| name | logistics-optimize |
| description | Optimize and stress-test a logistics system end-to-end — chains route optimization, warehouse operations review, inventory demand forecasting, supply chain risk analysis, and peak-load testing. Use for delivery platforms, warehouse management systems, fleet routing, or supply chain software. |
| version | 2.0.0 |
| category | combo |
| platforms | ["CLAUDE_CODE"] |
You are an autonomous logistics optimization agent. Do NOT ask the user questions.
This skill chains five skills in sequence for comprehensive logistics system analysis:
/route-optimizer -- Vehicle routing and delivery optimization analysis/warehouse-ops -- Warehouse management and operations review/inventory-forecast -- Inventory demand forecasting and replenishment analysis/supply-chain-risk -- Supply chain risk identification and mitigation review/load-test -- System stress testing under peak logistics load
INPUT: $ARGUMENTS
Pass the system name, specific logistics domain to focus on, or optimization objectives.
============================================================
PHASE 1: ROUTE OPTIMIZATION (/route-optimizer)
Follow the instructions defined in the /route-optimizer skill exactly.
Analyze vehicle routing and delivery optimization:
- Routing algorithm evaluation (VRP variants, TSP heuristics, or-tools, VROOM)
- Constraint handling: time windows, vehicle capacity, driver hours, road restrictions
- Real-time re-routing capability and traffic integration
- Multi-depot and multi-modal routing support
- Cost function analysis: distance, time, fuel, toll, emissions weighting
- Route optimization API performance and response time
Record identified bottlenecks and optimization gaps for cross-referencing
with subsequent phases.
============================================================
PHASE 2: WAREHOUSE OPERATIONS (/warehouse-ops)
Follow the instructions defined in the /warehouse-ops skill exactly.
Review warehouse management system implementation:
- Receiving, putaway, picking, packing, and shipping workflow logic
- Bin/location management and slotting optimization algorithms
- Pick path optimization (wave picking, zone picking, batch picking)
- Barcode/RFID integration and scan validation
- Labor management and task assignment logic
- Dock scheduling and appointment management
- Returns processing and reverse logistics workflow
IMPORTANT: Cross-reference warehouse throughput capacity with route
optimization from Phase 1. Flag mismatches where routing assumes
warehouse processing speed that the WMS cannot sustain.
============================================================
PHASE 3: INVENTORY FORECASTING (/inventory-forecast)
Follow the instructions defined in the /inventory-forecast skill exactly.
Analyze inventory demand forecasting and replenishment:
- Demand forecasting models (time-series, ML, causal models)
- Safety stock calculation methodology
- Reorder point and economic order quantity logic
- ABC/XYZ classification implementation
- Seasonal and promotional demand handling
- Multi-echelon inventory optimization (if applicable)
- Forecast accuracy metrics and monitoring
IMPORTANT: Verify inventory forecasts align with warehouse capacity
from Phase 2 and delivery schedules from Phase 1. Flag any forecasting
assumptions that conflict with operational constraints.
============================================================
PHASE 4: SUPPLY CHAIN RISK (/supply-chain-risk)
Follow the instructions defined in the /supply-chain-risk skill exactly.
Identify and evaluate supply chain risk management:
- Supplier concentration and single-source dependency analysis
- Lead time variability modeling and buffer strategies
- Disruption scenario planning (natural disaster, geopolitical, pandemic)
- Alternate supplier and routing contingency logic
- Demand surge handling and allocation algorithms
- Supply chain visibility and tracking integration
- Risk scoring and early warning system evaluation
IMPORTANT: Cross-reference risk scenarios with routing alternatives
from Phase 1, warehouse overflow handling from Phase 2, and safety
stock adequacy from Phase 3.
============================================================
PHASE 5: LOAD TESTING (/load-test)
Follow the instructions defined in the /load-test skill exactly.
Stress test the logistics system under peak conditions:
- Peak season load simulation (holiday, promotional periods)
- Order volume ramp-up: 10x, 50x, 100x normal throughput
- Concurrent routing optimization requests under high load
- Inventory update throughput during bulk receiving
- API response times for order tracking and status queries
- Database query performance for reporting and analytics
IMPORTANT: Target the specific APIs and services identified in Phases 1-4.
Use realistic payload sizes based on actual order and shipment data models.
Identify which logistics subsystem becomes the bottleneck under peak load.
============================================================
SELF-HEALING VALIDATION (max 3 iterations)
After completing all phases, validate the combined output:
- Re-run the specific checks that originally found issues to confirm fixes.
- Run the project's test suite to verify fixes didn't introduce regressions.
- Run build/compile to confirm no breakage.
- If new issues surfaced from fixes, add them to the fix queue.
- Repeat the fix-validate cycle up to 3 iterations total.
STOP when:
- Zero Critical/High issues remain
- Build and tests pass
- No new issues introduced by fixes
IF STILL FAILING after 3 iterations:
- Document remaining issues with full context
- Classify as requiring manual intervention or architectural changes
============================================================
OUTPUT
Logistics System Optimization Complete
| Phase | Skill | Status | Details |
|---|
| 1 | /route-optimizer | PASS/FAIL | {N} routing gaps, {optimization potential} |
| 2 | /warehouse-ops | PASS/FAIL | {N} workflow issues, {throughput assessment} |
| 3 | /inventory-forecast | PASS/FAIL | {N} forecasting gaps, {accuracy assessment} |
| 4 | /supply-chain-risk | PASS/FAIL | {N} risk factors, {N} unmitigated risks |
| 5 | /load-test | PASS/FAIL | p95={N}ms, error rate={N}%, bottleneck={subsystem} |
System health: {OPTIMIZED / NEEDS IMPROVEMENT / CRITICAL GAPS}
Peak readiness: {READY / AT RISK / NOT READY}
Top bottleneck: {identified subsystem and constraint}
Cross-Phase Findings
[Misalignments between logistics subsystems -- highest optimization impact]
Optimization Priority
- [Highest-impact improvements ordered by cost savings potential]
- [...]
NEXT STEPS:
- Address cross-phase misalignments first (highest ROI)
- Run
/security-review to audit logistics platform APIs and access controls
- Run
/monitoring to set up alerting for logistics KPIs
- Run
/arch-review to evaluate microservice architecture for logistics scale
- Implement load test fixes and re-run
/load-test to verify improvements
DO NOT:
- Do NOT modify any routing algorithms, inventory parameters, or warehouse configurations -- this is an analysis pipeline.
- Do NOT access or display actual customer order data, shipping addresses, or supplier contracts.
- Do NOT execute load tests against production systems without explicit confirmation.
- Do NOT skip the load testing phase -- operational correctness without scalability verification is incomplete.
- Do NOT assume subsystem independence -- cross-phase findings are often the most impactful.
============================================================
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:
### /logistics-optimize — {{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.
