OpenClaw/clawhub-security-signals huggingface dataset:
Skill: agimodel/dna (version 1.0.0)

--- name: dna description: > Biological code execution and genomic intelligence system. Translates raw A-T-C-G sequences into actionable health, longevity, and pharmacogenomic protocols. The bridge between your biological identity and your agent's decision-making engine. --- # DNA: The Biological Operating System ## Philosophy In the agentic era, your DNA is the most permanent code you own. It is the hard-coded instruction set of your physical existence. DNA Brain allows your agent to read this code, not just as data, but as a dynamic blueprint for optimizing performance, longevity, and wellness. --- ## Bio-Logic Architecture ```BIO_STACK = { "ingestion": "Direct interpretation of raw FASTQ/VCF genomic data", "privacy": "Zero-Knowledge Processing — DNA never leaves the encrypted enclave", "logic": "Multi-variate SNP analysis across 2026 clinical grade databases", "output": "Non-medical biological optimization protocols" }``` --- ## Genomic Primitives ```GENOMIC_FUNCTIONS = { "longevity": { "focus": "Epigenetic clock synchronization and telomere maintenance", "trigger": "Analyze my biological age vs. chronological age" }, "nutrigenomics": { "focus": "Genetic metabolic response to specific micro/macro nutrients", "trigger": "Optimize my diet based on my FTO and MC4R gene variants" }, "pharmacogenomics": { "focus": "Predictive drug response and cytochrome P450 enzyme efficiency", "trigger": "Check drug-gene compatibility for my current prescription" } }``` --- ## The Zero-Trust Privacy Protocol > "My biological data is my ultimate private key." 1. **Local Decryption**: Genomic files are decrypted only during the active inference session. 2. **Feature Extraction**: Only relevant genetic markers (SNPs) are extracted; the rest of the sequence is purged. 3. **Anonymous Benchmarking**: All population-level comparisons are performed on anonymized, aggregated datasets. --- ## Use Cases - **Morning Briefing**: "Based on your circadian rhythm genes (CLOCK/PER3), your peak cognitive window today is 10:15 AM." - **Supplementation**: "Given your high MTHFR risk, switch to methylated B12." - **Risk Mitigation**: "Monitor inflammation markers (CRP) more closely due to your genetic predisposition." ---


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: hekaiii/live-photo-maker (version 1.0.2)

--- name: live-photo-maker description: Convert a local video into an iPhone-compatible Live Photo package (.pvt) for Apple Photos, lock-screen wallpaper use, or Live Photo sharing. Use when the user gives a short video and wants an iPhone-ready Live Photo or live wallpaper package. --- # Live Photo Maker 1. Take a local video file as input. 2. If needed, make an optimized version for iPhone wallpaper use: - upscale gently with `ffmpeg` - keep portrait aspect ratio - avoid aggressive recompression unless necessary 3. Extract a high-quality cover frame as JPG. 4. Use `makelive` to build a `.pvt` Live Photo package from the JPG + video pair. 5. If the user needs chat delivery, package the `.pvt` bundle as ZIP because `.pvt` is a macOS package directory and many messengers cannot send it directly. 6. Explain that the ZIP should be unzipped on macOS and the `.pvt` imported into Photos. ## Main features - turns a local video into an Apple Photos-importable Live Photo - can optimize video size for iPhone wallpaper use - generates a high-quality cover frame automatically - produces a `.pvt` package and optional ZIP for transfer - works well for iPhone lock-screen wallpaper workflows ## Notes - Install missing dependencies yourself when safe: `pipx`, `makelive`, `ffmpeg`. - The video quality inside the Live Photo usually stays close to the source. Perceived softness is often caused by low source resolution or lock-screen scaling. - Prefer sending both the original Live package path and the ZIP when useful.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: qqyule/seo-competitor-analysis (version 1.0.0)

--- name: seo-competitor-analysis description: Perform deep SEO competitor analysis, including keyword research, backlink checking, and content strategy mapping. Use when the user wants to analyze a website's competitors or improve their own SEO ranking by studying the competition. --- # SEO Competitor Analysis Skill This skill automates the process of identifying and analyzing SEO competitors to inform content and ranking strategies. ## Workflow 1. **Identify Competitors**: If not provided, search for the target domain and identify top-ranking sites for similar keywords. 2. **Analyze Keywords**: Use `web_search` to find ranking keywords and search volume (if available via snippets). 3. **Content Gap Analysis**: Compare the user's content with competitors to identify missing topics. 4. **Report Generation**: Summarize findings into a structured report. ## Tools to Use - `web_search`: To find competitors and their ranking content. - `web_fetch`: To extract content from competitor pages for deep analysis. - `browser`: For complex pages that require JavaScript or manual navigation patterns. ## Scripts - `scripts/competitor_finder.py`: (Optional) Logic to automate the discovery of competitors using search APIs. ## References - `references/seo_metrics_guide.md`: Definition of SEO terms and how to interpret them. - `references/report_template.md`: A standard structure for the final SEO analysis report.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: openlark/mindmap-creation (version 1.0.0)

--- name: mindmap-creation description: Mind map creation expert that produces structured, hierarchical mind maps based on user-provided topics. Breaks down complex knowledge into logically clear tree structures. --- # Mind Map Creation Expert Create structured mind maps based on user-provided topics. ## Use Cases Use for knowledge organization, study notes, project planning, brainstorming, content outline design, and similar scenarios. ## Design Workflow ``` Topic analysis → Identify key concepts → Logical grouping → Main/sub-branch design → Structured output ``` ### 1. Topic Analysis - Confirm the topic's core concept and scope - Identify the key knowledge domains within the topic - Assess complexity to determine branch depth (typically 2-3 levels) ### 2. Branch Design Principles | Principle | Description | |-----------|-------------| | MECE (Mutually Exclusive, Collectively Exhaustive) | Main branches don't overlap, together cover all essential topic content | | Peer equivalence | Same-level branch granularity is consistent | | 3-7 rule | 3-7 sub-branches per node (too few = insufficient grouping, too many = needs intermediate layer) | | General to specific | Upper levels abstract and general, lower levels detailed and concrete | | Consistent phrasing | Same-level branches use unified sentence structure (all nouns or all verb-object) | ### 3. Common Branch Structures Choose structure based on topic type (see [references/patterns.md](references/patterns.md)): - **Categorical**: Organized by category (tech stacks, subject classifications) - **Process**: Arranged by time/step (project phases, operational flows) - **Element**: Expanded by components (What/Why/How) - **Comparison**: Divided by comparison dimensions (pros/cons, solution comparison) - **Hierarchical**: From macro to micro (concept→subconcept→details) ## Output Format (Strict) ``` # [Topic] ## [Main Branch 1] - [Sub-branch 1.1] - [Sub-branch 1.2] - [Child 1.2.1] - [Child 1.2.2] ## [Main Branch 2] - [Sub-branch 2.1] - [Sub-branch 2.2] ## [Main Branch 3] ... ``` ## Rules - Topic as `#` level-1 heading - Main branches as `##` level-2 headings, 3-7 branches - Sub-branches use `-` list indentation to show hierarchy - Each level concise (≤15 characters), remove redundant modifiers - Do not add any explanation, summary, or commentary text to output - Output only the mind map structure itself


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: musketyr/openclaw-mentor (version 2.0.3)

# ⚠️ This skill has been renamed **openclaw-mentor** is now **clawbuddy-buddy**. ## Install the new skill ```bash clawhub install clawbuddy-buddy ``` ## What changed? - **Name:** openclaw-mentor → clawbuddy-buddy - **Platform:** mentor.telegraphic.app → clawbuddy.help - **Terminology:** mentor → buddy, lectures → pearls - **Tokens:** `mtr_xxx` → `buddy_xxx` ## More info - Website: https://clawbuddy.help - Docs: https://clawbuddy.help/about

path: skill.json
content: name: openclaw-mentor
version: 2.0.0
description: ⚠️ RENAMED: This skill is now clawbuddy-buddy. Install clawbuddy-buddy instead.
deprecated: yes
replacement: clawbuddy-buddy

Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: chenxuyaun/webhook-debugger (version 1.0.0)

--- name: webhook-debugger description: Test, debug, and inspect webhooks locally. Receive webhooks, inspect payloads, debug integrations, and replay requests. Essential for API development and third-party integrations. metadata: { "openclaw": { "requires": { "bins": [] }, "install": [], }, } --- # Webhook Debugger Receive, inspect, and debug webhooks locally. ## Quick Start ``` webhook listen webhook listen 3000 webhook listen --url https://myendpoint.com/webhook ``` ## Features - 🎣 Local webhook receiver - 📋 Payload inspection - 🔄 Request replay - ⏱️ Request history - 🔍 Headers & query params - ✅ Signature verification - 📤 Forward to other URLs ## Usage ### Start listener ```bash webhook listen 8080 ``` ### Inspect incoming request ``` POST /webhook Headers: { content-type: application/json } Body: { "event": "payment", "amount": 100 } ✓ Received 2024-01-15 10:30:00 ``` ### Replay request ``` webhook replay <request-id> <target-url> ``` ## Common Use Cases - Debug Stripe webhooks - Test GitHub webhooks - Inspect form submissions - Verify API payloads - Debug Zapier/Make webhooks ## Commands - `webhook listen [port]` - Start local server - `webhook list` - Show request history - `webhook show <id>` - Show request details - `webhook replay <id> <url>` - Replay to new URL - `webhook clear` - Clear history - `webhook forward <url>` - Forward to another service ## Notes - Default port: 3000 - History stored locally - Supports JSON, form-data, plain text


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: suhteevah/claude-local-bridge (version 0.1.0)

--- name: claude-local-bridge version: 0.1.0 summary: Access local repos from Claude on your phone via approval-gated MCP bridge tags: - mcp - file-access - remote-development - code-bridge - approval-gating - developer-tools author: suhteevah repository: https://github.com/suhteevah/claude-local-bridge license: MIT install: - pip install -r requirements.txt env: [] --- # Claude Local Bridge Access your local repos from Claude on your phone. Secure MCP bridge server with approval gating. ## What it does Runs a local MCP server (over SSE) that gives Claude access to your files — but only after you explicitly approve each request from a real-time dashboard. ## Tools - **browse_files** — List workspace file tree (no approval needed) - **request_file_access** — Request approval to read/write files (blocks until you decide) - **read_file** — Read an approved file's contents - **write_file** — Write to an approved file - **list_approvals** — See all current approvals - **revoke_approval** — Revoke access - **view_audit_log** — View access history ## Quick Start ```bash git clone https://github.com/suhteevah/claude-local-bridge.git cd claude-local-bridge pip install -r requirements.txt python -m app.main --roots ~/projects ``` Then connect Claude to `http://localhost:9120/mcp/sse` ## Security - Sandboxed to whitelisted directories only - Extension blocklist (.env, .pem, .key, etc.) - Path traversal prevention - Bearer token auth - Every file access requires human approval - Full audit trail ## Remote Access Use Tailscale (free), Cloudflare Tunnel (free), or NetBird (FOSS) to access from your phone. See [tunnel.md](https://github.com/suhteevah/claude-local-bridge/blob/main/tunnel.md).

path: app/mcp/__init__.py
content: """MCP (Model Context Protocol) server for Claude Local Bridge."""


path: app/middleware/auth.py
content: """Bearer token authentication middleware."""

from __future__ import annotations

from fastapi import Depends, HTTPException, Security, status
from fastapi.security import HTTPAuthorizationCredentials, HTTPBearer

_bearer = HTTPBearer()

# Will be set at startup from config
_valid_token: str = ""


def set_token(token: str) -> None:
    global _valid_token
    _valid_token = token


async def require_token(
    creds: HTTPAuthorizationCredentials = Security(_bearer),
) -> str:
    """Dependency — rejects requests without a valid bearer token."""
    if creds.credentials != _valid_token:
        raise HTTPException(
            status_code=status.HTTP_401_UNAUTHORIZED,
            detail="Invalid or missing bearer token",
        )
    return creds.credentials


path: app/middleware/__init__.py
content: from .auth import require_token, set_token


path: app/models/__init__.py
content: from .schemas import *


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (CRITICAL)
Skill: ghwyever/03-storyboard-prompter (version 1.0.4)

# 分镜提示词生成技能 ## 功能介绍 将短剧剧本场景自动转换为AI绘画专用提示词，统一画面风格与构图描述。 ## 输入参数 - scenes：剧本场景列表（必填） ## 输出结果 - shot_list：分镜提示词列表 ## 使用场景 - 剧本转AI绘画提示词 - 批量生成分镜描述 - 统一短剧视觉风格 ## 技术说明 纯本地运行，无需API、无需模型配置。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: gabriel-zz/daily-inspiration (version 1.0.0)

--- name: daily-inspiration description: 每天定时推送倪海厦、南怀瑾等国学大师的相关内容，通过飞书发送给用户。内容包括佛学解脱、气功、周易、中医等主题。用于：用户请求每天获取精神修行相关资讯。 --- # 每日灵修推送 ## 触发方式 此技能在定时任务触发时自动运行，无需用户主动请求。 ## 内容优先级 1. **倪海厦天纪** - 易经、风水、命理、阴阳五行（30%） 2. **倪海厦人纪** - 中医经典、针灸、伤寒论（25%） 3. **老庄哲学** - 道德经、庄子、逍遥自在（25%） 4. **佛学/禅定** - 南怀瑾、禅定修行（20%） ## 执行流程 1. **选择主题**：随机从四个范畴中选择（天纪30%，人纪25%，老庄25%，佛学20%） 2. **搜索内容**：使用 batch_web_search 搜索相关主题的最新或经典内容 3. **整理内容**：提取有价值的信息片段 4. **发送飞书**：使用 message 工具发送到用户 ## 内容来源 - **倪海厦天纪**：易经、阴阳五行、风水、命理、紫微斗数 - **倪海厦人纪**：针灸、黄帝内经、神农本草经、伤寒论、金匮要略 - **老庄哲学**：道德经、庄子、列子、杨朱 - 佛学：南怀瑾著作、禅宗公案 ## 输出格式 每日两条推送： **早间（6:00-8:00）**：偏重修行、心性类 **晚间（18:00-20:00）**：偏重养生、实用类 消息格式： ``` 📖 每日灵修 [标题] [内容摘要/经典语录/实用知识] 来源：xxx ```

path: scripts/daily_push.py
content: """
每日灵修推送脚本
定时执行：从南怀瑾、倪海厦等相关内容中推送佛学修行、气功周易、中医知识
"""
import random
import json
from datetime import datetime

# 内容主题库（实际执行时动态搜索）
TOPICS = {
    "morning": [
        # 解脱修行
        "南怀瑾 禅定 修行心得",
        "佛学 禅宗 经典语录",
        "打坐 静心 方法",
        "心经 解读 修行",
        "金刚经 智慧",
        # 气功
        "站桩养生 方法",
        "八段锦 健身",
        # 中医
        "倪海厦 中医思想",
    ],
    "evening": [
        # 解脱
        "南怀瑾 人生智慧",
        "佛教 放下 执着",
        # 气功周易
        "周易 阴阳 五行",
        "气功 养生 原理",
        "五行学说 应用",
        # 中医
        "中医经典 养生",
        "黄帝内经 智慧",
    ]
}

def select_topic(time_of_day: str) -> str:
    """根据时段选择主题"""
    topics = TOPICS.get(time_of_day, TOPICS["morning"])
    return random.choice(topics)

def format_message(topic: str, content: str, source: str) -> str:
    """格式化推送消息"""
    return f"""📖 每日灵修

{topic}

{content}

来源：{source}"""

if __name__ == "__main__":
    # 获取当前时间判断时段
    hour = datetime.now().hour
    time_of_day = "morning" if 6 <= hour < 12 else "evening"
    
    topic = select_topic(time_of_day)
    print(f"选择主题：{topic}")
    print(f"时段：{time_of_day}")


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: tujinsama/forecast-analysis-claw (version 1.0.0)

--- name: forecast-analysis-claw description: | 根据历史销售数据预测未来销量并生成补货建议。核心能力：(1) 销量预测 - 基于移动平均、指数平滑、Holt-Winters、Prophet 等模型自动预测未来销量；(2) 补货计算 - 结合库存参数自动计算补货触发点和建议补货量；(3) 活动预测 - 叠加促销效应系数预测大促期间销量峰值；(4) 断货预警 - 识别高风险 SKU 并推送预警。 触发场景：用户提供历史销售数据（Excel/CSV），要求预测销量、计算补货量、备战大促、分析库存风险、生成补货计划等。也适用于用户提到"预测"、"补货"、"销量预测"、"需求预测"、"库存预测"、"大促备货"、"季节性"、"历史数据"、"销售曲线"、"备货计划"等关键词。 --- # forecast-analysis-claw 根据历史销售数据预测未来销量，自动计算补货建议，防止断货和积压。 ## 工作流程 ### 1. 数据准备 **必需字段**： - `date` / 日期 / 销售日期 - `sku` / SKU编码 / 商品编码 - `quantity` / 销量 / 数量 **可选字段**： - `current_stock` / 当前库存 - `in_transit` / 在途库存 - `lead_time_days` / 供应商交货周期 - `safety_days` / 安全库存天数 **数据清洗**（如需要）： ```bash python3 scripts/data-cleaner.py clean --input raw_sales.csv --output clean_sales.csv python3 scripts/data-cleaner.py detect-outliers --input sales.csv ``` ### 2. 执行预测 **单个 SKU 预测**： ```bash python3 scripts/forecast-runner.py single \ --sku SKU-001 \ --input sales.csv \ --days 30 \ --stock 500 \ --lead-time 14 \ --safety-days 7 ``` **批量预测**： ```bash python3 scripts/forecast-runner.py batch \ --input sales.csv \ --output forecast_result.csv \ --days 30 ``` ### 3. 生成报告 ```bash python3 scripts/forecast-runner.py report --forecast forecast_result.csv ``` ### 4. 评估准确率（可选） ```bash python3 scripts/forecast-runner.py evaluate \ --forecast last_month_forecast.csv \ --actual last_month_actual.csv ``` ## 预测模型 脚本会根据数据特征自动选择模型： - **移动平均法**：平稳型商品，无明显趋势 - **指数平滑法**：有趋势的商品 - **Holt-Winters**：有季节性的商品 - **Prophet**（可选）：复杂季节性 + 节假日效应 详见 `references/forecast-models.md`。 ## 补货参数 默认参数参考 `references/replenishment-params.md`： | 品类 | 安全库存 | 补货周期 | |-----|---------|---------| | 快消品 | 7天销量 | 14天 | | 服装鞋帽 | 14天销量 | 30天 | | 电子产品 | 21天销量 | 45天 | 可通过命令行参数覆盖：`--lead-time 20 --safety-days 10` ## 促销活动预测 叠加促销效应系数（参考 `references/promo-coefficients.md`）： ``` 活动预测销量 = 基准日均销量 × 拉升系数 × 活动天数 ``` 常见系数： - 双11：5x ~ 8x - 618：3x ~ 5x - 日常满减：1.3x ~ 1.8x 手动计算或在脚本中添加 `--promo-factor 6.0` 参数（需自行扩展脚本）。 ## 输出说明 **预测结果**： - `daily_avg_forecast`：日均预测销量 - `total_forecast`：预测周期总销量 - `lower_bound` / `upper_bound`：置信区间 - `model_used`：使用的预测模型 **补货建议**（如提供库存参数）： - `reorder_point`：补货触发点 - `available_stock`：当前可用库存（含在途） - `days_of_stock`：可用天数 - `replenishment_qty`：建议补货量 - `priority`：🔴 紧急 / 🟡 正常 / 🟢 可延后 ## 依赖安装 ```bash pip install pandas numpy scikit-learn pip install prophet # 可选，用于复杂预测 ``` ## 注意事项 - **最少数据量**：建议至少 30 天历史数据，90 天以上更佳 - **数据质量**：促销期间需标注，否则影响基准预测 - **预测误差**：预测结果是概率性的，需结合业务判断 - **新品预测**：无历史数据时，参考同类品系数 × 0.7 ## 与其他 Skills 协作 - **process-data-monitor-claw**：监控库存水位，触发重新预测 - **historical-data-compare-claw**：提供同比环比数据，辅助识别季节性 - **cross-platform-messenger-claw**：推送断货预警到飞书/邮件


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: seitzbg/dgx-spark-temperature (version 1.0.0)

# dgx-spark-temperature Read hardware temperature sensors on the DGX Spark via SNMP. ## When to use - User asks for body temperature, DGX Spark temp, hardware temps, how hot things are running - Any temperature/hardware health check request for the DGX Spark ## How to use Run `exec` with: ``` bash <workspace>/skills/dgx-spark-temperature/check_temperature.sh ``` The script uses: - `snmpwalk -v2c -c licpub dgx-spark1.fiber.house 1.3.6.1.4.1.2021.13.16.2.1` - Parses LM-SENSORS MIB table: `lmTempSensorsIndex`, `lmTempSensorsDevice`, `lmTempSensorsValue` - Values are in milliCelsius — divide by 1000 for °C ## Sensor mapping (16 sensors) | IDX | Sensor Name | Notes | |-----|-----------------------|---------------------------------| | 1 | asic | GPU/GB10 ASIC | | 2 | Module0 | GPU Module 0 | | 3 | mlx5-pci-0100:asic | Mellanox NIC #1 ASIC | | 4 | mlx5-pci-0100:Module0 | Mellanox NIC #1 module | | 5 | temp1 | Generic thermal sensor | | 6 | temp2 | Generic thermal sensor | | 7 | temp3 | Generic thermal sensor | | 8 | temp4 | Generic thermal sensor | | 9 | temp5 | Generic thermal sensor | | 10 | temp6 | Generic thermal sensor | | 11 | temp7 | Generic thermal sensor | | 12 | mlx5-pci-20101:asic | Mellanox NIC #3 ASIC | | 13 | mlx5-pci-0101:asic | Mellanox NIC #2 ASIC | | 14 | Composite | Overall/aggregate temp | | 15 | Sensor 1 | Additional thermal probe | | 16 | Sensor 2 | Additional thermal probe | ## File layout ``` skills/dgx-spark-temperature/ SKILL.md ← this file check_temperature.sh ← the script ``` ## Notes - Community string is `licpub` (read-only) - SNMPv2c, no auth/privacy - DGX Spark runs Ubuntu 24.04 kernel 6.17, aarch64 (NVIDIA) - Location: "Basement" (per SNMP sysLocation) - Hostname: `bseitz-spark1`


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: fangshan101-coder/yunji (version 0.1.0)

--- name: 云集返利助手 description: 云集会员制社交电商工具，提供自营+CPS双模式商品推荐、会员专属价查询和分销推广管理。 version: 0.1.0 --- # 云集返利助手 云集会员制社交电商工具。作为纳斯达克上市的社交电商平台，提供自营商品和CPS聚合双模式，会员可享受专属价格和分销佣金，覆盖美妆、食品、家居等品类。 ## 触发词 云集、云集返利、yunji、云集会员、云集分销、云集优惠 ## 计划实现能力 - **会员专属价**：云集会员商品专属价格查询 - **自营+CPS**：云集自营商品和外部CPS商品双模式 - **分销推广**：生成推广链接和素材，赚取分销佣金 - **品类推荐**：按美妆、食品、家居等品类推荐高佣商品 ## 输出格式 ``` ☁️ 云集返利查询 商品：[商品名称] 类型：[自营/CPS] 零售价：¥[价格] 会员价：¥[金额] 分销佣金：¥[金额] ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: leooooooow/shopify-tracking-auditor (version 1.0.0)

--- name: shopify-tracking-auditor description: Audit Shopify tracking integrity across UTM, pixel, CAPI, and checkout events to detect attribution leaks. Use when the user reports inconsistent ROAS data, missing conversion events, or mismatch between platform and store-side numbers. --- # Shopify Tracking Auditor ## Skill Card - **Category:** Measurement - **Core problem:** Where is tracking broken across ad click to purchase flow? - **Best for:** Performance teams debugging attribution leaks - **Expected input:** Event samples, pixel/CAPI config notes, UTM examples, checkout flow map - **Expected output:** Tracking gap audit with severity, suspected cause, and fix checklist - **Creatop handoff:** Apply fixes then rerun attribution review with creator-attribution-lite ## Workflow 1. Map funnel events from click -> landing -> add-to-cart -> checkout -> purchase. 2. Validate event naming consistency, deduplication, and timestamp continuity. 3. Identify drop-off points where measurement is missing or unreliable. 4. Produce a fix plan ordered by measurement impact. ## Output format Return in this order: 1. Executive summary (max 5 lines) 2. Priority actions (P0/P1/P2) 3. Evidence table (signal, confidence, risk) 4. 7-day execution plan ## Quality and safety rules - Distinguish data collection failures from real performance changes. - Keep findings tied to concrete event evidence. - Mark assumptions when full logs are unavailable. ## License Copyright (c) 2026 **Razestar**. This skill is provided under **CC BY-NC-SA 4.0** for non-commercial use. You may reuse and adapt it with attribution to Razestar, and share derivatives under the same license. Commercial use requires a separate paid commercial license from **Razestar**. No trademark rights are granted.

path: references/output-template.md
content: # Output Template

Use this template for consistent decision output.

## 1) Executive Summary
- Key finding:
- Primary risk:
- Recommended next action:

## 2) Priority Actions
| Priority | Action | Expected impact | Effort | Owner |
|---|---|---|---|---|
| P0 |  |  |  |  |
| P1 |  |  |  |  |
| P2 |  |  |  |  |

## 3) Evidence Table
| Signal | Source | Confidence | Notes |
|---|---|---|---|

## 4) 7-Day Plan
- D1:
- D2:
- D3:
- D4:
- D5:
- D6:
- D7:


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)