CD insurance-product-analyzer
面向消费者/投保人的保险产品分析器。当用户上传/粘贴/描述一款或多款保险产品（储蓄理财类如增额寿、年金、分红、两全、万能；健康保障类如寿/重/医/意外；财产险如车险/家财），希望「分析这款保险值不值」「对比几款产品」「算算 IRR/回本/总利益」「看懂条款坑点」时使用。支持单品深析与多品横向对比，内置红利/万能计算规则与消费者避坑清单。注意：本技能只做产品分析，不自动出保险计划书。
面向消费者/投保人的保险产品分析器。当用户上传/粘贴/描述一款或多款保险产品（储蓄理财类如增额寿、年金、分红、两全、万能；健康保障类如寿/重/医/意外；财产险如车险/家财），希望「分析这款保险值不值」「对比几款产品」「算算…
As a process D 40/100 · 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.
- The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
- 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: 29. 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") - warning
body-longSKILL.md body ≈ 14847 tokens (recommended < 5000); move details to references/ - note
frontmatter-keyunknown frontmatter key "agent_created"
Process rating: all ten parameters 40/100
- 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
- 20When it triggers. No condition that starts the skill
- 40Consistency. Frontmatter name (insurance-product-analyzer) differs from the folder (insurance)
- 40Execution cost. Instruction body is 14847 tokens: crowds the task out of the window
- 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
- 100Steps. 314 steps
- 100Running it twice. No mutating operations
- 100Progress reporting. Reports progress
- low 14 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
- -234 emoji in the instructions: noise for the model
- +1No license
- +2Single-language instructions
- +3Description length 192: enough signal without eating the budget
- +4Structure: 70 headings
- +3Step-by-step instructions: 314 items
- +4Has examples (16 code blocks)
- +4Reference files are cited in the instructions (1 of 1)
- +3All 14 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 66.