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feat(waterfall-lifecycle): add Phase 1 templates (9 templates)
Adds all 9 Phase 1 initiation templates: - problem-statement, vision-statement, stakeholder-map (subfase 1.1) - feasibility-assessment, data-feasibility-note, ai-feasibility-note (subfase 1.2) - initial-risk-register (subfase 1.3) - project-charter (subfase 1.4) - initiation-gate-pack (Gate A) All templates follow standard conventions: header metadata table, guidance comments, completeness checklist, and handover readiness section. Generated by Nuno Salvação Co-Authored-By: Nexo <nexo.modeling@gmail.com>
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‎plugins/waterfall-lifecycle/templates/phase-1/ai-feasibility-note.md.template‎

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# AI Feasibility Note
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| Field | Value |
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|-------|-------|
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| Phase | 1 — Initiation |
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| Subfase | 1.2 — Feasibility Analysis |
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| Date | {{date}} |
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| Owner | {{owner}} |
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| Status | {{status}} |
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## AI/ML Approach
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<!-- Describe the proposed AI/ML approach at a high level. What problem does the AI component solve? How does it fit into the overall solution architecture? -->
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{{ai_approach_description}}
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## Model Type and Rationale
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<!-- State the proposed model type(s) and explain why this approach is appropriate for the problem. Consider accuracy requirements, latency, explainability, and regulatory constraints. -->
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| Model Type | Rationale | Alternatives Considered |
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|-----------|-----------|------------------------|
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| {{model_type_1}} | {{model_rationale_1}} | {{model_alternatives_1}} |
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| {{model_type_2}} | {{model_rationale_2}} | {{model_alternatives_2}} |
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## Training Data Requirements
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<!-- Describe the data required to train and evaluate the model(s). Refer to the Data Feasibility Note for sourcing detail. -->
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| Requirement | Description | Status |
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|-------------|-------------|--------|
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| Minimum labelled examples | {{min_labels}} | {{labels_status}} |
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| Feature set | {{feature_set}} | {{features_status}} |
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| Historical data window | {{data_window}} | {{window_status}} |
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| Data freshness requirement | {{data_freshness}} | {{freshness_status}} |
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| Label quality / annotation | {{label_quality}} | {{label_quality_status}} |
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## GenAI/LLM Considerations (if applicable)
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<!-- Complete this section only if the solution includes a generative AI or large language model component. -->
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**Applicable:** {{genai_applicable}}
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<!-- yes / no / under evaluation -->
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| Topic | Detail |
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|-------|--------|
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| LLM provider / model | {{llm_provider}} |
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| Prompt strategy | {{prompt_strategy}} |
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| Context window requirements | {{context_window}} |
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| Hallucination risk and mitigations | {{hallucination_mitigations}} |
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| Fine-tuning required | {{fine_tuning_required}} |
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| RAG / retrieval augmentation | {{rag_approach}} |
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| Cost per inference estimate | {{inference_cost}} |
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## Performance Targets
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<!-- Define measurable performance targets the AI component must meet before it can be accepted into production. -->
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| Metric | Target | Minimum Acceptable | Measurement Method |
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|--------|--------|-------------------|-------------------|
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| {{metric_1}} | {{target_1}} | {{min_1}} | {{method_1}} |
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| {{metric_2}} | {{target_2}} | {{min_2}} | {{method_2}} |
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| {{metric_3}} | {{target_3}} | {{min_3}} | {{method_3}} |
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## Fallback Mechanism
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<!-- Describe the fallback if the AI component fails, underperforms, or is unavailable at runtime. The solution must remain viable without AI. -->
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**Fallback strategy:** {{fallback_strategy}}
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{{fallback_narrative}}
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## Ethical and Bias Considerations
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<!-- Identify potential ethical risks, bias sources, and fairness requirements. Reference applicable AI governance frameworks or regulations (e.g., EU AI Act). -->
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| Risk | Likelihood | Impact | Mitigation |
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|------|-----------|--------|-----------|
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| {{ethical_risk_1}} | {{likelihood_1}} | {{impact_1}} | {{mitigation_1}} |
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| {{ethical_risk_2}} | {{likelihood_2}} | {{impact_2}} | {{mitigation_2}} |
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| {{ethical_risk_3}} | {{likelihood_3}} | {{impact_3}} | {{mitigation_3}} |
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**EU AI Act risk category:** {{ai_act_category}}
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<!-- Unacceptable / High / Limited / Minimal -->
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## Completeness Checklist
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- [ ] AI/ML approach described at a solution architecture level
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- [ ] Model type(s) selected with explicit rationale and alternatives considered
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- [ ] Training data requirements documented and cross-referenced with Data Feasibility Note
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- [ ] GenAI/LLM section completed if applicable (or marked not applicable)
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- [ ] Measurable performance targets defined with minimum acceptable thresholds
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- [ ] Fallback mechanism documented — solution must be viable without AI
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- [ ] Ethical and bias risks identified with mitigations
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- [ ] EU AI Act or applicable AI governance category assigned
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## Handover Readiness
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<!-- Confirms this artefact is ready for formal handover to the next subfase or gate review. -->
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- [ ] AI Feasibility Note reviewed by ML lead or technical architect
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- [ ] Performance targets agreed with product owner
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- [ ] Ethical risk assessment reviewed with compliance or governance team
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- [ ] Fallback mechanism accepted by product owner and sponsor
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- [ ] Document version controlled and stored in the project artefact repository
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- [ ] Referenced in the Feasibility Assessment

‎plugins/waterfall-lifecycle/templates/phase-1/data-feasibility-note.md.template‎

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# Data Feasibility Note
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| Field | Value |
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|-------|-------|
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| Phase | 1 — Initiation |
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| Subfase | 1.2 — Feasibility Analysis |
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| Date | {{date}} |
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| Owner | {{owner}} |
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| Status | {{status}} |
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## Data Sources Identified
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<!-- List all known or candidate data sources required by the solution. Include internal systems, external providers, and open datasets. -->
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| ID | Source Name | Type | Owner / Provider | Availability | Notes |
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|----|-------------|------|-----------------|-------------|-------|
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| D01 | {{source_1_name}} | {{source_1_type}} | {{source_1_owner}} | {{source_1_availability}} | {{source_1_notes}} |
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| D02 | {{source_2_name}} | {{source_2_type}} | {{source_2_owner}} | {{source_2_availability}} | {{source_2_notes}} |
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| D03 | {{source_3_name}} | {{source_3_type}} | {{source_3_owner}} | {{source_3_availability}} | {{source_3_notes}} |
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## Data Quality Assessment
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<!-- Assess data quality across key dimensions. Rating: High / Medium / Low. -->
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| Dimension | Rating | Notes |
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|-----------|--------|-------|
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| Completeness | {{completeness_rating}} | {{completeness_notes}} |
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| Accuracy | {{accuracy_rating}} | {{accuracy_notes}} |
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| Consistency | {{consistency_rating}} | {{consistency_notes}} |
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| Timeliness | {{timeliness_rating}} | {{timeliness_notes}} |
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| Uniqueness | {{uniqueness_rating}} | {{uniqueness_notes}} |
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**Overall Data Quality Rating:** {{overall_quality_rating}}
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{{data_quality_narrative}}
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## Data Access and Licensing
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<!-- Describe access mechanisms, authentication requirements, and any licensing or contractual constraints on data use. -->
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| Source ID | Access Method | Authentication | Licence / Contract | Constraints |
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|-----------|--------------|----------------|-------------------|------------|
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| {{access_source_1}} | {{access_method_1}} | {{auth_1}} | {{licence_1}} | {{constraints_1}} |
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| {{access_source_2}} | {{access_method_2}} | {{auth_2}} | {{licence_2}} | {{constraints_2}} |
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## Data Volume Estimates
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<!-- Provide estimates of data volume and growth. This informs infrastructure and compute planning. -->
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| Data Source | Current Volume | Growth Rate | Retention Period | Storage Estimate |
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|-------------|---------------|-------------|-----------------|-----------------|
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| {{vol_source_1}} | {{current_vol_1}} | {{growth_1}} | {{retention_1}} | {{storage_1}} |
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| {{vol_source_2}} | {{current_vol_2}} | {{growth_2}} | {{retention_2}} | {{storage_2}} |
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## Data Gaps
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<!-- Document known data gaps that could affect the solution. For each gap, state the impact and proposed resolution. -->
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| Gap ID | Description | Impact | Proposed Resolution | Owner |
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|--------|-------------|--------|--------------------|----|
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| G01 | {{gap_1_desc}} | {{gap_1_impact}} | {{gap_1_resolution}} | {{gap_1_owner}} |
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| G02 | {{gap_2_desc}} | {{gap_2_impact}} | {{gap_2_resolution}} | {{gap_2_owner}} |
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| G03 | {{gap_3_desc}} | {{gap_3_impact}} | {{gap_3_resolution}} | {{gap_3_owner}} |
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## Completeness Checklist
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- [ ] All required data sources identified with owner and availability status
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- [ ] Data quality assessed across all five dimensions (completeness, accuracy, consistency, timeliness, uniqueness)
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- [ ] Access mechanisms and licensing constraints documented for each source
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- [ ] Volume and growth estimates provided
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- [ ] Data gaps identified with impact assessment and resolution plan
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- [ ] Data privacy and compliance implications noted (reference Feasibility Assessment legal section)
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## Handover Readiness
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<!-- Confirms this artefact is ready for formal handover to the next subfase or gate review. -->
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- [ ] Data Feasibility Note reviewed by data owner or data engineer
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- [ ] All critical data gaps have an assigned owner and resolution plan
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- [ ] Licensing/access constraints reviewed with legal or procurement
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- [ ] Document version controlled and stored in the project artefact repository
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- [ ] Referenced in the Feasibility Assessment

‎plugins/waterfall-lifecycle/templates/phase-1/feasibility-assessment.md.template‎

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# Feasibility Assessment
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| Field | Value |
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|-------|-------|
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| Phase | 1 — Initiation |
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| Subfase | 1.2 — Feasibility Analysis |
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| Date | {{date}} |
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| Owner | {{owner}} |
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| Status | {{status}} |
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## Technical Feasibility
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<!-- Assess whether the proposed solution is technically achievable with current or acquirable technology. Consider infrastructure, integration complexity, and team capabilities. -->
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**Rating:** {{technical_feasibility_rating}}
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<!-- Options: Feasible / Feasible with conditions / Not feasible -->
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{{technical_feasibility_narrative}}
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## Data Feasibility
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<!-- Assess whether the data required to support the solution (and any AI/ML components) is available, accessible, and of sufficient quality. Reference the Data Feasibility Note for detail. -->
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**Rating:** {{data_feasibility_rating}}
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<!-- Options: Feasible / Feasible with conditions / Not feasible -->
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{{data_feasibility_narrative}}
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## AI/ML Feasibility
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<!-- Assess whether the AI/ML approach is viable given the data, compute, and team expertise available. Reference the AI Feasibility Note for detail. -->
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**Rating:** {{ai_feasibility_rating}}
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<!-- Options: Feasible / Feasible with conditions / Not feasible -->
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{{ai_feasibility_narrative}}
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## Organisational Feasibility
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<!-- Assess whether the organisation has the capacity, skills, culture, and change readiness to adopt the solution. Consider training needs, change management, and stakeholder buy-in. -->
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**Rating:** {{org_feasibility_rating}}
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<!-- Options: Feasible / Feasible with conditions / Not feasible -->
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{{org_feasibility_narrative}}
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## Financial Feasibility
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<!-- Assess whether the project is financially viable. Include estimated investment, expected ROI, and break-even timeline. Reference budget estimates if available. -->
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**Rating:** {{financial_feasibility_rating}}
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<!-- Options: Feasible / Feasible with conditions / Not feasible -->
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| Cost Category | Estimate | Confidence |
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|---------------|----------|------------|
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| Development | {{dev_cost}} | {{dev_confidence}} |
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| Infrastructure | {{infra_cost}} | {{infra_confidence}} |
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| Licensing / Data | {{licence_cost}} | {{licence_confidence}} |
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| Change Management | {{change_cost}} | {{change_confidence}} |
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| Total Estimate | {{total_cost}} | {{total_confidence}} |
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{{financial_feasibility_narrative}}
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## Legal and Compliance
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<!-- Identify applicable legal, regulatory, or compliance requirements. Include data privacy (GDPR, CCPA), sector-specific regulations, and IP considerations. -->
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| Requirement | Applicable | Notes |
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|-------------|-----------|-------|
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| GDPR / Data Privacy | {{gdpr_applicable}} | {{gdpr_notes}} |
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| Sector Regulation | {{sector_reg_applicable}} | {{sector_reg_notes}} |
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| IP / Licensing | {{ip_applicable}} | {{ip_notes}} |
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| AI Act / Model Risk | {{ai_act_applicable}} | {{ai_act_notes}} |
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| Other | {{other_applicable}} | {{other_notes}} |
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## Overall Feasibility Verdict
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<!-- State the overall feasibility verdict based on all dimensions assessed above. -->
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**Verdict:** {{overall_verdict}}
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<!-- Options: feasible / not_feasible / conditional -->
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{{overall_verdict_rationale}}
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## Conditions and Mitigations
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<!-- If the verdict is conditional, list all conditions that must be met before the project can proceed. -->
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| # | Condition | Owner | Due Date | Status |
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|---|-----------|-------|----------|--------|
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| 1 | {{condition_1}} | {{condition_owner_1}} | {{condition_due_1}} | {{condition_status_1}} |
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| 2 | {{condition_2}} | {{condition_owner_2}} | {{condition_due_2}} | {{condition_status_2}} |
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| 3 | {{condition_3}} | {{condition_owner_3}} | {{condition_due_3}} | {{condition_status_3}} |
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## Completeness Checklist
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- [ ] All six feasibility dimensions assessed with a rating
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- [ ] Each rating supported by a written narrative
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- [ ] Financial feasibility includes cost breakdown with confidence levels
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- [ ] Legal and compliance requirements enumerated
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- [ ] Overall verdict is one of: feasible / not_feasible / conditional
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- [ ] All conditions documented with owner and due date (if conditional)
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- [ ] Data Feasibility Note referenced or attached
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- [ ] AI Feasibility Note referenced or attached
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## Handover Readiness
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<!-- Confirms this artefact is ready for formal handover to the next subfase or gate review. -->
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- [ ] Feasibility Assessment reviewed and signed off by technical lead and sponsor
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- [ ] All conditional items have assigned owners and due dates
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- [ ] Legal/compliance review completed or formally deferred with rationale
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- [ ] Document version controlled and stored in the project artefact repository
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- [ ] Linked to Data Feasibility Note and AI Feasibility Note

‎plugins/waterfall-lifecycle/templates/phase-1/initial-risk-register.md.template‎

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# Initial Risk Register
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| Field | Value |
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|-------|-------|
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| Phase | 1 — Initiation |
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| Subfase | 1.3 — Risk Identification |
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| Date | {{date}} |
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| Owner | {{owner}} |
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| Status | {{status}} |
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## Risk Register Table
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<!-- Document all identified risks. Likelihood: 1 (rare) to 5 (almost certain). Impact: 1 (negligible) to 5 (critical). Score = Likelihood × Impact. Status: Open / Mitigated / Accepted / Closed. -->
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| ID | Title | Category | Likelihood | Impact | Score | Status | Owner | Mitigation Plan |
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|----|-------|----------|-----------|--------|-------|--------|-------|----------------|
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| R01 | {{risk_1_title}} | {{risk_1_category}} | {{risk_1_likelihood}} | {{risk_1_impact}} | {{risk_1_score}} | {{risk_1_status}} | {{risk_1_owner}} | {{risk_1_mitigation}} |
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| R02 | {{risk_2_title}} | {{risk_2_category}} | {{risk_2_likelihood}} | {{risk_2_impact}} | {{risk_2_score}} | {{risk_2_status}} | {{risk_2_owner}} | {{risk_2_mitigation}} |
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| R03 | {{risk_3_title}} | {{risk_3_category}} | {{risk_3_likelihood}} | {{risk_3_impact}} | {{risk_3_score}} | {{risk_3_status}} | {{risk_3_owner}} | {{risk_3_mitigation}} |
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| R04 | {{risk_4_title}} | {{risk_4_category}} | {{risk_4_likelihood}} | {{risk_4_impact}} | {{risk_4_score}} | {{risk_4_status}} | {{risk_4_owner}} | {{risk_4_mitigation}} |
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| R05 | {{risk_5_title}} | {{risk_5_category}} | {{risk_5_likelihood}} | {{risk_5_impact}} | {{risk_5_score}} | {{risk_5_status}} | {{risk_5_owner}} | {{risk_5_mitigation}} |
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| R06 | {{risk_6_title}} | {{risk_6_category}} | {{risk_6_likelihood}} | {{risk_6_impact}} | {{risk_6_score}} | {{risk_6_status}} | {{risk_6_owner}} | {{risk_6_mitigation}} |
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| R07 | {{risk_7_title}} | {{risk_7_category}} | {{risk_7_likelihood}} | {{risk_7_impact}} | {{risk_7_score}} | {{risk_7_status}} | {{risk_7_owner}} | {{risk_7_mitigation}} |
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<!-- Risk categories: Technical / Data / AI-ML / Financial / Organisational / Legal-Compliance / External / Schedule -->
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## Top 5 Risks Summary
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<!-- Highlight the five highest-scoring risks for executive visibility. Update this section whenever the register changes. -->
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| Priority | Risk ID | Title | Score | Key Mitigation |
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|----------|---------|-------|-------|----------------|
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| 1 | {{top1_id}} | {{top1_title}} | {{top1_score}} | {{top1_mitigation}} |
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| 2 | {{top2_id}} | {{top2_title}} | {{top2_score}} | {{top2_mitigation}} |
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| 3 | {{top3_id}} | {{top3_title}} | {{top3_score}} | {{top3_mitigation}} |
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| 4 | {{top4_id}} | {{top4_title}} | {{top4_score}} | {{top4_mitigation}} |
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| 5 | {{top5_id}} | {{top5_title}} | {{top5_score}} | {{top5_mitigation}} |
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## Completeness Checklist
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- [ ] At least one risk identified per relevant category (Technical, Data, AI-ML, Financial, Organisational)
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- [ ] All risks scored with Likelihood × Impact
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- [ ] Each risk has a named owner and mitigation plan
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- [ ] Top 5 risks summarised for executive review
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- [ ] Risk register reviewed with project sponsor and technical lead
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- [ ] No open critical risks (score ≥ 20) without a documented mitigation plan
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## Handover Readiness
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<!-- Confirms this artefact is ready for formal handover to the next subfase or gate review. -->
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- [ ] Initial Risk Register reviewed and accepted by project sponsor
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- [ ] All high-score risks (score ≥ 15) have owners confirmed
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- [ ] Risk register will be carried forward and updated throughout all subsequent phases
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- [ ] Document version controlled and stored in the project artefact repository
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- [ ] Risks cross-referenced in the Project Charter risks summary section

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