skills/breakdown/SKILL.md
Converts repo stories and planning docs into directly executable, dependency-aware work items with traceability and execution handoff.
npx skillsauth add sirius-cc-wu/sirius-skills breakdownInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Use this skill after feature-level discovery and design to turn repo stories into directly executable work.
If you need a starting folder for a new feature, scaffold the default breakdown files with:
python3 skills/breakdown/scripts/scaffold_breakdown.py <feature-slug>
By default this creates:
docs/features/<feature-slug>/slice-planning.md
docs/features/<feature-slug>/slice-traceability.md
If .skills/planning.json defines planning_dir, the scaffold uses
<planning_dir>/<feature-slug>/ instead unless --base-dir is passed.
For subfeature-local breakdown work, you can also scaffold directly into an existing subfeature path:
python3 skills/breakdown/scripts/scaffold_breakdown.py \
docs/features/<feature-slug>/subfeatures/<subfeature-id>
When the target path is a real subfeature with .subfeature-meta.json,
the scaffold seeds subfeature context from the metadata and impact-analysis.md
so the resulting breakdown artifacts stay anchored to the affected parent
stories, slices, and baseline docs.
review-planning; after human approval and planning commit, execution can bootstrap with slice.<feature_path>/slice-planning.md<feature_path>/slice-traceability.mdResolve <feature_path> as either:
<planning_dir>/<feature-slug>/ for canonical feature planning
<planning_dir>/<feature-slug>/subfeatures/<subfeature-id>/ for a selected subfeature
If .skills/planning.json defines planning_dir, use that as <planning_dir>.
Otherwise default to docs/features.
Preferred execution output when available:
Use assets/slice-planning-template.md as the default starting point for <feature_path>/slice-planning.md.
Use assets/slice-traceability-template.md as the default starting point for <feature_path>/slice-traceability.md.
Use scripts/scaffold_breakdown.py when you want both files scaffolded together from a feature slug or an explicit subfeature path.
S, M, L, and XL.low, medium, or high.XL item before creating execution-ready slices.XL stories may split. A smaller S/M/L story may still split when risk, coupling, or validation shape would make one packet brittle.Use size as the default signal and risk as the override signal.
S and M stories usually stay as one planned slice unless there is a clear reason to split.L stories may stay whole when the work is cohesive and low-risk, but may split when the work naturally falls into independently verifiable packets.XL stories should be split before they become execution-ready.high risk can justify splitting even when the story is not XL.Treat risk as elevated when one or more of these are true:
When you choose keep, split, or defer, record both the size and the risk and explain the primary reason in slice-planning.md.
Use increments to bridge repo-level planning and slice-level execution:
slice-planning.md and slice-traceability.md, not in spec-slice stateDefault to single-agent execution when:
Prefer multi-agent execution only when:
When a story becomes execution-ready:
multi-agent, record lane ownership, handoff targets, and an integration checkpoint after each laneUse slice-planning.md as the execution backlog:
slice-planning.mdslice-planning.mdslice-planning.mdslice-traceability.mdRecord slices and dependencies directly in slice-planning.md and use it as the primary execution backlog.
Unless the repository explicitly overrides slice naming in
.skills/conventions.json, use this default convention:
<scope-prefix>-<capability-slug>slice-*Examples:
atf-read-file, tpm-runner, wmt-write-fileshp-store, dmc-capture-policy, mrc-retrieval-startupTreat repo story IDs as the planning-system identifiers and planned slice IDs as the execution identifiers.
Default mapping:
slice-traceability.mdPreferred description shape for a new slice:
Story: <story-id>
Increment: <increment-id>
Slice: <short slice name>
Acceptance:
- ...
Validation:
- ...
If one repo story splits into multiple executable work items:
user-stories.mdslice-traceability.md with one planned
slice per rowOnly reuse the repo story ID as the planned slice ID when all of the following are true:
Otherwise, use an opaque slice ID and keep the repo story ID in traceability metadata.
discover.md, system-design.md, optional ui-design.md, and user-stories.md.
For subfeatures, also read impact-analysis.md and the canonical feature's
user-stories.md, slice-planning.md, and slice-traceability.md.single-agent or multi-agent handling where relevant and record lane assumptions.slice-planning.md and slice-traceability.md with increment groupings, dependency notes, parallel-safe lanes, and integration checkpoints as needed.python3 skills/guide-planning/scripts/manage_planning.py sync-status <feature-selector> --through breakdown_ready so planning metadata records that breakdown artifacts are ready for review.review-planning and later bootstrap by slice after approval and planning commit.When generating slice-planning.md, start from assets/slice-planning-template.md and replace placeholders rather than inventing a new structure each time.
When generating slice-traceability.md, start from assets/slice-traceability-template.md and replace placeholders rather than inventing a new table shape each time.
slice-planning.md into a slice-scoped execution checklist; that belongs to guide-execution and blueprint later.slice-traceability.md row;
repeat the story row so each planned slice can be mapped back deterministically.slice-* IDs unless a repository-specific convention
explicitly requires them.tools
Create or resume a dedicated git worktree for one feature or subfeature, drive `ship` inside that worktree, and hand the finished branch back as a pull request to the original branch.
testing
Reviews one completed feature or subfeature against final implementation and slice artifacts, then records a durable reconciliation block before archive.
development
Tightens durable repo-level rules in AGENTS.md and adjacent governance surfaces when repeated drift reveals a policy gap.
data-ai
Resolves one reviewed feature or subfeature backlog into remaining planned slices and routes each active slice to the next owning execution step with one commit per completed slice.