BD real-estate-risk-analyst
房地产风险分析全链路工作流（采集→去化→货值→授信）。覆盖：跨城房源与备案价采集（36城实战、WAF瑞数攻坚、CDP兜底）、去化率四档口径（毛B/净A/签约C/现金回笼D）、开发商穿透检索防JV漏归、预售信息失真核查、货值与授信量化（业态折扣、可变现货值、结清触发点）、实战案例协议、交付规范。适用于：查楼盘房源备案价、算去化率、排查房企在售项目与现金流、做房地产授信尽调。曾用名 real-estate-filing-query。
房地产风险分析全链路工作流（采集→去化→货值→授信）。覆盖：跨城房源与备案价采集（36城实战、WAF瑞数攻坚、CDP兜底）、去化率四档口径（毛B/净A/签约C/现金回笼D）、开发商穿透检索防JV漏归、预售信息失真核查、货值与授信量化（业态折扣、可变现货值、结清触发点）、实战案例协议、交付规范。适用于：查楼盘房源备案价…
As a process D 43/100 · 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: 25. 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 ≈ 16739 tokens (recommended < 5000); move details to references/ - note
frontmatter-keyunknown frontmatter key "agent_created"
Process rating: all ten parameters 43/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Progress reporting. Says nothing while it works
- 10Execution cost. Instruction body is 16739 tokens: crowds the task out of the window
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 2 mutating operations with no state check
- 50Failures and branches. 0 branches, has a failure section
- 60Tools and files. Uses tools (bash, web, python, node) that frontmatter does not declare
- 100Steps. 327 steps
- 100Consistency. Name and required fields are in place
- low 12 top-level sections: this looks like several domains in one skill
- low The response is described with custom markup (13 tags): a typed call is more reliable
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
- -2localhost URLs: will not work for another user
- -294 emoji in the instructions: noise for the model
- +1No license
- +2Single-language instructions
- +3Description length 216: enough signal without eating the budget
- +4Structure: 50 headings
- +3Step-by-step instructions: 327 items
- +4Has examples (4 code blocks)
- +4Reference files are cited in the instructions (9 of 9)
- +3All 8 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 64.