BD saas-decision
SaaS产品辅助决策助手。用户输入SaaS产品行业与定位，自动从市场需求、用户画像、需求痛点、竞品格局、变现定价(MRR/ARR/LTV)、获客增长(CAC/PLG)、推广营销、成本结构、技术可行性9大维度进行综合分析，生成专业交互式HTML可行性决策报告。涵盖SaaS定价模型对比、MRR预估、LTV/CAC分析、多租户架构选型等SaaS专属议题。触发词：SaaS决策, SaaS可行性, SaaS评估, SaaS能不能做, 开发SaaS, SaaS分析报告, SaaS调研, 软件即服务决策, saas decision, SaaS选型, SaaS创业评估, 做SaaS产品。
SaaS产品辅助决策助手。用户输入SaaS产品行业与定位，自动从市场需求、用户画像、需求痛点、竞品格局、变现定价(MRR/ARR/LTV)、获客增长(CAC/PLG)、推广营销、成本结构、技术可行性9大维度进行综合分析，生成专业交互式HTML可行性决策报告。涵盖SaaS定价模型对比、MRR预估、LTV/CAC分析、多…
As a process D 46/100 · 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 · 0
✓ No critical or high findings
Files scanned: 4. 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 46/100
- 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
- 60Tools and files. Uses tools (web, python) that frontmatter does not declare
- 100Steps. 47 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1009 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
- +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
- -213 emoji in the instructions: noise for the model
- +1No license
- +2Single-language instructions
- +3Description length 290: enough signal without eating the budget
- +4Structure: 11 headings
- +3Step-by-step instructions: 47 items
- +4Has examples (3 code blocks)
- +3All 1 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 72.