| name | arch:gather-nfr |
| description | Collect non-functional requirements through interactive Q&A. Asks targeted questions about performance, scalability, security, reliability, and cost constraints. Invokable with /gather-nfr. |
Gather Non-Functional Requirements
Overview
This skill collects Non-Functional Requirements (NFRs) through an interactive questionnaire. It:
- Reads active story from
$AGENT_DOCS_DIR/active-story.yaml
- Analyzes story context (title, labels, body) to tailor questions
- Asks targeted questions across 6 NFR categories
- Collects responses using AskUserQuestion tool
- Appends NFRs to
$AGENT_DOCS_DIR/active-story.yaml for later use
NFRs inform architectural decisions, implementation choices, and ADR content.
NFR Categories
| Category | Always Ask? | Key Questions |
|---|
| Performance | User-facing features | DAU, response time, concurrency |
| Scalability | Growth expected | Growth projection, geography, peaks |
| Data & Storage | Creates/modifies data | Retention, volume, backup SLA |
| Security | Always | Sensitive data, auth, compliance |
| Reliability | Production features | Downtime SLA, error rate, monitoring |
| Cost | New infrastructure | Budget, preferred AWS services |
See @references/nfr-categories.md for detailed question tables and example responses.
Workflow
Step 1: Verify Active Story Exists
STORY_FILE="$CLAUDE_PROJECT_DIR/$AGENT_DOCS_DIR/active-story.yaml"
if [ ! -f "$STORY_FILE" ]; then
echo "❌ No active story found. Run /fetch-story first."
exit 1
fi
Step 2: Load and Analyze Story Context
const story = yaml.load(fs.readFileSync('$AGENT_DOCS_DIR/active-story.yaml', 'utf-8'));
const title = story.title.toLowerCase();
const labels = story.labels.map(l => l.toLowerCase());
const body = story.body.toLowerCase();
Determine applicable categories:
| Category | Skip if label contains... |
|---|
| Performance | "internal-tool" |
| Scalability | "poc" or "prototype" |
| Data & Storage | "read-only" |
| Security | Never skip |
| Reliability | "experimental" |
| Cost | Body mentions "existing infrastructure" |
Steps 3–8: Ask Category Questions
See @references/workflow-questions.md for the exact question text, options, defaults, and skip conditions for each category (Performance, Scalability, Data & Storage, Security, Reliability, Cost).
Step 9: Append NFRs to Active Story
story.nfrs = {
performance: { dailyActiveUsers: "...", maxResponseTime: "...", concurrentUsers: "..." },
scalability: { growthProjection: "...", geographic: "...", peakPatterns: "..." },
data: { dataRetention: "...", dataVolumePerUser: "...", backupSLA: "..." },
security: { sensitiveData: [...], authentication: "...", compliance: [...] },
reliability: { acceptableDowntime: "...", errorRate: "...", monitoring: "..." },
cost: { budget: "...", preferredServices: [...] }
};
fs.writeFileSync('$AGENT_DOCS_DIR/active-story.yaml', yaml.dump(story));
Step 10: Report Summary
✓ Non-Functional Requirements Collected
Performance: <DAU> DAU, <response time>, <concurrency>
Scalability: <growth>, <geography>, <peak patterns>
Data: <retention>, <volume/user>, <backup SLA>
Security: <sensitive data>, <auth>, <compliance>
Reliability: <downtime SLA>, <error rate>, <monitoring>
Cost: <budget>, <preferred services>
✓ NFRs saved to $AGENT_DOCS_DIR/active-story.yaml
Next steps:
Run /gather-context to collect technical context
Or run /play-story to continue the full workflow
Error Handling
See @references/error-handling.md for: no active story, NFRs already exist (overwrite prompt), and file write failures.
Question Strategy
See @references/question-strategy.md for recommendation logic per label, smart skipping rules, multi-select vs single-select guidance, and question count targets.
Integration with Other Skills
Called by /play-story
/play-story → /fetch-story → /gather-nfr (this) → /gather-context → /arch:create-adr
Output Used by Other Skills
/arch:create-adr — NFRs become Decision Drivers; security/compliance guides technology choices/gather-context — security requirements and preferred services guide code search
Best Practices
- 2-3 questions per category; skip irrelevant ones (target ~10-15 total)
- Pre-select sensible defaults — respect user time
- Use multiple choice whenever possible; text input sparingly
- Include "N/A" or "No constraint" options
- Store skipped categories as
{ skipped: true, reason: "..." }
Example Session
See @references/example-session.md for a complete walkthrough of /gather-nfr on a payment checkout story.
Summary
The /gather-nfr skill streamlines NFR collection by:
- Intelligent questioning — tailors questions to story context and labels
- Smart defaults — recommends sensible values based on feature type
- Category skipping — avoids irrelevant questions
- Structured storage — saves NFRs in reusable YAML format
- ADR-ready — output directly feeds into decision records
