BF restaurant-site-selection-roi
资深商业地产数据分析师视角的餐饮商铺量化评估与决策工具。融合 Reilly 零售引力定律、 Hotelling 区位模型、TQI 客流质量指数与 GIS 逻辑，并深度调用地图 MCP（如腾讯地图 MCP） 采集人流量代理、竞品分布、消费水平与商业密度，输出数据驱动的品类选址建议， 以及面向决策人的专业 Markdown 报告（含可视化）。 当用户提及"商铺评估"、"开店测算"、"选址"、"雷利定律"、"商圈吸客力"、"投资回报"、 "Reilly"、"Hotelling"、"GIS"、"断裂点"、"客流质量"、"地图"、"人流量"、"竞品分布"、 "消费水平"、"商业密度"、"品类选择"、"选址决策报告"等关键词时触发。 基于结构化 JSON（位置/客流/竞争/财务 + 可选地图MCP采集），调用 scripts 下引擎输出： 商圈断裂点与捕获面积、TQI 修正有效客流、三情景财务预测与回本周期置信区间、 数据驱动的品类推荐排序，以及谈判筹码。内置消防/排烟/产权一票否决（Red Flag）。
资深商业地产数据分析师视角的餐饮商铺量化评估与决策工具。融合 Reilly 零售引力定律、 Hotelling 区位模型、TQI 客流质量指数与 GIS 逻辑，并深度调用地图 MCP（如腾讯地图 MCP） 采集人流量代理、竞品分布、消费水平与商业密度，输出数据驱动的品类选址建议， 以及面向决策人的专业…
As a process F 31/100 · Will not run — References files that are not bundled: references/map_mcp_guide.md, references/benchmarks.md, scripts/category_recommender.py
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- The text references files that are not there: add them or drop the references.
- 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: 0. 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
missing-refreference to a missing file: references/map_mcp_guide.md - warning
missing-refreference to a missing file: references/benchmarks.md - warning
missing-refreference to a missing file: scripts/category_recommender.py - warning
missing-refreference to a missing file: examples/decision_report_demo.md - warning
missing-refreference to a missing file: scripts/roi_calculator.py - warning
missing-refreference to a missing file: scripts/report_generator.py - note
frontmatter-keyunknown frontmatter key "display_name" - note
frontmatter-keyunknown frontmatter key "agent_created"
Process rating: all ten parameters 31/100
- 0Tools and files. 6 referenced file(s) missing: references/map_mcp_guide.md, references/benchmarks.md, scripts/category_recommender.py
- 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
- 40Consistency. Frontmatter name (restaurant-site-selection-roi) differs from the folder (location-site-selection)
- 100Steps. 83 steps
- 100Execution cost. Instruction body is 2944 tokens
- 100Running it twice. No mutating operations
- low 10 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
- +1No license
- +2Single-language instructions
- +5Description quotes 3 example trigger phrases
- +3Description length 453: enough signal without eating the budget
- +4Structure: 29 headings
- +3Step-by-step instructions: 83 items
- +4Has examples (3 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 63.