BC system-controller
系统控制器为 AI Agent 提供操作系统层面的统一控制能力，覆盖进程管理、服务启停、文件事务、环境变量、计划任务与系统信息采集。它把 Linux/macOS/Windows 三套差异巨大的命令抽象为统一语义，让 Agent 用同一套指令跨平台操作。 核心能力：跨平台进程管理（查/启/停/杀）、系统服务控制、文件事务操作（带原子写入与回滚）、环境变量与持久配置、计划任务管理、系统资源监控、操作审计日志。 适用场景：自动化运维、环境初始化、服务排障、批量配置、一人公司服务器管理、Agent 长驻守护。 差异化：相比仅罗列命令的原始方案，本技能新增跨平台命令映射矩阵（同一语义自动翻译为对应平台命令）、文件事务回滚（写前快照，失败自动恢复）、进程安全终止梯度（SIGTERM→等待→SIGKILL）、操作审计日志（所有变更可追溯）、危险操作分级确认。明确聚焦"系统层"控制，不涉及 GUI 鼠标键盘（那是桌面自动驾驶技能的职责）。 触发关键词：进程, 服务, 文件, 系统, 进程管理, 服务控制, 环境变量, 系统信息, process, service, file, system, control, manage
系统控制器为 AI Agent 提供操作系统层面的统一控制能力，覆盖进程管理、服务启停、文件事务、环境变量、计划任务与系统信息采集。它把 Linux/macOS/Windows 三套差异巨大的命令抽象为统一语义，让 Agent 用同一套指令跨平台操作。…
As a process C 56/100 · 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.
- 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") - note
frontmatter-keyunknown frontmatter key "slug" - note
frontmatter-keyunknown frontmatter key "displayName" - note
frontmatter-keyunknown frontmatter key "summary" - note
frontmatter-keyunknown frontmatter key "tools"
Process rating: all ten parameters 56/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 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
- 50Failures and branches. 0 branches, has a failure section
- 100Tools and files. Tools declared in frontmatter
- 100Steps. 25 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2195 tokens
- low 14 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
- +2Single-language instructions
- +3Description length 515: enough signal without eating the budget
- +4Structure: 36 headings
- +3Step-by-step instructions: 25 items
- +4Has examples (25 code blocks)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 70.