| name | new-feature |
| description | Create a complete feature artifact scaffold — PRD slice, technical spec, and task breakdown. Use when starting work on any new feature, user story, or capability. Triggers on phrases like 'new feature', 'create a PRD for', 'start work on', 'scaffold a feature'. |
Skill: New Feature Scaffold
Purpose
Guide the practitioner through creating a complete, linked artifact chain for a new feature: PRD slice → Technical Specification → ADR identification → Task breakdown.
Execution steps
Step 1: Gather requirements (ask these questions before creating any files)
Ask the practitioner:
- What is the feature name? (5 words max for the title)
- What problem does it solve? (one sentence, no solution hints)
- Who is the user? (be specific — not "users," e.g., "authenticated enterprise customers")
- What does success look like? (ask for at least one quantified metric)
- What is explicitly OUT of scope?
- Is this feature AI-native (LLM-powered)? If yes, which capability?
Do not proceed until all five questions are answered.
Step 2: Determine the next available ID
Run:
ls project/.sdlc/specs/PRD-*.md 2>/dev/null | tail -1
The next PRD ID is one higher. If no PRDs exist, start at PRD-001. Use the same number for SPEC-NNN.
Step 3: Create the PRD slice
Copy templates/prd/PRD-TEMPLATE.md to project/.sdlc/specs/PRD-NNN-[kebab-feature-name].md.
Fill in:
- frontmatter: id, title, date (today), sprint (current sprint from
project/sprints/ folder name)
- Problem statement: one sentence from practitioner's answer to Q2
- User stories: 2–3 EARS-format stories capturing the practitioner's feature description
- Success metrics: quantified from practitioner's answer to Q4
- Out of scope: from practitioner's answer to Q5
Present the draft PRD to the practitioner for review before proceeding.
Step 4: Create the technical specification
After practitioner approves the PRD (set status: in-review and ask for confirmation):
Copy templates/spec/SPEC-TEMPLATE.md to project/.sdlc/specs/SPEC-NNN-[kebab-feature-name].md.
Fill in:
- frontmatter: id, parent (PRD-NNN), date, sprint
- Architecture overview: propose the component structure based on the feature description
- API contracts: draft based on the user stories
- Data models: draft minimal schema needed
- Acceptance criteria: map each EARS story to a measurable AC
For AI-native features, also complete:
- Add AI quality acceptance criteria (probabilistic thresholds)
- Note which model, prompt strategy, and orchestration decisions need ADRs
Present the draft SPEC for review.
Step 5: Identify required ADRs
Analyze the SPEC and list every decision that needs an ADR:
Always required for new features:
Required for AI-native features:
List the required ADRs to the practitioner. Offer to create stubs:
I've identified [N] ADRs needed before implementation can begin:
1. [ADR type]: [Decision area]
2. [ADR type]: [Decision area]
Would you like me to create stub files for these now, or will you fill them in manually?
Step 6: Create the task breakdown
After the SPEC is approved:
Analyze the SPEC's "Implementation order" section and create TASK files in project/.sdlc/specs/ using templates/task/TASK-TEMPLATE.md.
For each task:
- Generate the next available TASK-NNN ID
- Fill in spec reference, session start instructions, file scope, and implementation specification from the SPEC
- Estimate token budget: small (<16K), medium (16K–64K), large (>64K)
- Mark parallel-executable tasks with
parallel: true
- Set dependencies between sequential tasks
Step 7: Summary report
Present a summary to the practitioner:
## Feature scaffold complete: [Feature Name]
### Artifacts created:
- PRD-NNN: [path] — status: in-review
- SPEC-NNN: [path] — status: draft
- TASK-NNN through TASK-NNN: [N tasks created]
### ADRs required before implementation:
- [ ] [ADR type] for [decision]: create with ./tools/scripts/new-adr.sh
### Next steps:
1. Review and approve PRD-NNN
2. Review and approve SPEC-NNN
3. Create and accept all required ADRs
4. Start TASK-NNN (first sequential task) — estimated [X]K tokens context
BHIL AI-First Development Toolkit — Skill version 1.0
