AD coolskill-builder
Skill 生成与治理节点。将任何资源（GitHub 仓库、API 文档、自然语言需求、代码片段） 转化为零外部依赖、极致省 Token、可被任意生态（Kimi/OpenAI/Claude/自研 Agent）直接调用的标准化 Skill。 触发条件：(1) 用户要求创建/生成/编写一个 Skill，(2) 用户提供资源要求转化为可调用模块， (3) 用户提到零依赖、跨生态兼容、版本隔离等关键词，(4) 用户要求将代码/功能封装为标准化 Skill， (5) 用户要求实现 skill registry、版本管理或安全扫描，(6) 任何涉及 Agent 工具/函数生成的需求。 使用时加载此 Skill，严格按 6 步工作流执行，生成 4 文件（skill.yaml + impl.py + test.py + manifest.json） 并通过 5 层安全校验。
Skill 生成与治理节点。将任何资源（GitHub 仓库、API 文档、自然语言需求、代码片段） 转化为零外部依赖、极致省 Token、可被任意生态（Kimi/OpenAI/Claude/自研 Agent）直接调用的标准化 Skill。 触发条件：(1) 用户要求创建/生成/编写一个 Skill，(2)…
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.
- 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: 10. 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
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 100Steps. 53 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1403 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
- +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 384: enough signal without eating the budget
- +4Structure: 19 headings
- +3Step-by-step instructions: 53 items
- +4Has examples (4 code blocks)
- +4Reference files are cited in the instructions (4 of 4)
- +3All 3 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 79.