AD jf-store-traffic
杰峰连锁门店精准客流部署与数据分析技能。通过工作流完成门店创建、HA-5P-GM客流摄像头接入、精准客流算法配置（区域画框/进出店模式/外卖员过滤），并支持查询门店客流聚合统计（进店率/去重顾客数/批次/年龄性别分布），生成可视化 HTML 分析报告（KPI 总览/每日趋势/客群画像/进店批次）。当用户提到精准客流、门店客流、客流统计、客流部署、配置客流、连锁门店、进店率、客群分析、客流报告、客流报表、store traffic、foot traffic 时使用此技能。
杰峰连锁门店精准客流部署与数据分析技能。通过工作流完成门店创建、HA-5P-GM客流摄像头接入、精准客流算法配置（区域画框/进出店模式/外卖员过滤），并支持查询门店客流聚合统计（进店率/去重顾客数/批次/年龄性别分布），生成可视化 HTML 分析报告（KPI…
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.
- 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 · 1
✓ No critical or high findings
Medium and low: 1
-
low Secrets in code
secret-labelled-tokenscripts/crypto.py:172Labelled token / key literal (vendor format unknown — verify it is not a live credential) (placeholder value)app_secret = "90f8…xxx"
placeholder
Files scanned: 7. 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")
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
- 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
- 30Running it twice. 8 mutating operations with no state check
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 100Steps. 20 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2735 tokens
- 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
- +1No license
- +2Single-language instructions
- +3Description length 237: enough signal without eating the budget
- +4Structure: 18 headings
- +3Step-by-step instructions: 20 items
- +4Has examples (7 code blocks)
- +4Reference files are cited in the instructions (1 of 1)
- +3All 2 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 79.