CC huawei-cloud-optv-solver-assistant
1. 通过OptVerse决策引擎完成数学规划问题的需求分析、建模、数据校验、求解和资产发布全流程； 2. 适用于运筹优化场景（如设施选址、路径规划、生产排程等），用户提供需求分析文件和数据文件，通过多轮对话与决策引擎交互完成求解； 3. 触发词：OptVerse求解助手、决策引擎、数学规划建模、运筹优化求解、需求分析建模、天筹工具链、设施选址优化、模型发布
1. 通过OptVerse决策引擎完成数学规划问题的需求分析、建模、数据校验、求解和资产发布全流程； 2. 适用于运筹优化场景（如设施选址、路径规划、生产排程等），用户提供需求分析文件和数据文件，通过多轮对话与决策引擎交互完成求解； 3.…
As a process C 57/100 · Has gaps — weak spots: result and completion, when it triggers, failures and branches
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.
- 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: 12. 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 ≈ 5660 tokens (recommended < 5000); move details to references/
Process rating: all ten parameters 57/100
- 0Failures and branches. Linear process with no failure handling
- 20When it triggers. No condition that starts the skill
- 40Result and completion. Does not say what the result is
- 60Tools and files. Uses tools (bash, read, web, python) that frontmatter does not declare
- 70Inputs and preconditions. Inputs and preconditions are listed
- 70Execution cost. Instruction body is 5660 tokens
- 85Steps. 45 steps, 1 vague phrases
- 100Consistency. Name and required fields are in place
- 100Running it twice. Mutating operations check current state
- 100Progress reporting. Reports progress
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- 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
- -4Absolute local paths (C:\Users, /home/…): not portable
- +1No license
- +2Single-language instructions
- +3Description length 180: enough signal without eating the budget
- +4Structure: 40 headings
- +3Step-by-step instructions: 45 items
- +4Has examples (20 code blocks)
- +4Reference files are cited in the instructions (7 of 7)
- +3All 2 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 65.