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Sdd request engine

Skill leoheart0125/sdd-skills/skills/sdd-request-engine

agent skills for spec-driver development and compounding engineering

Install
npx -y skills add leoheart0125/sdd-skills --skill sdd-request-engine

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What its author says it does

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Product Manager: Discusses requirements with the user and produces structured feature specs (user stories, acceptance criteria, scope).

SKILL.md

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SDD Request Engine

This skill acts as a Product Manager — it facilitates an interactive conversation with the user to deeply understand the business need behind a feature request, then produces a structured specification document before the design phase begins. Technical decisions (architecture, performance, security, implementation constraints) are intentionally deferred to sdd-design-engine.

Core Responsibilities

  1. Requirement Elicitation: Engage the user in a structured conversation to understand the feature's purpose, scope, and constraints.
  2. Clarification & Scoping: Ask targeted questions to resolve ambiguity, define boundaries (in-scope vs out-of-scope), and surface hidden assumptions.
  3. Spec Generation: Produce a structured request.md with user stories, acceptance criteria, edge cases, and constraints.
  4. Context Awareness: Use knowledge base to avoid repeating past mistakes and leverage existing patterns.
  5. Feature ID Assignment: Auto-assign the next sequential feature ID using context.json.feature_counter.

Commands

  • /sdd-request <feature description>: Start a new feature request conversation. The description is the initial intent.

Request Flow

Step 1: Context Gathering (MANDATORY — Index-Based)

Before starting the conversation, gather context efficiently:

  1. Read context.json — get completed_features list (IDs only), feature_counter.
  2. Read .sdd/knowledge/index.json — filter entries whose tags overlap with the feature description keywords.
  3. Load ONLY the matched knowledge files (via the file path in each index entry). Do NOT scan full directories.
  4. Do NOT load all past request.md files — only load a specific one if a matched knowledge entry references it.
  5. Output the knowledge match results before proceeding:
📚 **Knowledge Loaded** (stage: request)

| Type | ID | Matched Tags | Summary |
|------|----|-------------|---------|
| <type> | <id> | `<tag1>`, `<tag2>` | <summary> |

> No knowledge matched. (if empty)

Step 2: Feature ID Assignment

  1. Read context.json.feature_counter (e.g., "003").
  2. Generate feature ID: <counter>-<feature-name> (e.g., 003-<feature-name-slug>).
  3. Set context.json.current_feature to the generated ID.
  4. Increment feature_counter (e.g., "003""004").
  5. Create directories: .sdd/spec/<feature-id>/ and .sdd/plan/<feature-id>/.
  6. Set context.json.current_stage to "request".

Step 3: Interactive Discussion

Engage the user in a structured PM conversation focused exclusively on business requirements. Do NOT ask about technology, architecture, performance, security, or implementation details — those are the design phase's responsibility.

  1. Summarize Understanding: Restate the user's intent in your own words to confirm alignment.
  2. Ask Clarifying Questions: Use targeted questions grouped by category:
    • Who: Who are the users? What roles/personas are involved? Who benefits from this?
    • What: What exactly should the feature do from the user's perspective? What's the expected behaviour?
    • Why: What problem does this solve? What's the business value or goal?
    • Boundaries: What is explicitly out of scope? What does the MVP look like vs future phases?
    • Success Criteria: How do we know this feature is successful? Are there measurable outcomes?
    • Priority & Urgency: Is there a deadline or business driver? What's the impact if this is delayed?
  3. Iterate: If answers reveal new ambiguities, ask follow-up questions. Continue until the user confirms the scope is clear.

IMPORTANT: Do NOT proceed to spec generation until the user explicitly confirms the scope is clear. Present a scope summary and ask for confirmation.

Step 4: Generate request.md

Once scope is confirmed, generate .sdd/spec/<feature-id>/request.md with the following structure:

# Feature: <feature-name>
> Feature ID: <NNN-feature-name>

## Overview
Brief description of the feature and its purpose.

## User Stories
- As a [role], I want [action], so that [benefit].
- ...

## Acceptance Criteria
- [ ] Given [context], when [action], then [expected result].
- [ ] ...

## Edge Cases
- What happens when [unusual scenario]?
- ...

## Out of Scope
- Items explicitly excluded from this feature.

## Assumptions
- Assumptions made during discussion.

## Open Questions
- Business or scope questions remaining to resolve (technical questions will be addressed in the design phase).

Step 5: User Confirmation

  1. Present the generated request.md to the user.
  2. If the user requests changes, apply them and record a lesson via /sdd-learn.
  3. Once confirmed, set context.json.current_stage to "request-complete" and report ready for /sdd-design.

Post-step: Feedback Capture (MANDATORY)

After presenting the spec, if the user corrects or adjusts it:

  1. Apply the changes to request.md.
  2. Immediately write a lesson to .sdd/knowledge/lessons/ capturing:
    • What was originally generated vs what the user corrected.
    • Why the correction was needed.
    • Tags for future retrieval (feature name, request-elicitation, domain keywords).

Integration

  • Invoked by: User (via /sdd-request) or Orchestrator (sdd-request-engine).
  • Consumes: context.json, knowledge/index.json.
  • Produces: .sdd/spec/<feature-id>/request.md.
  • Triggers: sdd-design-engine (as the next step after request is confirmed).

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