AC batch-processor-pro
批处理专家是处理大量数据项的能力包。它不只说"处理前dry-run、处理中报进度"，更解决 四个高频痛点：大批量一加载就OOM、中断后无法恢复只能从头重跑、缺乏幂等性导致重复 处理、进度不可见不知道还要等多久。 核心能力： - 流式分块：按行/按文件分块处理，内存恒定不OOM - 检查点恢复：每N项存检查点，中断后从断点续跑而非从头 - 幂等设计：每项有唯一键，重复执行只处理一次 - 进度报告：每10项报进度，预估剩余时间 - 并行决策矩阵：按任务类型选串行/并行/分片 - 五级错误分级：瞬时错误重试、数据错误跳过、系统错误中止 适用场景： - 批量处理目录下数百个文件 - 批量调API处理上万条记录 - 批量图片/视频转换 - 批量数据清洗与校验 - 任何"要对N个东西做同样操作"的任务 差异化： - 原始版本只有"前/中/后"三段口号，本版补齐可执行的检查点格式与恢复脚本 - 新增流式分块策略（按行/按文件/按批次） - 新增幂等设计章节（唯一键+去重检查） - 新增并行vs串行决策矩阵 - 新增五级错误分级与对应处理 - 增加FAQ与故障排查 触发关键词：批处理、批量、检查点、断点续跑、幂等、并行、流式、OOM、进度
批处理专家是处理大量数据项的能力包。它不只说"处理前dry-run、处理中报进度"，更解决 四个高频痛点：大批量一加载就OOM、中断后无法恢复只能从头重跑、缺乏幂等性导致重复 处理、进度不可见不知道还要等多久。 核心能力： - 流式分块：按行/按文件分块处理，内存恒定不OOM -…
As a process C 53/100 · 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 53/100
- 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
- 100Tools and files. Tools declared in frontmatter
- 100Steps. 15 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2318 tokens
- 100Running it twice. No mutating operations
- low 13 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
- +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
- +5Description quotes 2 example trigger phrases
- +3Description length 523: enough signal without eating the budget
- +4Structure: 36 headings
- +3Step-by-step instructions: 15 items
- +4Has examples (18 code blocks)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.