agile-lifecycle

Generate a lifecycle artefact from template. Without args, lists available templates for current phase.

Evaluate and log a change request — determines if change is incremental or significant, triggers change control process if needed.

Execute a formal gate review. Checks gate criteria, validates artefacts, produces gate-review-report with PASS/FAIL/WAIVED outcome.

Initialize agile-lifecycle framework for a new project. Creates lifecycle-state.json, directory structure, and bootstraps Phase 1.

Generate a metrics report for the current phase or overall lifecycle — delivery, quality, product, AI, governance metrics.

Use this agent to screen technical and commercial feasibility of an AI initiative. Examples: "Assess feasibility of this AI solution", "Is our data good enough for this model?", "Check technical constraints before we commit", "Go/no-go assessment for the opportunity", "Can we build this with the data we have?" <example> Context: Team wants to know if building a real-time fraud detection model is technically feasible given their infrastructure. user: "Screen the feasibility of our real-time fraud detection initiative before we commit the team" assistant: "I'll use the feasibility-screening agent to assess technical complexity, data availability, regulatory constraints, and commercial viability for this initiative." <commentary> Pre-commitment feasibility check — agent evaluates all viability dimensions before resources are allocated. </commentary> </example> <example> Context: Leadership is concerned about data privacy regulations affecting a proposed customer analytics platform. user: "What are the feasibility blockers for a GDPR-compliant customer analytics AI?" assistant: "I'll use the feasibility-screening agent to assess regulatory feasibility, data governance requirements, and technical architecture constraints for GDPR compliance." <commentary> Regulatory feasibility concern — agent evaluates compliance constraints as a first-class feasibility dimension. </commentary> </example>

Use this agent to map solution hypotheses, define value and AI hypotheses, and propose experiments. Examples: "Map our solution hypotheses", "Define value hypotheses for this AI feature", "Create the hypothesis canvas for Gate A", "What experiments should we run to test our assumptions?", "Frame our AI approach as testable hypotheses" <example> Context: Team has validated the problem and needs to structure their assumptions before designing the solution. user: "We know the problem is real — now map out all our hypotheses before we start designing" assistant: "I'll use the hypothesis-mapping agent to structure your desirability, feasibility, and viability hypotheses into a prioritized hypothesis map with validation methods." <commentary> Pre-design hypothesis structuring — agent organizes all assumptions into a testable hypothesis map before solution work begins. </commentary> </example> <example> Context: Hypothesis about user adoption of an AI recommendation feature is untested and blocking the Phase 2 go/no-go decision. user: "Our biggest assumption is that users will trust AI recommendations — how do we map and prioritize this?" assistant: "I'll use the hypothesis-mapping agent to frame this as a formal hypothesis, assess risk level, and design a lean validation experiment for Phase 1." <commentary> Critical adoption hypothesis needs formal framing — agent structures it for rigorous testing before Phase 2 commitment. </commentary> </example>

Use this agent when starting Phase 1 of the lifecycle — framing the opportunity, articulating the business problem, building the AI justification, and mapping stakeholders. Examples: "Let's frame our AI opportunity", "I need to write an opportunity statement", "Help me articulate the business case for using AI here", "Map out our stakeholders for this initiative" <example> Context: An executive sponsor has identified a potential AI initiative but needs it formally framed before board approval. user: "We want to build an AI-powered churn prediction model — help me frame the opportunity" assistant: "I'll use the opportunity-framing agent to structure this initiative with a clear problem statement, strategic alignment, and initial business case." <commentary> New AI initiative needs formal opportunity framing — this agent structures the business case and stakeholder context before any technical work begins. </commentary> </example> <example> Context: Product team has a vague idea about using ML for customer segmentation and needs to determine if it deserves investment. user: "Is there a real opportunity here for ML-based segmentation, or is this just a tech solution looking for a problem?" assistant: "I'll use the opportunity-framing agent to assess strategic fit, define the target customer, and articulate the value hypothesis before committing resources." <commentary> Opportunity validation question — agent applies structured framing to determine whether the initiative has strategic merit. </commentary> </example>

Use this agent to validate the problem with evidence through user research, pain point mapping, and market context analysis. Examples: "Validate our problem statement with user research", "Map user pain points for this problem", "What evidence do we have that this problem is real?", "Conduct a user journey analysis", "Confirm the problem is worth solving before we go further" <example> Context: Product team has framed a problem around manual data entry but hasn't confirmed users actually experience this as painful. user: "We think users hate manual data entry — how do we validate this is a real problem worth solving?" assistant: "I'll use the problem-validation agent to design a validation approach: user interviews, observation sessions, and quantitative usage data analysis to confirm problem significance." <commentary> Problem significance unclear — agent designs a validation plan before the team commits to building a solution. </commentary> </example> <example> Context: Stakeholders have conflicting views on whether the problem is customer-facing or an internal operations issue. user: "Some say it's a customer problem, others say it's operations — help us validate which one" assistant: "I'll use the problem-validation agent to structure the investigation, define the primary problem owner, and design evidence-gathering activities to resolve the conflict." <commentary> Problem ownership ambiguity — agent structures the validation to resolve the conflict with evidence rather than opinion. </commentary> </example>

Use this agent to plan iterations — break the solution into sprints, set iteration goals, define acceptance criteria, and structure the delivery roadmap. Examples: "Plan our delivery iterations", "Break down the solution into sprints", "Create our iteration plan", "How do we structure the delivery of this AI system?", "Define our sprint cadence and goals for Phase 2" <example> Context: Phase 2 design is complete and the team needs to plan delivery iterations for Phase 3-4 before committing to a Phase 2 exit. user: "Help us plan the delivery iterations — we have 6 weeks for phase 3 and 4" assistant: "I'll use the iteration-planning agent to design the sprint structure, allocate capacity, and define sprint goals across the 6-week delivery window." <commentary> Delivery planning at Phase 2 — agent structures iterations with realistic capacity and sprint goals before Phase 3 starts. </commentary> </example> <example> Context: Team velocity has been lower than expected and the Phase 4 sprint plan needs to be revised mid-delivery. user: "We're running behind — 3 sprints completed but only 60% of planned scope done. Replan the remaining iterations." assistant: "I'll use the iteration-planning agent to reassess remaining capacity, re-prioritize the backlog, and produce a revised sprint plan for the remaining iterations." <commentary> Mid-delivery replan request — agent recalculates realistic scope based on actual velocity and revised capacity. </commentary> </example>

This skill should be used when a project includes AI/ML components and needs guidance on experiment design, model card creation, AI validation, bias and fairness assessment, LLM red-teaming, or drift monitoring configuration. Applies when a user asks to design an AI experiment, plan model validation, set up ML monitoring, or assess AI-specific gate criteria (Gates D and E).

This skill should be used when a user needs to create, fill, or validate a lifecycle artefact using the framework's template library — including selecting the correct template for a phase or gate artefact, filling all mandatory fields, removing guidance comments, and validating against a JSON schema. Triggers when the user says "create the [artefact name]", "fill in the template for", or "is this artefact gate-ready".

This skill should be used when shaping the product backlog, prioritizing stories, or preparing items for sprint readiness. Triggers when entering Phase 3 (Discovery), transitioning to Phase 4 (Delivery), or when backlog health needs assessment.

This skill should be used when evaluating if a change is incremental or significant, when processing a change request, or when updating the change log. Triggers when scope, requirements, or architecture changes are proposed.

This skill should be used when defining, applying, or updating the Definition of Done. Triggers when Phase 4 is about to start, when a story is declared complete, or when quality standards need to be updated across sprint and release levels.

3 hooks across 2 events