AB huawei-cloud-agentorchard-find-skills
Search, discover, browse and install AI Gallery Agent skills via natural language. Triggers include: "AI Gallery", "AI Gallery有什么skill", "有什么skill", "AI Gallery相关skill", "AI Gallery agent skill 市场", "AI Gallery skill类目", "skill 市场", "搜索AI Gallery skill", "安装skill", "订阅skill", "有没有XX skill", "有没有XX的能力", "帮我找 XX skill", "帮我找一个能XX的工具", "我想扩展功能", "介绍 XX Skill 内容", "XX Skill 具体做什么", "explore AI Gallery skills", "show AI Gallery skill categories", "does an AI Gallery skill exist for...", "which AI Gallery skills exist", "search skill", "find skill".
Search, discover, browse and install AI Gallery Agent skills via natural language.
As a process B 65/100 · Nearly there — weak spots: result and completion, when it triggers, progress reporting
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- 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: 8. 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 65/100
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 40Result and completion. Does not say what the result is
- 50Failures and branches. 0 branches, has a failure section
- 60Tools and files. Uses tools (web, python) that frontmatter does not declare
- 70Inputs and preconditions. Inputs and preconditions are listed
- 100Steps. 19 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 828 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
- +1No license
- +2Single-language instructions
- +5Description quotes 23 example trigger phrases
- +3Description length 549: enough signal without eating the budget
- +4Structure: 10 headings
- +3Step-by-step instructions: 19 items
- +4Has examples (1 code blocks)
- +4Reference files are cited in the instructions (5 of 5)
- +3All 1 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.