✅ Requirements → Test Coverage Mapper (RTM) Skill
This skill turns a PRD, user stories, acceptance criteria, and constraints into a traceability-driven test coverage plan:
- a Requirements Traceability Matrix (RTM)
- a gap & ambiguity report
- test design recommendations across levels (unit/API/UI/e2e/non-functional)
- automation candidates and CI gating suggestions
The output is designed to be used as a living artifact and “single source of truth” for coverage, readiness, and auditability.
🎯 When to Use
Use this skill when you need to:
- Validate coverage completeness before/after implementation
- Convert PRD/user stories into a test plan that is traceable and reviewable
- Identify missing acceptance criteria, ambiguous requirements, or hidden scope
- Support change impact analysis when requirements evolve
- Build a risk-based regression strategy or release readiness assessment
🧭 Operational Workflow
Phase 0: Strategy Selection (Context First)
Classify the work to adjust rigor and output depth:
- Delivery stage: MVP / Iteration / Hardening / Release / Hotfix
- Domain risk: Low / Medium / High (payments, auth, compliance = high)
- Change surface: UI / API / Data / Infra / Cross-cutting
- AI criticality: None / Supporting / Core
- Target tooling (optional): Jira / Azure DevOps / TestRail / GitHub Issues
If unknown, mark as TBD and proceed with safe defaults.
Phase 1: Discovery (Mandatory Questions)
Before producing the RTM, ask at least 5 focused questions. Must cover:
- Test basis source
- PRD link/text? user stories? AC? designs? (what is “source of truth”?)
- Scope boundaries
- What is explicitly in/out? any non-goals?
- Quality attributes
- performance, security, accessibility, reliability, observability expectations?
- Data & environments
- test envs available, data seeding/masking rules, third-party dependencies?
- Release constraints
- deadline, MVP cuts, rollout type (flagged, staged, big-bang)?
- Definition of Done / acceptance
- who signs off and what evidence is required?
If the user cannot answer, capture assumptions explicitly in the output.
Phase 2: Normalization (Make Requirements Traceable)
The agent must normalize input into atomic, testable requirements:
- Split compound requirements into smaller “testable statements”
- Assign stable IDs if missing:
REQ-001,REQ-002… (requirements)US-001… (user stories)AC-001… (acceptance criteria)
- Mark ambiguous statements as
NEEDS_CLARIFICATION
Phase 3: Coverage Design (Levels + Risks + Data)
For each requirement, propose coverage across:
- Functional: positive/negative, edge cases, permissions
- Integration: APIs, events, DB, third parties
- UX: key flows, accessibility, error messaging
- Non-functional: performance, security, reliability, observability
- AI-specific (if applicable): evaluation, drift, hallucinations/failure modes, fallbacks
Use risk-based thinking:
- assign impact (H/M/L) and likelihood (H/M/L)
- derive priority and test depth from risk
🧾 Output Schema (Strict)
Your output MUST follow this structure and order.
0) Document Metadata
- Version:
0.x - Status: Draft / Review / Approved
- Owner: (name or
TBD) - Last Updated: YYYY-MM-DD
- Sources Used: (PRD/story links or
Provided text in prompt) - Assumptions Policy: “No assumptions unless explicitly listed below.”
1) Executive Coverage Summary
- Coverage status: % requirements mapped / unmapped
- High-risk areas and proposed test focus
- Top 5 gaps / blockers
- Recommended next actions (what to clarify, what to implement, what to test first)
2) Requirements Traceability Matrix (RTM)
A table with one row per atomic requirement.
| Req ID | Requirement / Statement | Source (Story/AC) | Risk (I×L) | Test Levels (Unit/API/UI/E2E/NFR) | Test Scenarios (IDs) | Automation Candidate | Status (Planned/Exists/Missing) | Notes | |-------:|--------------------------|-------------------|------------|-----------------------------------|----------------------|----------------------|----------------------------------|------|
Rules:
- Every requirement must have at least one scenario ID, or be flagged as
MISSING_TEST. - Every scenario must have a clear expected outcome.
- If requirement is ambiguous → mark
NEEDS_CLARIFICATIONand propose exact questions.
3) Test Scenario Catalog (Specification by Example)
List scenarios referenced in the RTM.
Each scenario must include:
- Scenario ID (
TS-001) - Title
- Level(s): Unit/API/UI/E2E/NFR
- Preconditions / Data
- Steps (high-level)
- Expected results (assertable)
- Observability notes (logs/metrics/traces that confirm behavior)
- Negative cases / edge cases (if relevant)
4) Gap & Ambiguity Report
A prioritized list of issues that block good testing:
- Missing acceptance criteria
- Undefined error handling behavior
- Missing NFR targets (latency, rate limits, availability)
- Unclear roles/permissions
- Testability concerns (no hooks, unstable IDs, no deterministic mode)
Include:
- Gap ID (
GAP-001) - Description
- Why it matters
- Suggested resolution (exact question or requirement rewrite)
5) Risk-Based Prioritization (What to Test First)
Provide a prioritized plan:
- Tier 0 (release blockers / critical flows)
- Tier 1 (high risk / high usage)
- Tier 2 (regression breadth)
For each tier include:
- linked Req IDs and Scenario IDs
- recommended execution cadence (per PR, nightly, pre-release)
- minimal “confidence suite” for hotfixes
6) Automation & CI Recommendations
- Which scenarios are best suited for automation (and why)
- What should remain manual (and why)
- CI gating suggestion:
- smoke suite vs regression suite
- flaky risk notes
- environment needs
If tooling is known (e.g., Azure DevOps/Jira/TestRail), propose how to encode links (IDs/tags). If unknown → provide tool-agnostic tagging conventions.
7) Assumptions, Dependencies, and Change-Impact Notes
- Assumptions (explicit list)
- Dependencies (systems/teams/vendors)
- Change impact rules:
- “If REQ-xxx changes, rerun suites: …”
- “Areas likely to regress: …”
✅ Quality Rules (Non-Negotiable)
DO
- Make every requirement atomic and testable
- Use stable IDs for traceability
- Flag unknowns; don’t invent constraints
- Prefer measurable outcomes and concrete expected results
- Recommend test levels intentionally (not everything must be E2E)
DON’T
- Don’t hallucinate requirements, flows, or data models
- Don’t produce only UI tests (must consider unit/API/NFR)
- Don’t accept ambiguous requirements without logging them as gaps
- Don’t mark coverage “complete” if AC is missing or unclear
🧠 AI Self-Review Checklist
Before final output:
- [ ] Every requirement has an ID and is atomic
- [ ] Every requirement maps to ≥1 scenario OR is flagged missing
- [ ] Ambiguities are captured as explicit questions
- [ ] Risk is assigned and used to prioritize
- [ ] Non-functional coverage is addressed where relevant
- [ ] Assumptions are listed explicitly
🧪 Example (Mini)
RTM (excerpt)
| Req ID | Requirement | Source | Risk | Levels | Scenarios | Auto | Status | Notes | |---:|---|---|---|---|---|---|---|---| | REQ-001 | User can reset password via email link valid for 15 minutes | US-002 / AC-004 | H×M | API/UI/E2E | TS-001, TS-002, TS-003 | Yes | Planned | Need rate-limit spec |
Scenario (excerpt)
TS-002 - Password reset token expires
- Levels: API + UI
- Data: user exists; token issued at T0
- Steps: request reset → wait 16 min (or simulate time) → attempt reset
- Expected: reset rejected with
TOKEN_EXPIRED; user not logged in; audit log entry exists
✅ Outcome
Used correctly, this skill produces a traceability-driven plan that:
- proves coverage,
- exposes gaps early,
- guides automation investment,
- and supports release readiness decisions.