Install any skill in seconds. Free to start, no credit card required.
Get Started Free →Break requirements into epics and user stories. Use when the user says "create the epics and stories list"
.claude/skills/bmad-code-org-bmad-create-epics-and-stories/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-07 | ✗→✓ | ▲ Improved | -10% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 4% | 0% |
| case-09 | ✗→✓ | ▲ Improved | -8% | 0% |
| case-10 | ✗→✓ | ▲ Improved | -3% | 0% |
| case-13 | ✗→✓ | ▲ Improved | 41% | 0% |
Goal: Transform PRD requirements and Architecture decisions into comprehensive stories organized by user value, creating detailed, actionable stories with complete acceptance criteria for the Developer agent.
Your Role: In addition to your name, communication_style, and persona, you are also a product strategist and technical specifications writer collaborating with a product owner. This is a partnership, not a client-vendor relationship. You bring expertise in requirements decomposition, technical implementation context, and acceptance criteria writing, while the user brings their product vision, user needs, and business requirements. Work together as equals.
steps/step-01-validate-prerequisites.md) resolve from the skill root.{skill-root} resolves to this skill's installed directory (where customize.toml lives).{project-root}-prefixed paths resolve from the project working directory.{skill-name} resolves to the skill directory's basename.This uses step-file architecture for disciplined execution:
stepsCompleted array when a workflow produces a documentstepsCompleted in frontmatter before loading next stepRun: uv run {project-root}/_bmad/scripts/resolve_customization.py --skill {skill-root} --project-root {project-root} --key workflow
If the script is not found, BMad is not set up here. Offer to run the bmad skill's setup, installing bmad first if you do not have it (npx skills add bmad-code-org/BMAD-METHOD --skill bmad), then run the command again.
If it fails for any other reason, resolve the workflow block yourself by reading these three files in base → team → user order and applying the same structural merge rules as the resolver:
{skill-root}/customize.toml — defaults{project-root}/_bmad/custom/{skill-name}.toml — team overrides{project-root}/_bmad/custom/{skill-name}.user.toml — personal overridesAny missing file is skipped. Scalars override, tables deep-merge, arrays of tables keyed by code or id replace matching entries and append new entries, and all other arrays append.
Execute each entry in {workflow.activation_steps_prepend} in order before proceeding.
Treat every entry in {workflow.persistent_facts} as foundational context you carry for the rest of the workflow run. Entries prefixed file: are paths or globs under {project-root} — load the referenced contents as facts. All other entries are facts verbatim.
Run: uv run {project-root}/_bmad/scripts/resolve_config.py --project-root {project-root} --key modules.bmm.planning_artifacts --key modules.bmm.project_knowledge
{planning_artifacts} for output location and artifact scanning{project_knowledge} for additional context scanningGreet the user.
Execute each entry in {workflow.activation_steps_append} in order.
Activation is complete. If activation_steps_prepend or activation_steps_append were non-empty, confirm every entry was executed in order before proceeding. Do not begin the main workflow until all activation steps have been completed.
Read fully and follow: steps/step-01-validate-prerequisites.md to begin the workflow.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 10,048 | 9,589 | -5% | 1 | 1 | 0% | 1,649 | 1,607 | -3% | 0 | 0 | — |
case-02 | fail→fail | 20,544 | 16,083 | -22% | 1 | 1 | 0% | 1,307 | 1,573 | +20% | 0 | 0 | — |
case-03 | fail→fail | 10,275 | 8,629 | -16% | 1 | 1 | 0% | 1,459 | 1,583 | +8% | 0 | 0 | — |
case-04 | pass→pass | 10,204 | 10,274 | +1% | 1 | 1 | 0% | 1,534 | 1,837 | +20% | 0 | 0 | — |
case-05 | fail→fail | 9,287 | 13,003 | +40% | 1 | 1 | 0% | 1,431 | 3,287 | +130% | 0 | 0 | — |
case-06 | pass→pass | 37,714 | 11,087 | -71% | 1 | 1 | 0% | 2,301 | 2,394 | +4% | 0 | 0 | — |
case-07 | fail→pass | 39,931 | 5,613 | -86% | 1 | 1 | 0% | 2,317 | 2,089 | -10% | 0 | 0 | — |
case-08 | fail→pass | 11,098 | 3,571 | -68% | 1 | 1 | 0% | 1,573 | 1,640 | +4% | 0 | 0 | — |
case-09 | fail→pass | 20,667 | 5,610 | -73% | 1 | 1 | 0% | 2,342 | 2,147 | -8% | 0 | 0 | — |
case-10 | fail→pass | 25,678 | 2,374 | -91% | 1 | 1 | 0% | 1,559 | 1,517 | -3% | 0 | 0 | — |
case-11 | pass→fail | 10,891 | 6,850 | -37% | 1 | 1 | 0% | 1,498 | 2,008 | +34% | 0 | 0 | — |
case-12 | fail→fail | 34,133 | 9,986 | -71% | 1 | 1 | 0% | 1,907 | 1,851 | -3% | 0 | 0 | — |
case-13 | fail→pass | 99,746 | 9,158 | -91% | 1 | 1 | 0% | 1,723 | 2,421 | +41% | 0 | 0 | — |
case-14 | fail→pass | 28,252 | 12,203 | -57% | 1 | 1 | 0% | 1,900 | 2,519 | +33% | 0 | 0 | — |
case-15 | pass→fail | 6,750 | 5,656 | -16% | 1 | 1 | 0% | 914 | 2,001 | +119% | 0 | 0 | — |
case-16 | fail→fail | 14,156 | 11,162 | -21% | 1 | 1 | 0% | 2,014 | 2,740 | +36% | 0 | 0 | — |
case-17 | fail→pass | 49,283 | 4,673 | -91% | 1 | 1 | 0% | 2,167 | 1,746 | -19% | 0 | 0 | — |
case-18 | pass→pass | 21,716 | 6,311 | -71% | 1 | 1 | 0% | 1,953 | 2,183 | +12% | 0 | 0 | — |
case-19 | pass→pass | 30,818 | 6,315 | -80% | 1 | 1 | 0% | 1,375 | 1,933 | +41% | 0 | 0 | — |
case-20 | fail→fail | 77,806 | 32,428 | -58% | 1 | 1 | 0% | 3,806 | 6,935 | +82% | 0 | 0 | — |
case-21 | fail→fail | 8,326 | 15,315 | +84% | 1 | 1 | 0% | 1,235 | 1,615 | +31% | 0 | 0 | — |
case-22 | fail→pass | 10,449 | 22,299 | +113% | 1 | 1 | 0% | 1,295 | 4,701 | +263% | 0 | 0 | — |
case-23 | pass→pass | 22,612 | 5,604 | -75% | 1 | 1 | 0% | 1,508 | 2,088 | +38% | 0 | 0 | — |
case-24 | fail→fail | 20,701 | 4,531 | -78% | 1 | 1 | 0% | 1,813 | 1,851 | +2% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 24 cases were attempted, and 19 counted toward the lift figure. The other 5 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +25 percentage points is the difference between those two pass rates over the 19 comparable cases. 3 cases got worse with the skill loaded, and they are included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.