Clerk Auth
davila7/claude-code-templates
Expert patterns for Clerk auth implementation, middleware, organizations, webhooks, and user sync Use when: adding authentication, clerk auth, user authentication, sign in, sign up.
导购个人业绩深度分析工具。支持普通门店(POS数据)和AIoT门店(POS+AIoT数据). An agent skill from LeoYeAI/openclaw-master-skills.
$ npx skills add LeoYeAI/openclaw-master-skills --skill retail-clerk-performance-analysis -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install LeoYeAI/openclaw-master-skills retail-clerk-performance-analysis --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/retail-clerk-performance-analysis .claude/skills/retail-clerk-performance-analysis && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "retail-clerk-performance-analysis" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/retail-clerk-performance-analysis into .claude/skills/retail-clerk-performance-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "retail-clerk-performance-analysis", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/retail-clerk-performance-analysisType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add LeoYeAI/openclaw-master-skills --skill retail-clerk-performance-analysis -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install LeoYeAI/openclaw-master-skills retail-clerk-performance-analysis --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/retail-clerk-performance-analysis .agents/skills/retail-clerk-performance-analysis && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "retail-clerk-performance-analysis" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/retail-clerk-performance-analysis into .agents/skills/retail-clerk-performance-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "retail-clerk-performance-analysis", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add LeoYeAI/openclaw-master-skills --skill retail-clerk-performance-analysis -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install LeoYeAI/openclaw-master-skills retail-clerk-performance-analysis --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/retail-clerk-performance-analysis .cursor/skills/retail-clerk-performance-analysis && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "retail-clerk-performance-analysis" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/retail-clerk-performance-analysis into .cursor/skills/retail-clerk-performance-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "retail-clerk-performance-analysis", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/LeoYeAI/openclaw-master-skills.git --path skills/retail-clerk-performance-analysis--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add LeoYeAI/openclaw-master-skills --skill retail-clerk-performance-analysis -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install LeoYeAI/openclaw-master-skills retail-clerk-performance-analysis --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/retail-clerk-performance-analysis .gemini/skills/retail-clerk-performance-analysis && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "retail-clerk-performance-analysis" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/retail-clerk-performance-analysis into .gemini/skills/retail-clerk-performance-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "retail-clerk-performance-analysis", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install LeoYeAI/openclaw-master-skills retail-clerk-performance-analysisInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add LeoYeAI/openclaw-master-skills --skill retail-clerk-performance-analysis -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/retail-clerk-performance-analysis .github/skills/retail-clerk-performance-analysis && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "retail-clerk-performance-analysis" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/retail-clerk-performance-analysis into .github/skills/retail-clerk-performance-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "retail-clerk-performance-analysis", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add LeoYeAI/openclaw-master-skills --skill retail-clerk-performance-analysis -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install LeoYeAI/openclaw-master-skills retail-clerk-performance-analysis --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/retail-clerk-performance-analysis .opencode/skills/retail-clerk-performance-analysis && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "retail-clerk-performance-analysis" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/retail-clerk-performance-analysis into .opencode/skills/retail-clerk-performance-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "retail-clerk-performance-analysis", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
retail-clerk-performance-analysis导购个人业绩深度分析工具。支持普通门店(POS数据)和AIoT门店(POS+AIoT数据). An agent skill from LeoYeAI/openclaw-master-skills.
Retail Clerk Performance Analysis is an agent skill from LeoYeAI/openclaw-master-skills. 导购个人业绩深度分析工具。支持普通门店(POS数据)和AIoT门店(POS+AIoT数据)。 输出导购个人详细诊断报告,包含: 1. 核心业绩指标(销售额、排名、业绩占比) 2. 雷达图能力对比(6维能力 vs 门店平均) 3. 商品特征分析(品类/价格带/包型/颜色/新品偏好) 4. Top5 SKU爆品分析(门店贡献率、SPU集中度、上市时间) 5. 订单结构分析(折扣/连带/会员结构) 6. AIoT高试用低转化分析(仅AIoT门店) 7. AIoT客户漏斗分析(仅AIoT门店) 8. 14天销售趋势分析 9. 综合诊断与行动建议 触发条件: - 用户询问导购业绩(如"李翠业绩怎么样") - 用户分析导购能力(如"导购销售能力如何") - 用户需要导购诊断(如"导购有什么问题")
Its SKILL.md is about 3.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files (for example `USAGE.md`, `_meta.json` and `analyze.py`).
The repository describes itself as: 🧠 Curated collection of 1209+ best OpenClaw skills — weekly updated by MyClaw.ai. The licence is MIT.
10 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit e5199b5. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Ships script files (Python), which the agent can run.
From the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Retail Clerk Performance Analysis loads about 3.1k tokens when it runs. Until then it costs about 96 tokens; SKILL.md has 536 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
The full file from LeoYeAI/openclaw-master-skills at commit e5199b5, republished under its MIT licence (© LeoYeAI). 536 words, ~3,118 tokens.
.claude/skills/retail-clerk-performance-analysis/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.clerk-performance-analysis
对单个导购进行深度业绩分析,支持两种门店类型:
输出导购个人的详细诊断报告,包含业绩指标、商品特征、订单结构、AIoT转化分析等多维度分析。
详见 USAGE.md 获取完整使用指南。
import sys
sys.path.insert(0, '~/.openclaw/skills/clerk-performance-analysis')
from analyze import analyze
# 执行分析(Skill自动判断门店类型)
result = analyze(
store_id="416759_1714379448487",
guide_name="李翠",
from_date="2026-03-01",
to_date="2026-03-25"
)
# 查看结果
print(f"销售额: ¥{result['core_metrics']['sales']['amount']:,.0f}")
print(f"排名: #{result['core_metrics']['sales']['rank']}")GET /api/v1/guide/detail?guideName={name}&storeId={id}&fromDate={from}&toDate={to}返回数据结构:
guideOverallPerformance - 导购整体表现(含排名、雷达图)featureDistribution - 商品特征分布(品类/价格带/包型/颜色/上市日期)skuRanking - SKU销售排行GET /api/v1/guide/order-analysis?storeId={id}&fromDate={from}&toDate={to}&guideName={name}返回数据结构:
OrderDiscounts - 订单折扣分布OrderAttachs - 订单连带分布OrderMembers - 订单会员结构GET /api/v1/guide/high-trial-low-conversion?guideName={name}&storeId={id}&fromDate={from}&toDate={to}返回数据结构:
highTrialLowConversion - 高试用低转化商品列表goodsName - 商品名称goodsModelCode - 款号trialCount - 试用次数dealCount - 成交件数conversionRate - 转化率standardPrice - 标准价GET /api/v1/guide/customer-funnel?guideName={name}&storeId={id}&fromDate={from}&toDate={to}返回数据结构:
customerFunnel - 客户分层漏斗数据有效客户 - 有交互行为的客户总数普通客户 - 无试用行为的客户潜在客户 - 有普通试用的客户意向客户 - 有深度试用的客户成交客户 - 有成交的客户6维能力对比(导购 vs 门店平均):
重要说明:
high-trial-low-conversion返回的是门店整体数据,非个人数据。边界情况处理:
| 情况 | 现象 | 处理方式 |
|---|---|---|
| 未佩戴工牌 | 所有客户显示为成交客户 | 正常,提示"未佩戴电子工牌" |
| 信息未维护 | 客户漏斗全为0 | 警告"AIoT信息未维护",高试用低转化数据为门店整体 |
前提条件:
customer-funnel 数据正常(不全为0)分析维度:
触发培训建议:
import sys
sys.path.insert(0, '~/.openclaw/skills/clerk-performance-analysis')
from analyze import analyze, batch_analyze
# 分析单个导购
result = analyze(
store_id="416759_1714379448487",
guide_name="李翠",
from_date="2026-03-01",
to_date="2026-03-25"
)
# 批量分析多个导购
results = batch_analyze(
store_id="416759_1714379448487",
guide_names=["李翠", "杨丽", "赵泽瑞"],
from_date="2026-03-01",
to_date="2026-03-25"
)| 参数 | 类型 | 必填 | 说明 |
|---|---|---|---|
store_id | str | 是 | 门店ID |
guide_name | str | 是 | 导购姓名 |
from_date | str | 是 | 分析开始日期 (YYYY-MM-DD) |
to_date | str | 是 | 分析结束日期 (YYYY-MM-DD) |
{
"status": "ok",
"store_id": "416759_1714379448487",
"guide_name": "李翠",
"analysis_period": {"from": "2026-03-01", "to": "2026-03-25"},
"core_metrics": {
"sales": {"amount": 64559, "rank": 2, "share": 20.6},
"orders": {"count": 96, "rank": 2},
"atv": {"value": 672.49, "rank": 3},
"attach": {"qty_ratio": 1.48, "sku_ratio": 1.43},
"new_customers": {"count": 29, "rank": 1},
"efficiency": {"value": 3398, "rank": 1, "order_days": 19}
},
"radar_analysis": {
"salesAmount": {"guide": 64559, "store_avg": 43391, "ratio": 1.49, "gap": 21168},
"effectiveOrderCount": {"guide": 96, "store_avg": 70, "ratio": 1.37, "gap": 26},
"newCustomerCount": {"guide": 29, "store_avg": 16, "ratio": 1.81, "gap": 13},
"customerUnitPrice": {"guide": 672, "store_avg": 655, "ratio": 1.03, "gap": 17},
"qtyUnitPrice": {"guide": 455, "store_avg": 450, "ratio": 1.01, "gap": 5},
"attachQtyRatio": {"guide": 1.48, "store_avg": 1.48, "ratio": 1.0, "gap": 0}
},
"feature_analysis": {
"category": {"top": "女包", "top_percentage": 78, "distribution": [...]},
"price_range": {"top": "1000-1200", "top_percentage": 30, "high_price_ratio": 65},
"shape": {"top": "方包", "top_percentage": 30},
"color": {"top": "黑色系", "top_percentage": 37},
"new_items": {"ratio": 30}
},
"sku_analysis": {
"top5_skus": [
{
"rank": 1,
"name": "心遥",
"code": "H51556659",
"sales": 3510.1,
"qty": 3,
"avg_price": 1170.03,
"contribute_rate": 100,
"standard_price": 1299,
"launch_date": "2025/7/1",
"launch_year": 2025,
"launch_month": 7,
"is_new": false,
"months_diff": 8
}
],
"total_skus": 5,
"top5_total_sales": 13161.13,
"top5_total_qty": 12,
"spu_concentration": {
"total_spu": 4,
"spu_list": [
{"name": "心遥", "sku_count": 2, "sales": 5444.12, "qty": 5},
{"name": "舒珀", "sku_count": 1, "sales": 3409.05, "qty": 3}
]
},
"launch_date_analysis": {
"total_items": 5,
"new_items_count": 1,
"new_items_ratio": 20.0,
"launch_dates": [...]
},
"new_items": [
{
"name": "鞣迹",
"code": "H51457102",
"launch_date": "2025/12/1",
"months_diff": 3,
"is_new": true
}
]
},
"order_analysis": {
"discount": {
"main_range": "8-9折",
"main_share": 55.2,
"low_discount_ratio": 27.6,
"total_orders": 98
},
"attach": {
"single_item_ratio": 65.3,
"multi_item_ratio": 12.2,
"avg_attach": 1.50,
"total_orders": 98
},
"member": {
"old_member_ratio": 64.2,
"new_member_ratio": 33.3,
"non_member_ratio": 2.3,
"total_customers": 89
}
},
"aiot_analysis": {
"is_aiot_store": true,
"item_count": 5,
"total_trials": 14,
"items": [
{
"name": "心遥(2)",
"code": "H51757021",
"trial_count": 3,
"deal_count": 0,
"conversion_rate": 0,
"price": 1299
}
]
},
"funnel_analysis": {
"is_aiot_store": true,
"effective_customers": 42,
"deal_customers": 42,
"conversion_rate": 100.0,
"badge_not_worn": true,
"note": "导购未佩戴电子工牌,仅记录成交客户",
"funnel": {
"有效客户": {"value": 42, "percentage": 100, "trend": "down", "link_relative_rate": 28.0},
"普通客户": {"value": 0, "percentage": 0},
"潜在客户": {"value": 0, "percentage": 0},
"意向客户": {"value": 0, "percentage": 0},
"成交客户": {"value": 42, "percentage": 100, "trend": "down", "link_relative_rate": 22.0}
}
},
"findings": [...],
"recommendations": [...]
}| 指标 | 字段 | 说明 |
|---|---|---|
| 销售额 | sales.amount | 销售总额 |
| 业绩排名 | sales.rank | 门店内排名 |
| 业绩占比 | sales.share | 占门店总业绩比例 |
| 订单数 | orders.count | 有效订单数 |
| 客单价 | atv.value | 平均客单价 |
| 连带率 | attach.qty_ratio | 数量连带率 |
| 新客数 | new_customers.count | 新增会员数 |
| 人效值 | efficiency.value | 日均业绩贡献 |
| 有订单天数 | efficiency.order_days | 统计周期内有成交的天数 |
| 指标 | 说明 |
|---|---|
ratio | 导购值 / 门店平均值,>1表示优于平均 |
gap | 导购与门店平均的绝对差距 |
| 指标 | 说明 |
|---|---|
top | 占比最高的品类/价格带/包型/颜色 |
top_percentage | 占比百分比 |
high_price_ratio | 800元以上商品销售占比 |
new_items.ratio | 2026年新品销售占比 |
| 指标 | 字段 | 说明 |
|---|---|---|
| 主销折扣 | order_analysis.discount.main_range | 占比最高的折扣区间 |
| 低折扣占比 | order_analysis.discount.low_discount_ratio | 6折以下订单占比 |
| 1件单占比 | order_analysis.attach.single_item_ratio | 单件订单占比 |
| 多连带占比 | order_analysis.attach.multi_item_ratio | ≥3件订单占比 |
| 平均连带 | order_analysis.attach.avg_attach | 平均连带件数 |
| 老会员占比 | order_analysis.member.old_member_ratio | 老会员销售占比 |
| 新会员占比 | order_analysis.member.new_member_ratio | 新会员销售占比 |
| 指标 | 字段 | 说明 |
|---|---|---|
| 是否AIoT门店 | aiot_analysis.is_aiot_store | 是否有AIoT数据 |
| 高试用低转化商品数 | aiot_analysis.item_count | 试用>0但成交=0的商品数 |
| 总试用次数 | aiot_analysis.total_trials | 这些商品的总试用次数 |
| 商品名称 | aiot_analysis.items[].name | 高试用低转化商品名称 |
| 试用次数 | aiot_analysis.items[].trial_count | 该商品被试用次数 |
| 成交件数 | aiot_analysis.items[].deal_count | 该商品成交件数 |
| 转化率 | aiot_analysis.items[].conversion_rate | 试用转化率 |
| 问题 | 判断条件 | 严重程度 |
|---|---|---|
| 业绩排名靠后 | 排名 > 3 | 🟡 中 |
| 新客获取能力不足 | 新客数 < 门店平均70% | 🟡 中 |
| 新客获取能力突出 | 新客数 > 门店平均150% | 🟢 低(优势) |
| 客单价偏低 | 客单价 < 门店平均85% | 🟡 中 |
| 品类过于集中 | 单一品类占比 > 70% | 🟡 中 |
| 高客单销售弱 | 800元以上占比 < 30% | 🟡 中 |
| 连带销售能力不足 | 1件单占比 > 60% | 🟡 中 |
| 低折扣订单占比过高 | 6折以下订单 > 20% | 🟡 中 |
| 导购AIoT信息未维护 | 客户漏斗全为0 | 🟡 中(仅AIoT) |
| 高试用低转化商品多 | ≥3个高试用低转化商品 | 🟡 中(仅AIoT) |
| 新品试用转化差 | 高试用低转化中新品≥2个 | 🟡 中(仅AIoT) |
| 有效客户大幅下降 | 环比下降>20% | 🔴 高(仅AIoT) |
| 无销售天数过多 | 14天中≥5天无销售 | 🟡 中 |
| 日均销售额偏低 | 日均<¥1000 | 🟡 中 |
核心业绩:
雷达图对比:
商品特征:
订单结构:
爆品分析(Top5 SKU):
| 排名 | 款号 | 名称 | 销量 | 销售额 | 均价 | 门店贡献率 | 上市时间 | 新品 |
|---|---|---|---|---|---|---|---|---|
| #1 | H51556659 | 心遥 | 3件 | ¥3,510 | ¥1,170 | 100% | 2025/7 | |
| #2 | H51456675 | 舒珀 | 3件 | ¥3,409 | ¥1,136 | 100% | 2025/7 | |
| #3 | H51457102 | 鞣迹 | 2件 | ¥2,174 | ¥1,087 | 69% | 2025/12 | ✓ |
| #4 | H60101090 | 爱旅 | 2件 | ¥2,134 | ¥1,067 | 100% | 2025/7 | |
| #5 | H51556656 | 心遥 | 2件 | ¥1,934 | ¥967 | 34% | 2025/7 |
Top5合计: 销量12件, 销售额¥13,161(占总业绩20.4%)
门店贡献率 = 导购该SKU销售额 / 门店该SKU总销售额
SPU集中度:
上市时间偏好:
AIoT高试用低转化分析(仅AIoT门店):
AIoT客户漏斗分析(仅AIoT门店):
诊断发现:
行动建议:
v3.1.0 - 导购个人业绩深度分析(新增销售额Top5与高试用低转化Top5对比分析)
© LeoYeAI, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 3 other files in skills/retail-clerk-performance-analysis of LeoYeAI/openclaw-master-skills.
Open the folder on GitHubat commit e5199b5
Retail Clerk Performance Analysis next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Retail Clerk Performance Analysis this skillLeoYeAI/openclaw-master-skills | 2.2k | — | ~3.1k | Automated safety check: Pass | MIT | |
| Clerk Authdavila7/claude-code-templates | 33k | 5 repos | ~376 | Automated safety check: Pass | MIT | |
| Clerk Authsickn33/agentic-awesome-skills | 47k | 2 repos | ~355 | Automated safety check: Pass | MIT | |
| ClerkAnil-matcha/awesome-muse-connectors | 1.3k | — | ~550 | Automated safety check: Pass | MIT | |
| Retail Product Search Agentgoogle/adk-recipes | 10k | — | ~3k | Automated safety check: Pass | Apache-2.0 | |
| Clerk Observabilityjeremylongshore/tons-of-skills-marketplace | 2.8k | — | ~2.1k | Automated safety check: Pass | MIT |
davila7/claude-code-templates
Expert patterns for Clerk auth implementation, middleware, organizations, webhooks, and user sync Use when: adding authentication, clerk auth, user authentication, sign in, sign up.
sickn33/agentic-awesome-skills
Expert patterns for Clerk auth implementation, middleware, organizations, webhooks, and user sync
Anil-matcha/awesome-muse-connectors
Read and write Clerk: list users, look up a user, create, update, and delete users.
google/adk-recipes
Builds a retail product search agent on Google Cloud, from catalog ingestion into BigQuery and Vector Search to ADK scaffolding, evaluation and Cloud Run deployment.
jeremylongshore/tons-of-skills-marketplace
Implement monitoring, logging, and observability for Clerk authentication.
digoal/blog
Write and save Chinese beginner-friendly paid manual subsections for "全品种操盘手册", a retail-investor education/subscription product covering the full 17-chapter + appendix outline from digoal's…
LeoYeAI/openclaw-master-skills
Manages pipelines on a DevOps quality and efficiency platform through its OpenAPI: list workspaces and templates, create, update, run and cancel pipelines, and read run records.
LeoYeAI/openclaw-master-skills
Patches OpenClaw's Feishu extension so an edited document triggers an isolated agent session that reads the doc and replies inline, turning it into a live chat space.
LeoYeAI/openclaw-master-skills
Multi-context memory management system for OpenClaw agents with group-isolated storage, global shared memory, workspace organization, and group-specific skills isolation.
LeoYeAI/openclaw-master-skills
Runs a brand's AI-search visibility work end to end: diagnosing how AI platforms represent it, repositioning it, producing AI-optimized content and monitoring ongoing mentions.
LeoYeAI/openclaw-master-skills
Installs and authenticates the gws CLI, then automates Gmail, Drive, Sheets, Calendar, Docs, Chat and Tasks with ready-made recipes, persona bundles and security audits.
LeoYeAI/openclaw-master-skills
Runs four advisor roles, a fitness coach, nutritionist, data analyst and TCM practitioner, to build a health profile and track workouts, diet and wellness over time.
导购个人业绩深度分析工具。支持普通门店(POS数据)和AIoT门店(POS+AIoT数据). An agent skill from LeoYeAI/openclaw-master-skills. Retail Clerk Performance Analysis is an agent skill from LeoYeAI/openclaw-master-skills. 导购个人业绩深度分析工具。支持普通门店(POS数据)和AIoT门店(POS+AIoT数据)。 输出导购个人详细诊断报告,包含: 1.
Run `npx skills add LeoYeAI/openclaw-master-skills --skill retail-clerk-performance-analysis -a claude-code`. Or copy the skill folder (skills/retail-clerk-performance-analysis in LeoYeAI/openclaw-master-skills) into .claude/skills/retail-clerk-performance-analysis in your project. Claude Code loads it when a task matches its description.
Run `npx skills add LeoYeAI/openclaw-master-skills --skill retail-clerk-performance-analysis -a codex`. Or copy the skill folder (skills/retail-clerk-performance-analysis in LeoYeAI/openclaw-master-skills) into .agents/skills/retail-clerk-performance-analysis in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add LeoYeAI/openclaw-master-skills --skill retail-clerk-performance-analysis -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/retail-clerk-performance-analysis, .gemini/skills/retail-clerk-performance-analysis, .github/skills/retail-clerk-performance-analysis and .opencode/skills/retail-clerk-performance-analysis in your project.
Going by SKILL.md and its folder, Retail Clerk Performance Analysis needs Python for the scripts in its folder. Our summary lists: Python 3.
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
Retail Clerk Performance Analysis is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 3.1k tokens (SKILL.md is roughly 12k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Retail Clerk Performance Analysis: Clerk Auth (davila7/claude-code-templates, 33k stars), Clerk Auth (sickn33/agentic-awesome-skills, 47k stars), Clerk (Anil-matcha/awesome-muse-connectors, 1.3k stars) and Retail Product Search Agent (google/adk-recipes, 10k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
LeoYeAI (a GitHub user) maintains it in LeoYeAI/openclaw-master-skills, which has 2,161 GitHub stars. The repository holds 1,235 skills in this directory. The repository was last updated on July 20, 2026.
Source: LeoYeAI/openclaw-master-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.