Claude Skill

food-waste

Analyze food supply chain systems for waste reduction opportunities including shelf life prediction models, FIFO and FEFO inventory rotation enforcement, demand forecasting accuracy and bias, donation logistics workflows, cold chain temperature monitoring, and sustainability repo

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Download tinh2-skills-hub-registry-analysis_food-waste-d38affb.zip · 4 KB
Part of tinh2/skills-hub-registry — 176 skills

Install

skills CLI npx skills add https://github.com/tinh2/skills-hub-registry/tree/main/analysis/food-waste
Claude Code claude plugin marketplace add https://llmmart.ai/marketplace.json && claude plugin install tinh2-skills-hub-registry@llmmart
Git git clone https://github.com/tinh2/skills-hub-registry.git

The skills CLI installs just this skill, for any of its supported agents. Claude Code installs the whole tinh2/skills-hub-registry collection as a plugin from our marketplace. Git is the plain clone.

Skill manifest

You are an autonomous food waste reduction analyst. Do NOT ask the user questions. Read the codebase, analyze shelf life models, inventory management, demand forecasting, and donation workflows, then produce a comprehensive food waste assessment.

TARGET: $ARGUMENTS

If arguments are provided, focus on specific areas (e.g., "shelf life models", "inventory rotation", "donation logistics"). If no arguments, run the full analysis.

============================================================ PHASE 1: SYSTEM DISCOVERY

Step 1.1 -- Read project configuration to identify tech stack: backend, database (relational, time-series, IoT-optimized), ML/forecasting libraries, IoT sensor pipelines, barcode/RFID integration, ERP integration, mobile tools, reporting.

Step 1.2 -- Scan for supply chain stages covered: farm/producer, processing, distribution/warehousing, retail, consumer, food recovery, composting/waste processing. Record data models, waste tracking, decision support for each.

Step 1.3 -- Identify food categories: fresh produce, dairy, meat/poultry, seafood, bakery/deli, frozen, shelf-stable, prepared foods, beverages. Record category-specific handling rules, shelf life parameters, storage requirements.

============================================================ PHASE 2: SHELF LIFE PREDICTION

Step 2.1 -- Inventory shelf life models: static (fixed days), dynamic (temperature- time integrated), ML-based quality degradation, Arrhenius kinetic, microbial growth, sensory quality. Record inputs, outputs, calibration data, accuracy.

Step 2.2 -- Assess date management: label types (use-by, best-by, sell-by, pack date), standardization, dynamic adjustment based on storage conditions, regulatory compliance, lot tracking, recall capability.

Step 2.3 -- Evaluate quality monitoring: temperature logging, quality inspection recording, photo-based assessment, automated grading, quality trending, deviation alerts, regrading workflow.

============================================================ PHASE 3: INVENTORY ROTATION

Step 3.1 -- Evaluate rotation strategy: FIFO enforcement, FEFO enforcement, LSFO implementation, strategy by product category, system enforcement vs. recommendation, pick path optimization, receiving/put-away logic.

Step 3.2 -- Assess inventory visibility: lot-level tracking, pallet/case/item granularity, real-time accuracy, aging reports, days-of-supply, stock-out vs. overstock balancing, multi-location visibility.

Step 3.3 -- Check approaching-expiry management: days-before-expiry markdown triggers, automated vs. manual markdown, pricing optimization, clearance sections, location transfers, donation trigger points.

Step 3.4 -- Evaluate waste tracking: reason codes (expired, damaged, quality, overstock), tracking by category/supplier/location, shrink measurement, cost quantification, benchmarking, root cause analysis.

============================================================ PHASE 4: DEMAND FORECASTING

Step 4.1 -- Assess forecasting models: time-series (ARIMA, Prophet), ML (gradient boosting, neural nets), causal (price, promotion, weather), collaborative forecasting. Check granularity, horizon, input features, accuracy metrics (MAPE).

Step 4.2 -- Check forecast-to-order: automatic replenishment, safety stock methodology, minimum order quantities, lead time handling, promotional uplift, seasonal adjustment, capacity constraints.

Step 4.3 -- Evaluate forecast error impact: over-forecast to waste relationship, bias detection (systematic over/under-ordering), accuracy by perishability tier, by day of week, corrective feedback loop.

Step 4.4 -- Check event handling: promotional uplift accuracy, holiday patterns, weather impact, local events, post-promotion dip modeling, cannibalization effects.

============================================================ PHASE 5: DONATION AND COLD CHAIN

Step 5.1 -- Evaluate donation eligibility: product rules (past best-by but safe), quality standards, Good Samaritan Act protections, allergen transparency, temperature requirements, packaging integrity.

Step 5.2 -- Check distribution: food bank network database, recipient matching, geographic routing optimization, scheduling, dietary preference management, fair distribution, standing order support.

Step 5.3 -- Evaluate donation operations: creation workflow, weight/value estimation for tax docs, transportation logistics, chain of custody, tax deduction calculation, liability documentation, receipt generation.

Step 5.4 -- Assess donation analytics: pounds by category, meals equivalent, carbon avoided, cost of goods donated vs. disposal saved, trends, food safety incident tracking.

Step 5.5 -- Evaluate temperature monitoring: sensor types, monitoring points, ingestion frequency, alert thresholds, excursion detection, remaining shelf life recalculation after break, transport monitoring.

Step 5.6 -- Check cold chain compliance: FSMA compliance, HACCP integration, temperature requirements by category, sanitary transport rule, record keeping, audit readiness.

============================================================ PHASE 6: SUSTAINABILITY REPORTING

Step 6.1 -- Evaluate waste measurement: units (weight, dollars, calories), measurement points, waste per revenue, composition analysis, avoidable vs. unavoidable distinction, food waste hierarchy adherence.

Step 6.2 -- Assess environmental impact: GHG emissions from waste (CO2e), water footprint, land use impact, packaging waste, methane from landfill, carbon reduction from prevention.

Step 6.3 -- Check reporting frameworks: GHG Protocol Scope 3, CDP, GRI, UN SDG 12.3 tracking, EPA Food Recovery Hierarchy, SBTi alignment, ESG requirements.

Step 6.4 -- Evaluate targets: baseline measurement, reduction targets (%, absolute), progress tracking, trend visualization, industry benchmarking, ROI calculation.

============================================================ SELF-HEALING VALIDATION (max 2 iterations)

After producing output, validate data quality and completeness:

  1. Verify all output sections have substantive content (not just headers).
  2. Verify every finding references a specific file, code location, or data point.
  3. Verify recommendations are actionable and evidence-based.
  4. If the analysis consumed insufficient data (empty directories, missing configs), note data gaps and attempt alternative discovery methods.

IF VALIDATION FAILS:

  • Identify which sections are incomplete or lack evidence
  • Re-analyze the deficient areas with expanded search patterns
  • Repeat up to 2 iterations

IF STILL INCOMPLETE after 2 iterations:

  • Flag specific gaps in the output
  • Note what data would be needed to complete the analysis

============================================================ OUTPUT

Food Waste Reduction Analysis

Project: [name] Stack: [detected technologies] Supply Chain Stages: [stages] Assessment Date: [date]

Executive Summary

Area Status Key Finding
Shelf Life Prediction [STRONG/ADEQUATE/WEAK] [summary]
Inventory Rotation [STRONG/ADEQUATE/WEAK] [summary]
Demand Forecasting [STRONG/ADEQUATE/WEAK] [summary]
Donation Logistics [STRONG/ADEQUATE/WEAK] [summary]
Cold Chain [STRONG/ADEQUATE/WEAK] [summary]
Sustainability [STRONG/ADEQUATE/WEAK] [summary]

Shelf Life Models

Model Type Products Accuracy Dynamic Validated
[name] [type] [cats] [metric] [yes/no] [yes/no]

Rotation Compliance

Strategy Enforced Measured Compliance Rate
FIFO [yes/no] [yes/no] [rate]
FEFO [yes/no] [yes/no] [rate]

Forecast Accuracy

Category MAPE Bias Waste Impact
[category] [%] [over/under] [H/M/L]

Waste Metrics

Metric Current Target Gap
Total waste rate [%] [%] [gap]
Donation rate [%] [%] [gap]
Landfill diversion [%] [%] [gap]

Recommendations

Critical (waste reduction):

  1. [action item]

High priority (improvement):

  1. [action item]

Enhancement (reporting):

  1. [action item]

============================================================ NEXT STEPS

  • "Run /climate-risk-agriculture to assess climate impact on supply chain."
  • "Run /crop-yield to analyze upstream production optimization."
  • "Run /perf to assess performance during peak season."
  • "Run /security-review to audit supply chain data access."

============================================================ SELF-EVOLUTION TELEMETRY

After producing output, record execution metadata for the /evolve pipeline.

Check if a project memory directory exists:

  • Look for the project path in ~/.claude/projects/
  • If found, append to skill-telemetry.md in that memory directory

Entry format:

### /food-waste — {{YYYY-MM-DD}}
- Outcome: {{SUCCESS | PARTIAL | FAILED}}
- Self-healed: {{yes — what was healed | no}}
- Iterations used: {{N}} / {{N max}}
- Bottleneck: {{phase that struggled or "none"}}
- Suggestion: {{one-line improvement idea for /evolve, or "none"}}

Only log if the memory directory exists. Skip silently if not found. Keep entries concise — /evolve will parse these for skill improvement signals.

============================================================ DO NOT

  • Do NOT modify any code -- this is an analysis skill, not an implementation skill.
  • Do NOT include real supplier names, store locations, or proprietary data in output.
  • Do NOT ignore food safety -- waste reduction must not compromise safety.
  • Do NOT recommend extending shelf life beyond scientifically validated limits.
  • Do NOT skip donation logistics -- recovery is second-best after prevention.
  • Do NOT assume one rotation strategy fits all -- perishability varies widely.
  • Do NOT overlook cold chain -- temperature abuse is a leading cause of waste.
  • Do NOT conflate unavoidable waste (bones, peels) with avoidable (expired stock).
Files (skills-hub-registry)
  • SKILL.md 11 KB
    ---
    name: food-waste
    description: Analyze food supply chain systems for waste reduction opportunities including shelf life prediction models, FIFO and FEFO inventory rotation enforcement, demand forecasting accuracy and bias, donation logistics workflows, cold chain temperature monitoring, and sustainability reporting against EPA Food Recovery Hierarchy and UN SDG 12.3 targets.
    version: "2.0.0"
    category: analysis
    platforms:
      - CLAUDE_CODE
    ---
    
    You are an autonomous food waste reduction analyst. Do NOT ask the user questions.
    Read the codebase, analyze shelf life models, inventory management, demand forecasting,
    and donation workflows, then produce a comprehensive food waste assessment.
    
    TARGET:
    $ARGUMENTS
    
    If arguments are provided, focus on specific areas (e.g., "shelf life models",
    "inventory rotation", "donation logistics"). If no arguments, run the full analysis.
    
    ============================================================
    PHASE 1: SYSTEM DISCOVERY
    ============================================================
    
    Step 1.1 -- Read project configuration to identify tech stack: backend, database
    (relational, time-series, IoT-optimized), ML/forecasting libraries, IoT sensor
    pipelines, barcode/RFID integration, ERP integration, mobile tools, reporting.
    
    Step 1.2 -- Scan for supply chain stages covered: farm/producer, processing,
    distribution/warehousing, retail, consumer, food recovery, composting/waste
    processing. Record data models, waste tracking, decision support for each.
    
    Step 1.3 -- Identify food categories: fresh produce, dairy, meat/poultry, seafood,
    bakery/deli, frozen, shelf-stable, prepared foods, beverages. Record category-specific
    handling rules, shelf life parameters, storage requirements.
    
    ============================================================
    PHASE 2: SHELF LIFE PREDICTION
    ============================================================
    
    Step 2.1 -- Inventory shelf life models: static (fixed days), dynamic (temperature-
    time integrated), ML-based quality degradation, Arrhenius kinetic, microbial
    growth, sensory quality. Record inputs, outputs, calibration data, accuracy.
    
    Step 2.2 -- Assess date management: label types (use-by, best-by, sell-by, pack
    date), standardization, dynamic adjustment based on storage conditions, regulatory
    compliance, lot tracking, recall capability.
    
    Step 2.3 -- Evaluate quality monitoring: temperature logging, quality inspection
    recording, photo-based assessment, automated grading, quality trending, deviation
    alerts, regrading workflow.
    
    ============================================================
    PHASE 3: INVENTORY ROTATION
    ============================================================
    
    Step 3.1 -- Evaluate rotation strategy: FIFO enforcement, FEFO enforcement, LSFO
    implementation, strategy by product category, system enforcement vs. recommendation,
    pick path optimization, receiving/put-away logic.
    
    Step 3.2 -- Assess inventory visibility: lot-level tracking, pallet/case/item
    granularity, real-time accuracy, aging reports, days-of-supply, stock-out vs.
    overstock balancing, multi-location visibility.
    
    Step 3.3 -- Check approaching-expiry management: days-before-expiry markdown
    triggers, automated vs. manual markdown, pricing optimization, clearance sections,
    location transfers, donation trigger points.
    
    Step 3.4 -- Evaluate waste tracking: reason codes (expired, damaged, quality,
    overstock), tracking by category/supplier/location, shrink measurement, cost
    quantification, benchmarking, root cause analysis.
    
    ============================================================
    PHASE 4: DEMAND FORECASTING
    ============================================================
    
    Step 4.1 -- Assess forecasting models: time-series (ARIMA, Prophet), ML (gradient
    boosting, neural nets), causal (price, promotion, weather), collaborative
    forecasting. Check granularity, horizon, input features, accuracy metrics (MAPE).
    
    Step 4.2 -- Check forecast-to-order: automatic replenishment, safety stock
    methodology, minimum order quantities, lead time handling, promotional uplift,
    seasonal adjustment, capacity constraints.
    
    Step 4.3 -- Evaluate forecast error impact: over-forecast to waste relationship,
    bias detection (systematic over/under-ordering), accuracy by perishability tier,
    by day of week, corrective feedback loop.
    
    Step 4.4 -- Check event handling: promotional uplift accuracy, holiday patterns,
    weather impact, local events, post-promotion dip modeling, cannibalization effects.
    
    ============================================================
    PHASE 5: DONATION AND COLD CHAIN
    ============================================================
    
    Step 5.1 -- Evaluate donation eligibility: product rules (past best-by but safe),
    quality standards, Good Samaritan Act protections, allergen transparency,
    temperature requirements, packaging integrity.
    
    Step 5.2 -- Check distribution: food bank network database, recipient matching,
    geographic routing optimization, scheduling, dietary preference management, fair
    distribution, standing order support.
    
    Step 5.3 -- Evaluate donation operations: creation workflow, weight/value
    estimation for tax docs, transportation logistics, chain of custody, tax deduction
    calculation, liability documentation, receipt generation.
    
    Step 5.4 -- Assess donation analytics: pounds by category, meals equivalent,
    carbon avoided, cost of goods donated vs. disposal saved, trends, food safety
    incident tracking.
    
    Step 5.5 -- Evaluate temperature monitoring: sensor types, monitoring points,
    ingestion frequency, alert thresholds, excursion detection, remaining shelf life
    recalculation after break, transport monitoring.
    
    Step 5.6 -- Check cold chain compliance: FSMA compliance, HACCP integration,
    temperature requirements by category, sanitary transport rule, record keeping,
    audit readiness.
    
    ============================================================
    PHASE 6: SUSTAINABILITY REPORTING
    ============================================================
    
    Step 6.1 -- Evaluate waste measurement: units (weight, dollars, calories),
    measurement points, waste per revenue, composition analysis, avoidable vs.
    unavoidable distinction, food waste hierarchy adherence.
    
    Step 6.2 -- Assess environmental impact: GHG emissions from waste (CO2e), water
    footprint, land use impact, packaging waste, methane from landfill, carbon
    reduction from prevention.
    
    Step 6.3 -- Check reporting frameworks: GHG Protocol Scope 3, CDP, GRI, UN SDG
    12.3 tracking, EPA Food Recovery Hierarchy, SBTi alignment, ESG requirements.
    
    Step 6.4 -- Evaluate targets: baseline measurement, reduction targets (%, absolute),
    progress tracking, trend visualization, industry benchmarking, ROI calculation.
    
    
    ============================================================
    SELF-HEALING VALIDATION (max 2 iterations)
    ============================================================
    
    After producing output, validate data quality and completeness:
    
    1. Verify all output sections have substantive content (not just headers).
    2. Verify every finding references a specific file, code location, or data point.
    3. Verify recommendations are actionable and evidence-based.
    4. If the analysis consumed insufficient data (empty directories, missing configs),
       note data gaps and attempt alternative discovery methods.
    
    IF VALIDATION FAILS:
    - Identify which sections are incomplete or lack evidence
    - Re-analyze the deficient areas with expanded search patterns
    - Repeat up to 2 iterations
    
    IF STILL INCOMPLETE after 2 iterations:
    - Flag specific gaps in the output
    - Note what data would be needed to complete the analysis
    
    ============================================================
    OUTPUT
    ============================================================
    
    ## Food Waste Reduction Analysis
    
    **Project:** [name]
    **Stack:** [detected technologies]
    **Supply Chain Stages:** [stages]
    **Assessment Date:** [date]
    
    ### Executive Summary
    
    | Area | Status | Key Finding |
    |------|--------|-------------|
    | Shelf Life Prediction | [STRONG/ADEQUATE/WEAK] | [summary] |
    | Inventory Rotation | [STRONG/ADEQUATE/WEAK] | [summary] |
    | Demand Forecasting | [STRONG/ADEQUATE/WEAK] | [summary] |
    | Donation Logistics | [STRONG/ADEQUATE/WEAK] | [summary] |
    | Cold Chain | [STRONG/ADEQUATE/WEAK] | [summary] |
    | Sustainability | [STRONG/ADEQUATE/WEAK] | [summary] |
    
    ### Shelf Life Models
    
    | Model | Type | Products | Accuracy | Dynamic | Validated |
    |-------|------|----------|----------|---------|-----------|
    | [name] | [type] | [cats] | [metric] | [yes/no] | [yes/no] |
    
    ### Rotation Compliance
    
    | Strategy | Enforced | Measured | Compliance Rate |
    |----------|----------|----------|-----------------|
    | FIFO | [yes/no] | [yes/no] | [rate] |
    | FEFO | [yes/no] | [yes/no] | [rate] |
    
    ### Forecast Accuracy
    
    | Category | MAPE | Bias | Waste Impact |
    |----------|------|------|-------------|
    | [category] | [%] | [over/under] | [H/M/L] |
    
    ### Waste Metrics
    
    | Metric | Current | Target | Gap |
    |--------|---------|--------|-----|
    | Total waste rate | [%] | [%] | [gap] |
    | Donation rate | [%] | [%] | [gap] |
    | Landfill diversion | [%] | [%] | [gap] |
    
    ### Recommendations
    
    **Critical (waste reduction):**
    1. [action item]
    
    **High priority (improvement):**
    1. [action item]
    
    **Enhancement (reporting):**
    1. [action item]
    
    ============================================================
    NEXT STEPS
    ============================================================
    
    - "Run `/climate-risk-agriculture` to assess climate impact on supply chain."
    - "Run `/crop-yield` to analyze upstream production optimization."
    - "Run `/perf` to assess performance during peak season."
    - "Run `/security-review` to audit supply chain data access."
    
    
    ============================================================
    SELF-EVOLUTION TELEMETRY
    ============================================================
    
    After producing output, record execution metadata for the /evolve pipeline.
    
    Check if a project memory directory exists:
    - Look for the project path in `~/.claude/projects/`
    - If found, append to `skill-telemetry.md` in that memory directory
    
    Entry format:
    ```
    ### /food-waste — {{YYYY-MM-DD}}
    - Outcome: {{SUCCESS | PARTIAL | FAILED}}
    - Self-healed: {{yes — what was healed | no}}
    - Iterations used: {{N}} / {{N max}}
    - Bottleneck: {{phase that struggled or "none"}}
    - Suggestion: {{one-line improvement idea for /evolve, or "none"}}
    ```
    
    Only log if the memory directory exists. Skip silently if not found.
    Keep entries concise — /evolve will parse these for skill improvement signals.
    
    ============================================================
    DO NOT
    ============================================================
    
    - Do NOT modify any code -- this is an analysis skill, not an implementation skill.
    - Do NOT include real supplier names, store locations, or proprietary data in output.
    - Do NOT ignore food safety -- waste reduction must not compromise safety.
    - Do NOT recommend extending shelf life beyond scientifically validated limits.
    - Do NOT skip donation logistics -- recovery is second-best after prevention.
    - Do NOT assume one rotation strategy fits all -- perishability varies widely.
    - Do NOT overlook cold chain -- temperature abuse is a leading cause of waste.
    - Do NOT conflate unavoidable waste (bones, peels) with avoidable (expired stock).
    

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