AC enterprise-financial-stress-assessment
围绕目标企业现金流状态、融资活动、债务负担、偿债能力、回款压力、信用风险及经营承压信号等 核心维度，依托公开合规信息，搭建覆盖“经营造血能力—融资补血能力—债务偿付压力—外部信用风险— 经营承压迹象—资金压力等级—风险应对建议”的全维度企业资金压力分析体系。本技能核心解决“企业 是否存在资金压力、压力核心来源、偿债风险等级、融资依赖程度、资金风险对招商合作及项目落地的 影响”等关键问题。当用户需要分析企业资金压力、评估现金流状态、研判偿债风险、排查资金链隐患、 评估企业招引适配性、研判项目落地风险或开展企业信用尽调时，激活此技能。
围绕目标企业现金流状态、融资活动、债务负担、偿债能力、回款压力、信用风险及经营承压信号等 核心维度，依托公开合规信息，搭建覆盖“经营造血能力—融资补血能力—债务偿付压力—外部信用风险— 经营承压迹象—资金压力等级—风险应对建议”的全维度企业资金压力分析体系。本技能核心解决“企业…
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: 3. 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 "slug" - note
frontmatter-keyunknown frontmatter key "displayName" - note
frontmatter-keyunknown frontmatter key "summary" - note
frontmatter-keyunknown frontmatter key "triggers" - note
frontmatter-keyunknown frontmatter key "parameters" - note
frontmatter-keyunknown frontmatter key "tools"
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. Tools declared in frontmatter
- 100Steps. 140 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2065 tokens
- 100Running it twice. No mutating operations
- low 13 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
- +2Single-language instructions
- +5Description quotes 2 example trigger phrases
- +3Description length 268: enough signal without eating the budget
- +4Structure: 41 headings
- +3Step-by-step instructions: 140 items
- +4Has examples (1 code blocks)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.