ai-sdlc-user-story-decomposition

ai-sdlc-user-story-decomposition: User Story Decomposition

Internal AI SDLC skill, not client-facing by default. Every rule below is important to follow. None of it can be skipped. Before producing the final artifact, confirm required inputs, target audience, missing facts, output format, and constraints when they are unclear. Do not invent missing information. Ask concise clarification questions when required inputs are absent.

0. Skill Card

• Skill name: ai-sdlc-user-story-decomposition
• Primary audience: BA
• Supporting audience: PM, QA, Dev
• Audience tags: BA, PM, QA, Dev
• SDLC stage: Story decomposition
• Purpose: Turn a clarified delivery package into implementable, actor-based user stories with acceptance logic and scenario coverage.
• Output: Epics, user stories, acceptance criteria, scenario coverage, and priority signals

0.1 Required Inputs

• Clarified delivery package or gap-reviewed requirements.
• Actors, workflows, and business outcomes.
• MVP, priority, or release constraints.

0.2 Clarification Rules

• Ask concise questions before finalizing when role, artifact, requirements, scope, audience, or constraints are unclear.
• If optional information is missing, mark it as TBD, Not provided, or Assumption instead of inventing it.
• Separate confirmed facts from assumptions and open questions.
• Do not proceed to downstream synthesis when a required upstream artifact or decision is missing.

0.2.1 Flow Mode Flags

• Support two explicit execution flags: --quick-flow and --full-flow.
• If both flags are supplied, --full-flow takes precedence because it is the stricter mode.
• --quick-flow: move fast, make high-quality progress with available context, avoid clarification questions unless continuing would create material product, security, compliance, data-loss, or irreversible implementation risk.
• In --quick-flow, use documented assumptions, recommended defaults, existing repository patterns, and the nearest available artifact evidence; record important assumptions and decisions in decision-log.md.
• In --quick-flow, run only focused checks that are directly relevant, cheap, and likely to catch regressions for the requested work; report any skipped broader checks as residual risk.
• --full-flow: ask concise clarification questions when inputs, scope, ownership, acceptance criteria, or decisions are unclear; do not silently assume material requirements.
• In --full-flow, verify upstream and downstream artifacts, decision-log entries, traceability links, acceptance criteria, and validation evidence before finalizing.
• In --full-flow, run or recommend the skill-appropriate gates, reviews, scripts, and validation commands needed for end-to-end confidence; document any blocked verification explicitly.
• When neither flag is supplied, follow the skill default rules and choose the least risky behavior for the request size and domain.

0.3 Output Rules

• Keep output structured with headings and bullets.
• Make findings, gaps, risks, and blockers explicit.
• Tie recommendations to evidence from the provided artifact, specs-refiniment/<feature-name>/<file.md> workspace, or user context.
• Include role ownership when the output creates follow-up work for BA, QA, Dev, PM, or Delivery.
• Return progress, completion, validation, and handoff summaries directly in the Codex response.
• Before the final response, emit the ai-sdlc-handoff/v1 contract with result, blockers, next_required, and next_optional; every action includes reason, command, and expected_artifact.
• Do not create summary.txt, *-summary.txt, or another standalone summary file unless the user explicitly requests one.
• Keep durable writes limited to the canonical lifecycle artifacts, decision log, human-readable index, and _ai_sdlc machine files.
• Let shared helpers migrate legacy paths on the next write; never overwrite or manually merge divergent legacy and canonical files.

0.4 Artifact Routing

• Maintain a feature decision log whenever this skill records, resolves, changes, or depends on a product, delivery, QA, security, validation, branching, implementation, or rollout decision.

• For PM, BA, QA, Delivery, discovery, planning, refinement, and readiness work, write decisions to specs-refiniment/<feature-name>/decision-log.md.

• For developer implementation SDD work, write decisions to specs/<feature-name>/decision-log.md.

• Each decision-log entry must include date, decision, context or evidence, options considered when relevant, owner, status, and links to affected artifacts, tasks, tests, or validation evidence.

• Use this exact decision-log structure:

  # Decision Log
  
  | ID | Date | Status | Owner | Decision | Context/Evidence | Options Considered | Affected Artifacts | Validation/Trace Links |
  | --- | --- | --- | --- | --- | --- | --- | --- | --- |
  | DEC-001 | YYYY-MM-DD | proposed / accepted / superseded / rejected | role or name | concise decision | source facts, artifact links, or evidence | option A; option B; recommended default | affected docs, tasks, code, tests, or rollout notes | requirement IDs, test IDs, validation commands, PRs, commits, or tickets |
  

• When writing or updating files, place PM, BA, QA, Delivery, discovery, planning, refinement, and readiness artifacts at specs-refiniment/<feature-name>/<file.md>.

• Use the path pattern specs-refiniment/<feature-name>/<file.md>; choose a stable feature slug when known, otherwise use tbd-<short-topic> for <feature-name>.

• Do not write this skill's output into specs/; that folder is reserved for developer implementation SDD artifacts.

• If the user explicitly asks to convert a refined artifact into developer implementation work, hand off to $ai-sdlc-sdd.

0.5 Feature State Machine

• Maintain feature lifecycle state in TOON at specs-refiniment/<feature-name>/_ai_sdlc/state.toon for refinement work and specs/<feature-name>/_ai_sdlc/state.toon for implementation work.
• Before executing this skill for a feature, check the state machine with python3 skills/_shared/state_machine.py check --feature <feature-name> --skill <this-skill-name> --workspace <refinement|implementation> --quick-flow|--full-flow.
• When this skill starts durable work, mark it in progress with begin; when the skill's required artifact or review is complete, mark it done with complete and include --artifacts <path> plus --decision-ref DEC-### when a decision was involved.
• In --full-flow, do not proceed when predecessor stages are incomplete, another lifecycle skill is active, or the state file reports a blocker.
• In --quick-flow, a predecessor skip is allowed only when continuing is low risk and the command includes --assumption "..." or --decision-ref DEC-###; record the same assumption or decision in decision-log.md.
• Use python3 skills/_shared/state_machine.py status --feature <feature-name> --workspace <refinement|implementation> --format toon to emit compact LLM-readable state before choosing the next skill.
• The state machine is feature-scoped: do not reuse a state.toon across unrelated feature folders.

0.6 Artifact Metadata And Metatags

• Every Markdown artifact generated or updated by this skill must start with an artifact_metadata YAML frontmatter block before the first visible heading.
• Use schema ai-sdlc-artifact-metadata/v1 and keep these fields current: feature, artifact, path, workspace, skill, flow_mode, state_file, decision_log, status, owner, created_at, updated_at, trace_ids, related_artifacts, validation, and metatags.
• metatags must include at minimum ai-sdlc, the workspace (refinement or implementation), this skill name, the artifact type or filename stem, and a lifecycle/status tag such as draft, review, approved, or validated.
• When --quick-flow is active, set flow_mode: quick, keep assumptions visible in the body, and add tags for major defaults or unresolved risk only when they help retrieval.
• When --full-flow is active, set flow_mode: full, keep blockers and validation evidence reflected in status, validation, trace_ids, and related_artifacts.
• Update metadata whenever the artifact path, status, owner, trace links, validation evidence, related artifacts, or decision references change.
• Metadata is an index for routing, retrieval, and traceability; it does not replace the artifact body, decision-log.md, or state.toon.

0.7 Specs Index

• Before searching across feature folders, inspect the compact LLM index first: specs-refiniment/_ai_sdlc/specs-index.toon for refinement work or specs/_ai_sdlc/specs-index.toon for implementation work.
• Use the human-readable index at specs-refiniment/specs-index.md or specs/specs-index.md when reporting feature coverage, artifact inventory, or handoff status to people.
• After this skill creates or materially updates an artifact, refresh the matching workspace index with python3 skills/_shared/ai_sdlc_specs_index.py --workspace <refinement|implementation> --quick-flow|--full-flow.
• In --quick-flow, rely on specs-index.toon to choose the smallest relevant artifact set before opening files.
• In --full-flow, verify the updated artifact appears in both specs-index.toon and specs-index.md before final handoff.
• The specs index summarizes artifact metadata and state; it does not replace reading the selected source artifacts when details, approvals, or validation evidence matter.

0.8 Complete Refinement Cascade

• Trigger the complete cascade only when the user explicitly asks for a full, complete, or end-to-end spec refinement or asks for every refinement artifact. A normal --full-flow call for one skill remains single-stage.
• Before the first durable write, run python3 skills/_shared/refinement_status.py --feature <feature-name> --gate full --format toon and start with the earliest reported next_skill, including stages earlier than this skill.
• Execute the existing refinement skills in lifecycle order with --full-flow: discovery, PRFAQ, delivery-package gap review, requirements readiness, goal/capability mapping, backlog gap review, backlog decomposition, story decomposition, release slicing, BA context, delivery spec, QA plan, QA gap review, test strategy, test cases, test suite, QA readiness, and delivery handoff.
• Produce all 18 canonical Markdown artifacts. release-slicing.md is mandatory for a complete cascade; when release slicing is not applicable, write an explicit evidence-backed N/A artifact and complete the stage instead of skipping it.
• After every stage, finalize its artifact, record required decisions, mark the stage done, and refresh the refinement indexes before selecting the next skill.
• Do not declare the cascade complete until python3 skills/_shared/refinement_status.py --feature <feature-name> --gate full --format markdown exits successfully with 18/18. If it fails, continue with the reported next skill or return the concrete blocker and remaining inventory in Codex.
• Surface checkpoint and final summaries in Codex only; never persist a cascade summary as a text file.

References

• Use scripts/story_map.py when deterministic scaffolding, planning, or formatting is useful for this workflow; pass the same --quick-flow or --full-flow flag that was supplied to the skill.
• Read references/story-structures.md when the task needs the detailed structure, checklist, or examples for this skill.

Script Usage

• In default and full flow, always run this skill's primary analysis script with --format toon --budget-tokens 24000 before drafting; explicit inputs are priority evidence but do not replace the rest of the feature package.

• Read this skill's reference file before writing sections. Use its detailed tables and quality bar, not only the compact scaffold headings.

• Run the primary script with --emit-template in the active flow mode to obtain the exact shared context headings and required stage table columns before section writes.

• Make every default/full artifact self-contained by completing all ten shared feature-context sections plus the stage-specific profile sections. Quick flow may use the compact stage-only draft.

• Follow every next_reads entry before finalization and list every consumed source in Source Coverage; do not claim whole-feature context from a partial source set.

• Keep the final artifact within --max-artifact-tokens 24000; condense repetition instead of dropping feature dimensions or source traceability.

• Run scripts/story_map.py before drafting or updating this skill's artifact when inputs are longer than a few bullets, when traceability matters, or when a flow flag is supplied. For agent analysis, pass --format toon, read anchors first, and open only next_reads; without that flag the script keeps its human-readable Markdown output.

• Quick flow analysis: python3 skills/ai-sdlc-user-story-decomposition/scripts/story_map.py --feature <feature-name> --quick-flow <input.md>...

• Full flow analysis: python3 skills/ai-sdlc-user-story-decomposition/scripts/story_map.py --feature <feature-name> --full-flow <input.md>...

• To write content, pass one canonical heading with --section "<section>"; provide only that section body on stdin, without H1, H2, frontmatter, or a temporary content file.

• Repeat --section for each required section, then run the same script with --finalize to validate the artifact and refresh metadata and specs indexes.

• The AI must not write or directly edit the routed Markdown artifact; the script owns scaffold creation, section placement, and durable file writes.

• Use --decision-row with one nine-cell Markdown table row on stdin when a decision-log entry is required.

• Legacy --emit-template, --emit-decision-log-entry, and --write remain available for compatibility.

• Use --quick-flow for first-pass synthesis with assumptions; use --full-flow before readiness, handoff, signoff, or any decision-sensitive output.

Purpose

Turn a clarified delivery package into implementable, actor-based user stories with acceptance logic and scenario coverage.

Use When

• The upstream package is clear enough to decompose.
• The user needs stories, acceptance criteria, and scenario coverage for delivery planning.

Do Not Use When

• The input package still has blocking gaps.
• The task only needs a high-level product narrative rather than delivery artifacts.

Workflow

1. Identify actors, goals, and outcomes.
2. Group related work into epics or capability areas when useful.
3. Write stories in actor-value form.
4. Add acceptance criteria and negative or edge scenarios where they materially affect delivery.
5. Capture dependencies, assumptions, open questions, and priority for each story cluster.

Story Rules

• Every story must name a concrete actor.
• Every story must state the user or business outcome it supports.
• Do not accept stories that are only UI elements or technical tasks without user/business value.
• Add failure, edge, and exception scenarios where omission would create delivery risk.
• Keep priorities tied to MVP scope, not personal preference.

Structures

Use references/story-structures.md.

Completion Criteria

• Stories cover the main actor journeys.
• Acceptance criteria are testable.
• Dependencies and open questions are visible.
• The story set is consistent with MVP scope and business goals.