BD zero-one-two-three
基于中国道家思维从"道"到"万物"，执行第一性原理的智能体分发系统。核心理念：0+1+2≠3→∞。独创"邮箱灵感笔记"与"人机协作知识创生引擎"。探索知识资产变现交付方式：知识二创、加密解密、阅后即焚等。完成数字分身胶囊"造人→打包→部署→变现"全闭环：通过微信上操作装灵魂+大脑，小微智能体小程序加枷锁+收钱+兑换码，微信生态一键分发裂变。其中包含 LangChain 向量连接器（支持 IMA/Get 笔记/语雀/飞书）、跨平台知识碰撞、大模型自动填补审批流，特色功能图书馆、沉默是金、发芽联想、风格克隆、语音分身。
As a process D 42/100 · Unfinished process — 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 · 4
✓ No critical or high findings
Medium and low: 4
-
low Secrets in code
secret-password-literalknowledge_lock.py:700Hard-coded password / key literal (may be an example)password = sys.argv[3]
-
low Secrets in code
secret-password-literalknowledge_lock.py:724Hard-coded password / key literal (may be an example)password = sys.argv[3]
-
low Secrets in code
secret-high-entropy-tokenSKILL.md:115High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)- 🧠 **默认**：`para…-v2` (HuggingFace，首次自动下载~400MB)
quoted -
low Secrets in code
secret-high-entropy-tokenSKILL.md:427High-entropy token-like string (may be an id, hash or a credential)🧠 加载嵌入模型：para…-v2
Files scanned: 19. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
frontmatter-yamlSKILL.md: the frontmatter is not valid YAML (YAML parse error: Plain value cannot start with reserved character @ at line 4, column 12: namespace: @zero-one-two-three ^ ); fields were read line by line. The usual cause is a colon inside an unquoted value - warning
description-no-whendescription does not say WHEN to use the skill (no "use when") - note
frontmatter-keyunknown frontmatter key "namespace"
Process rating: all ten parameters 42/100
- 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
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 70Execution cost. Instruction body is 4126 tokens
- 100Steps. 62 steps
- 100Consistency. Name and required fields are in place
- low 10 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
- -2123 emoji in the instructions: noise for the model
- +2Single-language instructions
- +5Description quotes 3 example trigger phrases
- +3Description length 260: enough signal without eating the budget
- +4Structure: 49 headings
- +3Step-by-step instructions: 62 items
- +4Has examples (24 code blocks)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 65.