BD ling-shu-agent-designer
Agent 孵化器，聚焦「孵化 Agent」这一核心场景。从业务需求出发，自动完成 Agent 的骨架搭建、能力封装和发布。 核心工作流：需求沟通 → 场景大纲 → 创建基础版 Agent → skill 按需迭代（AI 内部工作节奏，用户不感知技术细节）。 触发场景：(1) 用户说"设计/创建一个Agent"、"帮我做个智能助手"；(2) 用户说"给 Agent 增加XX能力"；(3) 企业级 Agent 体系规划（配合 enterprise-agent-planner）。 设计原则：AI 隐藏技术细节；先跑 MVP 再迭代；Agent = 配置 + skill 包。 融合思想：吴明辉（组织视角）+ 吴恩达（方法视角）+ 傅盛（落地视角）。
Agent 孵化器，聚焦「孵化 Agent」这一核心场景。从业务需求出发，自动完成 Agent 的骨架搭建、能力封装和发布。 核心工作流：需求沟通 → 场景大纲 → 创建基础版 Agent → skill 按需迭代（AI 内部工作节奏，用户不感知技术细节）。 触发场景：(1)…
As a process D 43/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
What is at stake
Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.
Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.
The skill asks for more than the task needs: broad tool access, credential environment variables, binaries. Every extra permission widens the damage from a mistake or a compromise.
Narrow allowed-tools and the variable list to the minimum; replace binaries with readable sources or scripts.
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 · 1
✓ No critical or high findings
Medium and low: 1
-
medium Broad scope
meta-agent-memory-dumpIDENTITY.mdAgent memory / workspace files bundled with the skill (2) — likely a workspace dump with personal data or tokensIDENTITY.md, SOUL.md
Files scanned: 5. 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 43/100
- 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
- 100Steps. 38 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1737 tokens
- low 15 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
- -215 emoji in the instructions: noise for the model
- +1No license
- +2Single-language instructions
- +5Description quotes 3 example trigger phrases
- +3Description length 325: enough signal without eating the budget
- +4Structure: 42 headings
- +3Step-by-step instructions: 38 items
- +4Has examples (6 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.