BC project-landing-risk-assessment
围绕招商项目、产业项目、投资项目在建设、运营、市场、政策、资金、审批、环保、安全 及区域承接等方面的潜在风险，系统评估项目从签约、建设、投产到运营全过程中可能面临的 不确定因素，形成项目落地风险识别、风险等级判断及风险应对建议。本技能核心解决"项目 能不能顺利落地、落地过程中有哪些风险、哪些风险会影响建设进度和运营成效、政府或园区 应如何提前防控"等核心问题。当用户需要评估招商项目落地风险、判断项目是否适合继续推进、 分析项目建设运营市场政策风险、对签约项目进行风险审查、对重点项目进行上会前风控评估、 判断项目落地卡点、制定项目风险应对方案、形成项目落地风险评估报告时，激活此技能。
围绕招商项目、产业项目、投资项目在建设、运营、市场、政策、资金、审批、环保、安全 及区域承接等方面的潜在风险，系统评估项目从签约、建设、投产到运营全过程中可能面临的 不确定因素，形成项目落地风险识别、风险等级判断及风险应对建议。本技能核心解决"项目…
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: 2. 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. 198 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1293 tokens
- 100Running it twice. No mutating operations
- 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
- +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
- +4No input/output examples
- +2Single-language instructions
- +3Description length 294: enough signal without eating the budget
- +4Structure: 54 headings
- +3Step-by-step instructions: 198 items
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 66.