AC benzhi
本质分析技能——用于深度分析事物本质的自我追问工具。当用户明确要求分析某个事物/现象/概念的本质时触发（如"XX的本质是什么"、"分析一下XX的本质"、"挖一下XX的本质"、"为什么XX"）。通过十维假设（生成即筛选）、维度权重排序、深度追问（带停止标准）、正反推收敛验证、反例攻击，帮助从多维度识别核心变量、内在悖论和深层规律。支持快速模式和深度模式。
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: 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")
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. 17 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2449 tokens
- 100Running it twice. No mutating operations
- low 11 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
- -225 emoji in the instructions: noise for the model
- +1No license
- +2Single-language instructions
- +5Description quotes 3 example trigger phrases
- +3Description length 177: enough signal without eating the budget
- +4Structure: 29 headings
- +3Step-by-step instructions: 17 items
- +4Has examples (27 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.
External checks
ClawHub: clean
This is an instruction-only analysis framework with no code execution, credentials, external access, or install-time behavior; its main caveats are broad trigger wording and optional-looking self-learning notes.
LLM: benign (high) · VirusTotal: · 29 May 2026