AC daily-decision-making
一个结构化的个人决策数据库系统，帮助用户系统性地处理生活与职业中的复杂抉择。 当用户面临以下情况时使用此技能： (1) 遇到"我该怎么办""帮我做个决定""我在 X 和 Y 之间纠结"等表述； (2) 涉及职业（跳槽/转行）、生活方式（搬家）、重大消费（买房/买车）或人际关系的决策； (3) 需要系统性地权衡利弊； (4) 想把决策过程记录下来以供日后回顾和复盘。
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.
For the model run — optional
- 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: 3. 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 "triggers"
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. No external tools needed
- 100Steps. 20 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 381 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 2 example trigger phrases
- +3Description length 183: enough signal without eating the budget
- +4Structure: 13 headings
- +3Step-by-step instructions: 20 items
- +4Has examples (1 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 76.
External checks
ClawHub: clean
This is a transparent decision-journaling skill whose main risk is saving sensitive personal notes if the user chooses to create records.
LLM: benign (high) · VirusTotal: · 29 May 2026