An advanced operations and data analysis skill for local restaurant businesses, specializing in influencer marketing, ad ROI calibration, menu pricing optimization, and platform appeal responses across Chinese local-life platforms.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install restaurant-ops-analysis
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install restaurant-ops-analysis using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Restaurant Operations Data Analyst?
The Restaurant Operations Data Analyst is a powerful, specialized addition to your AI agent capabilities, designed for restaurant businesses operating on platforms like Douyin, Meituan, Dianping, and Xiaohongshu. This capability is part of the broader ecosystem of Openclaw Skills, enabling AI agents to seamlessly analyze complex local-life metrics, evaluate influencer video performance, calibrate ad spend, optimize menu pricing tiers, and generate bulletproof platform appeal drafts.
By integrating this skill, your agent moves beyond generic data processing to enforce strict, industry-proven operational heuristics. For instance, it automatically prioritizes the voucher refund rate as a critical metric, classifying structural behaviors versus experience issues, and calibrating ROI figures to reflect true net values. It acts as a virtual CMO and operations director, delivering concise, action-oriented conclusions followed by robust data visualization tables.
Restaurant Operations Data Analyst Use Cases
- Analyzing Douyin influencer performance data to identify high-reach, low-conversion traffic waste.
- Calibrating local promotion (local-push) ad spend to calculate true redemption ROI versus superficial platform metrics.
- Deconstructing product and combo meal performance to discover "refund engines" and restructure pricing ladders.
- Drafting point-by-point, professional appeal responses to platform penalties, bad reviews, or customer complaints.
- Developing comprehensive, phase-based omnichannel marketing plans for multi-store local life operations.
How Restaurant Operations Data Analyst Works
- Trigger Recognition: The agent identifies operation-specific user requests regarding store metrics, influencer data, ad spend, combo meal performance, or platform penalties.
- Reference Retrieval: Before commencing any analysis, the skill dynamically reads the quarterly-updated knowledge base, including
references/benchmarks.mdfor industry benchmarks,references/platform-rules.mdfor platform algorithms, andreferences/content-templates.mdfor templates. - Data Cleaning: Raw inputs are processed—commas are stripped from numeric strings, percentages are parsed to floats, date columns are unified into datetime formats, and video IDs are de-duplicated.
- Logical Evaluation: The skill executes custom analytical pipelines depending on the active module, such as executing a 4-quadrant efficiency matrix on ad spend or calculating True Redemption ROI.
- Reporting: Outputs a conclusion-first executive brief in Markdown tables with standardized precision (1 decimal for percentages, clean integers or 2 decimals for currency).
Restaurant Operations Data Analyst Setup
To add this capability to your agent, register it within your Openclaw workspace configuration.
First, clone or download the skill directory containing the SKILL.md and reference schemas to your workspace's skill store:
mkdir -p skills/restaurant-ops-analysis/references
cp -r references/ skills/restaurant-ops-analysis/references/
Next, verify that your agent framework has access to the updatable knowledge base directory. Your agent can then naturally invoke the Openclaw Skills suite to process local life data files or text-based prompts:
# Run a quick check to ensure reference files are readable by the agent
cat skills/restaurant-ops-analysis/references/benchmarks.md
Restaurant Operations Data Analyst Data Schema & Taxonomy
The skill processes and produces clean datasets structured around the following metrics and files:
Core Operational Metrics
| Metric Name | Formula / Definition | Output Precision |
|---|---|---|
| 1,000-View GMV | (Cumulative Direct Revenue / Cumulative Views) * 1000 |
Integer or 2 Decimals |
| Voucher Refund Rate | Refunded Vouchers / Purchased Vouchers |
1 Decimal (%) |
| Redemption Rate | Redeemed Vouchers / Purchased Vouchers |
1 Decimal (%) |
| CPM | (Ad Spend / Impressions) * 1000 |
2 Decimals |
| True Redemption ROI | Surface ROI * Redemption Rate |
2 Decimals |
| Net ROI | Surface ROI * (Redemption Rate - Refund Rate) |
2 Decimals |
Knowledge Base References
references/benchmarks.md: Contains local-life thresholds such as safety lines for refund rates (healthy < 15%, alarm > 25%), minimum 5s video completion rate standards (Pass >= 18%, Excellent >= 22%), and influencer selection thresholds.references/platform-rules.md: Houses the latest algorithmic specifications from Meituan, Douyin POI weight rules, and platform risk control parameters.references/content-templates.md: Offers tested video title structures, keyword templates, and complaint resolution formats.
Restaurant Operations Data Analyst Advanced Features
- Dynamic ROI Calibration Engine: Automatically discounts surface-level ad platform ROIs based on real redemption and refund behaviors, preventing over-investment in low-quality leads.
- Four-Quadrant Creative Efficiency Matrix: Classifies ad videos based on CPM and CVR, automatically sorting creatives into scale, optimize cost, observe, or immediate-halt lists.
- Decentralized Knowledge Sync: Decouples industry thresholds and platform algorithm rules into isolated reference files, allowing you to update Meituan or Douyin policies without altering the core logic.
- Contextual Platform Lock: Confines analyses strictly to the target platform requested by the user, avoiding irrelevant cross-platform noise unless requested.
- Compliant Platform Appeal Synthesizer: Converts combative disputes into high-probability win appeals by aligning with platform-specific audit guidelines and factual rebuttal frameworks under 500 words.
SKILL.md
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