BC productivity-boost
面向真实工作场景的效率操作系统，直击"任务清单焦虑、完美系统难维护、能量错配、忽视情境约束"四大痛点。不追求理想化全功能系统，而是从最小可行结构起步，基于能量与情境动态调整，通过反过载分诊和强制回顾重建系统信任。 核心能力包括能量感知规划（高/中/低能量时段匹配任务类型）、最小可行系统起步（5 文件够用，按需扩展）、反过载分诊（紧急砍范围而非加任务）、情境适配指南（ADHD/倦怠/远程/管理者等 10+ 角色）、目标-项目-任务严格分层、习惯摩擦管理、每日/每周/每月回顾节奏、承诺与委派跟踪、深度工作保护。 适用场景：任务清单过长感到焦虑、系统建了却不维护、能量管理与任务错配、需要情境化建议（ADHD/倦怠/远程）、希望可持续而非三天崩塌的效率系统。 差异化：相比理想化效率方法论，本系统从最小可行起步避免过度设计、能量感知匹配避免高能做低价值事、反过载分诊优先砍范围、10+ 情境指南适配真实约束、回顾节奏重于频繁重计划。所有文件按需加载，降低 token 消耗。 触发关键词：效率、生产力、任务管理、过载、倦怠、计划、回顾、focus、productivity、burnout、ADHD
面向真实工作场景的效率操作系统，直击"任务清单焦虑、完美系统难维护、能量错配、忽视情境约束"四大痛点。不追求理想化全功能系统，而是从最小可行结构起步，基于能量与情境动态调整，通过反过载分诊和强制回顾重建系统信任。 核心能力包括能量感知规划（高/中/低能量时段匹配任务类型）、最小可行系统起步（5…
As a process C 51/100 · 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 51/100
- 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
- 30Running it twice. 1 mutating operations with no state check
- 100Tools and files. Tools declared in frontmatter
- 100Steps. 7 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2029 tokens
- low 17 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 503: enough signal without eating the budget
- +4Structure: 39 headings
- +3Step-by-step instructions: 7 items
- +4Has examples (10 code blocks)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 70.