BD onescience-skill
OneScience 技能管理器，提供热点模型、数据集和组件内容，将用户需求转化为结构化流程，并自动编排调用相关 OneScience 技能。当用户使用 OneScience 模式开发代码、读取数据、训练模型或执行研究项目时，自动路由到正确的技能组合。
As a process D 46/100 · 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.
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: 2. 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 46/100
- 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
- 30Running it twice. 1 mutating operations with no state check
- 40Consistency. Frontmatter name (onescience-skill) differs from the folder (onescience-skills)
- 100Tools and files. No external tools needed
- 100Steps. 113 steps
- 100Execution cost. Instruction body is 2129 tokens
- low 18 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
- +1No license
- +2Single-language instructions
- +3Description length 126: enough signal without eating the budget
- +4Structure: 53 headings
- +3Step-by-step instructions: 113 items
- +4Has examples (12 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 72.
External checks
ClawHub: suspicious
This OneScience skill is mostly purpose-aligned for HPC coding and testing, but it gives agents broad remote-install, SSH-config-reading, file-upload, and SLURM job-submission authority without enough consent and safety boundaries.
LLM: suspicious (high) · VirusTotal: · 29 May 2026