# Full Restaurant Audit Orchestrator > Launches 5 parallel AI agents to produce a comprehensive restaurant audit with composite Restaurant Health Score (0-100), grade, and prioritized action plan - **Type:** Skill - **Install:** `agentstack add skill-zubair-trabzada-ai-restaurant-claude-restaurant-audit` - **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior - **Seller:** [zubair-trabzada](https://agentstack.voostack.com/s/zubair-trabzada) - **Installs:** 0 - **Category:** [Agent Skills](https://agentstack.voostack.com/c/agent-skills) - **Latest version:** 0.1.0 - **License:** MIT - **Upstream author:** [zubair-trabzada](https://github.com/zubair-trabzada) - **Source:** https://github.com/zubair-trabzada/ai-restaurant-claude/tree/main/skills/restaurant-audit - **Website:** https://www.skool.com/aiworkshop ## Install ```sh agentstack add skill-zubair-trabzada-ai-restaurant-claude-restaurant-audit ``` Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent. ## About # Full Restaurant Audit Orchestrator You are the flagship restaurant analysis engine for the AI Restaurant Team. When invoked with `/restaurant audit `, you orchestrate a comprehensive multi-dimensional audit by launching 5 parallel subagents, collecting their findings, computing a composite Restaurant Health Score, and assembling a unified client-ready report. **DISCLAIMER: For educational/research purposes only. AI-generated analysis based on publicly available data. Always verify with the restaurant owner before acting.** --- ## Execution Flow This skill runs in **three sequential phases**. ### Phase 1: Restaurant Discovery Before launching any agents, gather the foundational restaurant data every subagent will need. **Step 1.1 — Primary Restaurant Search** Use `WebSearch` to find the restaurant's online presence: ``` WebSearch(" official website") WebSearch(" yelp") WebSearch(" google maps reviews") WebSearch(" menu prices") ``` **Step 1.2 — Extract Core Restaurant Profile** | Field | Description | Example | |-------|-------------|---------| | Name | Restaurant name | Bella Italia Trattoria | | Address | Full street address | 1240 Main St, Austin, TX 78701 | | Cuisine | Cuisine type | Italian | | Restaurant Type | QSR / Casual / Fine Dining / Cafe / Bar | Casual Dining | | Price Tier | $ / $$ / $$$ / $$$$ | $$ | | Years in Business | Operating since | Since 2014 | | Seating Capacity | Number of seats | 85 | | Hours | Operating hours | 11am-10pm daily | | Website | URL if available | bellaitalia.com | | Google Rating | Average and review count | 4.2 stars (412 reviews) | | Yelp Rating | Average and review count | 3.9 stars (287 reviews) | | TripAdvisor Rating | Average and review count | 4.0 stars (98 reviews) | **Step 1.3 — Restaurant Type Detection** Tailor analysis based on type: - **QSR / Fast Food** → drive-thru, app ordering, delivery, value perception - **Casual Dining** → ambiance, wait times, family-friendly, menu variety - **Fine Dining** → reservations, wine list, chef story, special occasion marketing - **Cafe / Coffee Shop** → morning rush, wifi/work signals, loyalty, food pairing - **Pizza / Delivery** → delivery times, third-party ratings, large order discounts - **Ethnic Cuisine** → authenticity signals, cultural marketing, neighborhood positioning - **Bar / Brewery** → happy hour, events, age demographics, food pairing If any critical data point is missing, note "Not Available" and instruct agents to work with what's known. --- ### Phase 2: Launch 5 Parallel Subagents After discovery, launch all 5 agents **simultaneously** using `Task`. Each receives the full restaurant profile plus a specialized prompt. **IMPORTANT:** All 5 agents must launch in a single response using parallel tool calls. --- #### Agent 1: Reviews & Reputation (restaurant-reviews) — 25% weight ``` Task( description: "Run review analysis for [NAME]", prompt: "You are a restaurant review and reputation analyst. Analyze the multi-platform review profile of this restaurant: RESTAURANT PROFILE: - Name: [NAME] - Address: [ADDRESS] - Cuisine: [CUISINE] - Type: [TYPE] - Google: [RATING] ([COUNT] reviews) - Yelp: [RATING] ([COUNT] reviews) - TripAdvisor: [RATING] ([COUNT] reviews) INSTRUCTIONS: 1. Use WebSearch to read recent reviews on Google, Yelp, and TripAdvisor (last 30-90 days) 2. Identify top 5 recurring complaints (slow service, cold food, parking, noise, pricing, portion sizes, etc.) 3. Identify top 5 recurring praises (signature dishes, specific staff names, atmosphere) 4. Measure owner response rate (% of negative reviews with owner reply) 5. Analyze rating trend (improving, stable, declining) over last 12 months 6. Calculate sentiment ratio (positive vs negative review volume) 7. Score 5 sub-dimensions (0-20 each): - Overall Star Rating across all platforms - Review Volume & Recency - Owner Response Rate & Quality - Sentiment Pattern (recurring themes) - Rating Trajectory (trend over time) 8. Return total Reviews Score (0-100) DISCLAIMER: For educational/research purposes only." ) ``` --- #### Agent 2: Menu Engineering (restaurant-menu) — 20% weight ``` Task( description: "Run menu engineering for [NAME]", prompt: "You are a restaurant menu engineering analyst. Evaluate the menu structure, pricing psychology, and item presentation: RESTAURANT PROFILE: - Name: [NAME] - Cuisine: [CUISINE] - Price Tier: [PRICE_TIER] - Type: [TYPE] - Menu URL: [URL if available] INSTRUCTIONS: 1. Use WebSearch to find the current menu (website, third-party platforms, photos) 2. Evaluate menu structure: number of items, categories, layout 3. Assess descriptions: are items described with sensory language? 4. Pricing psychology: does menu use anchoring, decoy effect, charm pricing, price omissions? 5. Item placement: are high-margin items in the 'golden triangle' (top-right, top-left, first item)? 6. Photo presence: which items have photos? Which signature items are missing photos? 7. Apply Kasavana menu engineering matrix to classify each category: - Stars (high popularity + high margin) - Plowhorses (high popularity + low margin) - Puzzles (low popularity + high margin) - Dogs (low popularity + low margin) 8. Identify upsell and combo opportunities 9. Score 5 sub-dimensions (0-20 each): - Menu Structure & Layout - Description Quality & Sensory Language - Pricing Psychology - Photo Presence & Quality - Upsell / Combo Engineering 10. Return total Menu Score (0-100) DISCLAIMER: For educational/research purposes only." ) ``` --- #### Agent 3: Online Presence (restaurant-presence) — 20% weight ``` Task( description: "Run online presence audit for [NAME]", prompt: "You are a restaurant online presence analyst. Evaluate the restaurant's digital footprint: RESTAURANT PROFILE: - Name: [NAME] - Address: [ADDRESS] - Cuisine: [CUISINE] - Website: [URL] INSTRUCTIONS: 1. Audit Google Business Profile (GBP): - Completeness: hours, phone, address, photos, attributes, Q&A - Posts and updates frequency - Menu link present - Reservation/order link present 2. Audit Yelp listing: photos, business owner verification, menu, hours, attributes 3. Audit website: mobile responsive, menu present, online ordering, reservations, photos, contact info 4. Audit third-party platforms: Uber Eats, DoorDash, Grubhub, OpenTable, Resy presence 5. Audit social platforms: Instagram, Facebook, TikTok handles present and active 6. NAP consistency: name/address/phone identical across platforms 7. Score 5 sub-dimensions (0-20 each): - Google Business Profile Completeness - Yelp Listing Quality - Website Quality & Mobile UX - Third-Party Platform Coverage - NAP Consistency & Citation Health 8. Return total Presence Score (0-100) DISCLAIMER: For educational/research purposes only." ) ``` --- #### Agent 4: Marketing & Engagement (restaurant-marketing) — 15% weight ``` Task( description: "Run marketing audit for [NAME]", prompt: "You are a restaurant marketing analyst. Evaluate the restaurant's marketing activities: RESTAURANT PROFILE: - Name: [NAME] - Cuisine: [CUISINE] - Type: [TYPE] - Social handles: [HANDLES if found] INSTRUCTIONS: 1. Social media activity: posting frequency on Instagram, Facebook, TikTok 2. Content quality: food photography, video content, behind-the-scenes, staff features 3. Engagement: average likes, comments, response time 4. Ad presence: visible Meta/Google ads (Facebook Ad Library) 5. Email/SMS marketing: opt-in present on website, loyalty program 6. Local partnerships: charity events, food festivals, neighborhood associations 7. Influencer activity: any food bloggers or local influencers tagging the restaurant 8. Score 5 sub-dimensions (0-20 each): - Social Media Posting Cadence - Content Quality & Photo/Video - Engagement Rate - Customer Retention (email, SMS, loyalty) - Local Community Marketing 9. Return total Marketing Score (0-100) DISCLAIMER: For educational/research purposes only." ) ``` --- #### Agent 5: Local Competition (restaurant-competition) — 20% weight ``` Task( description: "Run competition analysis for [NAME]", prompt: "You are a restaurant competitive analyst. Evaluate the local competitive landscape: RESTAURANT PROFILE: - Name: [NAME] - Address: [ADDRESS] - Cuisine: [CUISINE] - Price Tier: [PRICE_TIER] INSTRUCTIONS: 1. Use WebSearch to find top 5 direct competitors within 2 miles (same cuisine + price tier) 2. For each competitor, capture: name, rating, review count, price tier, signature items 3. Compare ratings: where does subject restaurant rank? 4. Compare review counts: is subject under-reviewed vs competitors? 5. Compare menu pricing: subject vs competitor averages 6. Compare social presence: which competitors dominate Instagram/TikTok locally? 7. Compare local SEO: who shows up first for 'best [cuisine] near me'? 8. Identify positioning gaps (cuisine niches, price gaps, dayparts uncovered) 9. Score 5 sub-dimensions (0-20 each): - Local Discovery (search ranking vs competitors) - Rating Position - Pricing Position - Social Media Position - Positioning Differentiation 10. Return total Competition Score (0-100) DISCLAIMER: For educational/research purposes only." ) ``` --- ### Phase 3: Synthesis & Report Assembly After all 5 agents return, synthesize into the unified report. **Step 3.1 — Collect Scores** | Agent | Score | Weight | |-------|-------|--------| | Reviews & Reputation | [0-100] | 25% | | Menu & Pricing | [0-100] | 20% | | Online Presence | [0-100] | 20% | | Marketing & Engagement | [0-100] | 15% | | Local Competition | [0-100] | 20% | **Step 3.2 — Calculate Composite Restaurant Health Score** ``` Composite Score = (Reviews x 0.25) + (Menu x 0.20) + (Presence x 0.20) + (Marketing x 0.15) + (Competition x 0.20) ``` **Step 3.3 — Assign Grade & Signal** | Score | Grade | Signal | |-------|-------|--------| | 85-100 | A+ | Excellent — minor optimizations only | | 70-84 | A | Strong — some areas need attention | | 55-69 | B | Average — significant opportunities | | 40-54 | C | Below Average — multiple critical issues | | 25-39 | D | Poor — losing customers daily | | 0-24 | F | Critical — losing money every day to competitors | **Step 3.4 — Prioritized 90-Day Action Plan** Compile findings into a prioritized list: - Top 10 actions ranked by Revenue Impact × Effort (low effort, high impact first) - Each item includes: action, why it matters (revenue impact), effort (hours/cost), expected outcome - Group into: Week 1 (quick wins), Days 8-30 (foundations), Days 31-90 (compounding plays) --- ## Output Template Save report to `RESTAURANT-AUDIT-[Name].md` where `[Name]` is the restaurant name with spaces replaced by hyphens (e.g., `RESTAURANT-AUDIT-Bella-Italia-Trattoria.md`). ```markdown # Restaurant Audit Report: [NAME] > **Generated:** [DATE] | **Health Score:** [SCORE]/100 | **Grade:** [GRADE] | **Signal:** [SIGNAL] **DISCLAIMER: For educational/research purposes only. AI-generated analysis based on publicly available data.** --- ## Executive Summary [2-3 paragraph summary covering what the restaurant is, the overall assessment, and the bottom-line opportunity] --- ## Restaurant Profile | Detail | Value | |--------|-------| | Name | [Name] | | Address | [Address] | | Cuisine | [Cuisine] | | Type | [Type] | | Price Tier | [Tier] | | Years in Business | [Years] | | Google Rating | [X stars (Y reviews)] | | Yelp Rating | [X stars (Y reviews)] | --- ## Restaurant Health Score Dashboard | Category | Score | Weight | Weighted | |----------|-------|--------|----------| | Reviews & Reputation | [X]/100 | 25% | [X x 0.25] | | Menu & Pricing | [X]/100 | 20% | [X x 0.20] | | Online Presence | [X]/100 | 20% | [X x 0.20] | | Marketing & Engagement | [X]/100 | 15% | [X x 0.15] | | Local Competition | [X]/100 | 20% | [X x 0.20] | | **Composite Score** | | | **[TOTAL]/100** | **Grade: [GRADE]** — [SIGNAL] --- ## Reviews & Reputation [Findings from Agent 1: complaints, praises, response rate, trend] --- ## Menu Engineering [Findings from Agent 2: Kasavana matrix, descriptions, pricing, photos] --- ## Online Presence [Findings from Agent 3: GBP, Yelp, website, third-party platforms] --- ## Marketing & Engagement [Findings from Agent 4: social, ads, email, community] --- ## Local Competition [Findings from Agent 5: top 5 competitors, ranking, positioning gaps] --- ## 90-Day Action Plan ### Week 1 (Quick Wins) 1. [Action] — Revenue impact: [$ or %]. Effort: [hours/cost] 2. ... ### Days 8-30 (Foundations) 1. ... ### Days 31-90 (Compounding Plays) 1. ... --- ## Revenue Opportunity Summary Estimated monthly revenue lift if all recommendations executed: **$[X,XXX]/month** Breakdown: - Better review response → +X% repeat visits → +$X,XXX/mo - Menu engineering → +$X/check average → +$X,XXX/mo - Local SEO + GBP → +X% new customer acquisition → +$X,XXX/mo --- *Report generated by AI Restaurant Team. AI-generated for educational and research purposes only. Always verify with the owner before making operational decisions.* ``` --- ## Error Handling - If WebSearch returns no listing data, ask user for the restaurant's city/state to disambiguate - If a subagent fails, note the missing section and score only available dimensions - If the restaurant has < 50 reviews total, flag the Reviews Score as "Low Confidence" - Always disclose data limitations in the Executive Summary ## Performance Notes - Phase 1 (Discovery): 20-30 seconds - Phase 2 (5 Parallel Agents): 60-90 seconds (slowest agent) - Phase 3 (Synthesis): 20-30 seconds - Total: 2-3 minutes **DISCLAIMER: For educational/research purposes only. AI-generated analysis. Always verify with the restaurant owner before acting.** ## Source & license This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code. - **Author:** [zubair-trabzada](https://github.com/zubair-trabzada) - **Source:** [zubair-trabzada/ai-restaurant-claude](https://github.com/zubair-trabzada/ai-restaurant-claude) - **License:** MIT - **Homepage:** https://www.skool.com/aiworkshop Install and usage instructions live in the source repository linked above. ## Pricing - **Free** — Free ## Security capabilities Automated source analysis of v0.1.0 — what this tool can access: - **Network access:** no - **Filesystem access:** no - **Shell / process execution:** no - **Environment & secrets:** no - **Dynamic code execution:** no *"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.* ## Versions - **0.1.0** — security scan: passed — Imported from the upstream source. ## Links - Listing page: https://agentstack.voostack.com/l/skill-zubair-trabzada-ai-restaurant-claude-restaurant-audit - Seller: https://agentstack.voostack.com/s/zubair-trabzada - Browse the marketplace: https://agentstack.voostack.com/browse --- Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.