The agent operates as a senior operations manager, applying Lean Six Sigma, PDCA, and capacity-planning frameworks to drive measurable efficiency gains.
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Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionoperations-managerExecute the skills CLI command in your project's root directory to begin installation:
Fetches operations-manager from borghei/claude-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate operations-manager. Access via /operations-manager in your agent's command palette.
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Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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The agent operates as a senior operations manager, applying Lean Six Sigma, PDCA, and capacity-planning frameworks to drive measurable efficiency gains.
Checkpoint: After step 3, confirm that every KPI has an owner and a data source before moving to analysis.
| Level | Name | Characteristics |
|---|---|---|
| 1 | Reactive | Ad-hoc processes, hero-dependent, crisis management, limited visibility |
| 2 | Managed | Documented processes, basic metrics, standard procedures, some automation |
| 3 | Defined | Consistent processes, performance tracking, cross-functional coordination, continuous improvement |
| 4 | Measured | Data-driven decisions, predictive analytics, optimized workflows, proactive management |
| 5 | Optimized | Self-optimizing systems, innovation culture, industry-leading efficiency, strategic advantage |
| Category | Metric | Formula | Target |
|---|---|---|---|
| Efficiency | Utilization | Active time / Available time | 85%+ |
| Productivity | Output per FTE | Units / FTE hours | Varies |
| Quality | First-pass yield | Good units / Total | 95%+ |
| Speed | Cycle time | End time - Start time | Varies |
| Cost | Cost per unit | Total cost / Units | Varies |
| Customer | CSAT | Satisfied / Total responses | 90%+ |
# Process: [Name]
- **Owner:** [Role]
- **Frequency:** [Daily / Weekly / On-demand]
- **Trigger:** [What starts this process]
- **Output:** [Deliverable or state change]
## Steps
| # | Action | Owner | Input | Output | SLA |
|---|--------|-------|-------|--------|-----|
| 1 | Receive request | Ops team | Ticket | Validated ticket | 1 hr |
| 2 | Validate request | Analyst | Validated ticket | Approved / Rejected | 2 hr |
| 3 | Execute action | Specialist | Approved ticket | Completed work | 4 hr |
| 4 | Notify requester | System | Completion record | Notification sent | 15 min |
## Decision Points
| Decision | Criteria | Yes Path | No Path |
|----------|----------|----------|---------|
| Valid request? | Meets intake checklist | Step 2 | Reject and notify |
| Approval required? | Value > $5K | Escalate to manager | Step 3 |
## Metrics
| Metric | Target | Current |
|--------|--------|---------|
| Cycle time | < 8 hours | |
| Error rate | < 2% | |
| Volume | 50/day | |
A fulfillment team running 6.5-hour average cycle time against a 5-hour target:
DEFINE
Problem: Cycle time 30% above target (6.5 hr vs 5.0 hr)
Scope: Order-to-ship for domestic orders
Metric: Average cycle time, measured from ERP timestamps
MEASURE
Baseline data (30 days, n=1200 orders):
Mean: 6.5 hr | Median: 6.1 hr | P95: 9.8 hr
Bottleneck: Pick-and-pack stage accounts for 55% of total time
ANALYZE
5 Whys on pick-and-pack delay:
1. Why slow? -> Pickers walk long distances
2. Why long walks? -> Items stored alphabetically, not by frequency
3. Why alphabetical? -> Legacy warehouse layout from 2019
Root cause: Storage layout does not reflect current SKU velocity
IMPROVE
Action: Re-slot top 20% SKUs (by volume) to Zone A near packing stations
Pilot: 2-week trial on Aisle 1-3
Expected result: 25% reduction in pick time
CONTROL
Post-pilot (14 days, n=580 orders):
Mean: 4.8 hr | Median: 4.5 hr | P95: 7.2 hr
Result: 26% reduction -- standardize across all aisles
Control: Weekly cycle-time dashboard with alert at > 5.5 hr
Capacity Required = Forecast Volume x Time per Unit
Capacity Available = FTE x Hours per Day x Productivity Factor
Gap = Required - Available
Planning Horizons:
Daily -> Staff scheduling, shift adjustments
Weekly -> Workload balancing across teams
Monthly -> Temp staffing, overtime authorization
Quarterly -> Hiring plans, cross-training programs
Annual -> Strategic workforce and capex planning
| Dimension | Weight | Metrics |
|---|---|---|
| Quality | 30% | Defect rate (< 1%), first-pass acceptance (> 95%) |
| Delivery | 25% | On-time delivery (> 98%), lead time (< 5 days) |
| Cost | 20% | Price vs market (within 5%), invoice accuracy (> 99%) |
| Service | 15% | Response time (< 24 hr), issue resolution (< 48 hr) |
| Relationship | 10% | Communication quality, flexibility |
Score each metric 1-5. Weighted total determines vendor tier: 4.5+ = Strategic Partner, 3.5-4.4 = Preferred, below 3.5 = Under Review.
DIRECT COSTS
Labor: Wages + Benefits + Overtime
Materials: Raw materials + Supplies
Equipment: Depreciation + Maintenance
INDIRECT COSTS
Overhead: Facilities + Utilities + Insurance
Administrative: Management + Support staff
Cost per Unit = (Direct + Indirect) / Units Produced
references/process_design.md - Process design principlesreferences/lean_operations.md - Lean methodologyreferences/vendor_management.md - Vendor management guidereferences/cost_optimization.md - Cost reduction strategies# Map and analyze business processes
python scripts/process_mapper.py --file process_steps.csv
python scripts/process_mapper.py --file process_steps.csv --json
# Resource capacity planning
python scripts/capacity_planner.py --file resources.csv --forecast demand.csv
python scripts/capacity_planner.py --file resources.csv --forecast demand.csv --json
# SLA compliance tracking
python scripts/sla_tracker.py --file tickets.csv
python scripts/sla_tracker.py --file tickets.csv --threshold 95 --json
| Problem | Root Cause | Resolution |
|---|---|---|
| Cycle time increasing despite no volume change | Process drift, undocumented workarounds, or degraded tooling | Re-map the current process against documented standard; look for unofficial steps added over time; check system performance and integration latency |
| First-pass yield dropping below 95% | Training gaps, unclear specifications, or upstream quality issues | Run a fishbone analysis on defect categories; check if the issue correlates with new hires (training) or specific inputs (upstream); add quality gates at handoff points |
| Utilization consistently above 95% | Understaffing, poor demand forecasting, or inability to say no to ad-hoc requests | Sustained >95% utilization causes burnout and errors; hire or cross-train to reach 85% target; implement demand prioritization with SLA tiers |
| SLA compliance below target | Unrealistic SLAs, inconsistent triage, or capacity bottlenecks | Audit SLA definitions against actual capability; implement priority-based routing; add escalation triggers at 70% of SLA elapsed time |
| Cost per unit rising | Volume decline (fixed cost spread), scope creep, or vendor price increases | Decompose costs into fixed and variable; benchmark vendor costs annually; eliminate non-value-add process steps identified through value stream mapping |
| Cross-functional handoffs cause delays | No clear ownership at boundaries, different systems, or misaligned SLAs | Define RACI for every handoff; align upstream/downstream SLAs; implement handoff checklists with automated notifications |
| Improvement projects fail to sustain gains | No control plan, missing ownership, or competing priorities | Every DMAIC project must include a Control phase with dashboards, alert thresholds, and a named process owner; conduct 30/60/90 day post-implementation reviews |
| Dimension | Metric | Target | Measurement |
|---|---|---|---|
| Efficiency | Process cycle time | Within 10% of target for each process | ERP/workflow system timestamps |
| Efficiency | Resource utilization | 80-90% (avoid burnout above 95%) | Time tracking / capacity planning tool |
| Quality | First-pass yield | > 95% | Quality inspection data or error logs |
| Quality | Error/rework rate | < 2% | Defect tracking system |
| Cost | Cost per unit trend | Year-over-year reduction of 3-5% | Finance cost allocation reports |
| Cost | Budget variance | Within +/- 5% of plan | Monthly budget vs actual reporting |
| Customer | Internal CSAT | > 90% satisfied | Quarterly internal customer survey |
| Customer | SLA compliance | > 95% of commitments met | SLA tracking dashboard |
| Delivery | On-time delivery | > 98% | Order/ticket com ✓ Make data-driven prioritization decisions faster Stakeholder CommunicationDraft PRDs, status updates, and stakeholder presentations Example Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement ✓ Save 3-5 hours/week on communication overhead Implementation GuidePrerequisites
Time Estimate 30-60 minutes to see productivity improvements Steps
Common Pitfalls
Best Practices✓ Do
✗ Don't
💡 Pro Tips
When to Use This✓ Use when Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work. ✗ Avoid when Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed. Learning Path
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