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AI产品经理全流程助手。覆盖需求洞察→竞品分析→PRD方案设计→AI能力设计(Prompt/Agent/RAG)→上线评估→迭代优化6大阶段。融合模型边界管理、Prompt工程、Agent编排、RAG数据工程、AI评估五大核心能力。触发词: AI产品经理, AI PM, 写PRD, 竞品分析, Prompt设计, Agent工作流, RAG设计, AI评估, 需求分析, 产品方案, 模型选型, AI功能设计, ai product manager, PRD生成, 技术可行性评估, AI产品设计。
AI产品经理全流程助手。覆盖需求洞察→竞品分析→PRD方案设计→AI能力设计(Prompt/Agent/RAG)→上线评估→迭代优化6大阶段。融合模型边界管理、Prompt工程、Agent编排、RAG数据工程、AI评估五大核心能力。触发词: AI产品经理, AI PM, 写PRD, 竞品分析, Prompt设计…
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.
- 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: 6. 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 "agent_created"
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. 121 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1935 tokens
- 100Running it twice. No mutating operations
- low 10 top-level sections: this looks like several domains in one skill
- low The skill ranks results itself: that belongs to the system behind the tool, not the model
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
- -42 reference files, but SKILL.md never points to them: the model will not open them
- -32 of 2 scripts are never mentioned in SKILL.md
- +1No license
- +2Single-language instructions
- +3Description length 250: enough signal without eating the budget
- +4Structure: 39 headings
- +3Step-by-step instructions: 121 items
- +4Has examples (14 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 64.