行完整分析流程，提取共识序列并识别关键功能片段、统计氨基酸组成、预测片段主要功能。适用于：（1）用户提到"提取蛋白关键序列/片段"、"分析蛋白保守区"、"预测蛋白功能片段"时，（2）对新物种/类群运行完整分析流程，（3）从已有FASTA序列提取共识序列并识别关键片段，（4）跨物种横向对比关键片段差异，（5）生成结构化分析报告。适用于任何蛋白质家族。
As a process C 53/100 · Has gaps — 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.
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: 49. 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")
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. 39 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1373 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
- -214 emoji in the instructions: noise for the model
- +1No license
- +2Single-language instructions
- +5Description quotes 3 example trigger phrases
- +3Description length 209: enough signal without eating the budget
- +4Structure: 23 headings
- +3Step-by-step instructions: 39 items
- +4Has examples (11 code blocks)
- +4Reference files are cited in the instructions (2 of 3)
- +3All 2 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 82.
External checks
ClawHub: clean
This appears to be a local protein-analysis skill with some documentation and scientific-claim caveats, but no hidden data access, persistence, or malicious behavior.
LLM: benign (high) · VirusTotal: · 29 May 2026