refactor(topics): 멀티 에이전트용 지식 재편 — _Common(공통 기본기) + Domain_* 구조
에이전트 8종(대화형/프로그래머 C·S/디자이너/설계자/기획자/QA/PD/PM)에게 [공통 기본 능력 + 롤별 Specialty] 2층으로 지식을 주입하기 위한 재분류. 문서 내용·포맷은 무수정, 폴더 이동만 (6,372개 문서 수 보존 확인). - Topic_Programming → Domain_Programming (내부 구조 보존) - Topic_Graphic → Domain_Design - Topic_Business → Domain_Product - Topic_General → Domain_General - _Common 신설: Math(구 Topic_Math_Specialty), Reasoning(구 General/From_Thinking & Reasoning), Reasoning_Creativity(구 General/From_창의성), Communication(Poetic_Blog_Writing + From_writing) - 타 도메인의 From_* 폴더는 유지 (출처 표기일 뿐, 이미 도메인에 맞게 분류된 문서) - 빈 폴더 정리 (memory/procedures) - 에이전트→폴더 매핑은 workspace의 .astra/agent-knowledge-map.json (9개 에이전트) Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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Antigravity Agent
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---
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id: wiki-2026-0508-requirements
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title: Requirements
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [Software-Requirements, Functional-Requirements, Non-Functional-Requirements]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.9
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verification_status: applied
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tags: [requirements, spec, product, engineering]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: markdown
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framework: gherkin
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---
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# Requirements
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## 매 한 줄
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> **"매 잘 쓴 requirement = 절반의 implementation"**. Requirements engineering은 매 stakeholder의 needs를 매 unambiguous, testable, prioritized statements로 매 변환 — 2026 의 매 AI-aided spec drafting (Claude/GPT-5)이 매 standard, 매 BDD/Gherkin은 매 living spec format 의 dominant.
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## 매 핵심
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### 매 분류
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- **Functional (FR)**: 매 system이 매 무엇을 하는가 — input/output, behavior.
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- **Non-functional (NFR)**: 매 어떻게 — performance, security, scalability, accessibility, reliability (SLO/SLA).
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- **Constraints**: tech stack, regulatory (GDPR, HIPAA), budget.
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- **Assumptions / Dependencies**: external upon 매 의존.
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### 매 Quality criteria (INVEST + SMART)
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- **Independent, Negotiable, Valuable, Estimable, Small, Testable** (user stories).
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- **Specific, Measurable, Achievable, Relevant, Time-bound**.
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- 매 Ambiguity의 매 적: "fast", "user-friendly" → 매 numeric ("p95 < 200ms", "SUS > 80").
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### 매 Formats (modern 2026)
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- **User story**: `As a <role>, I want <goal> so that <benefit>`.
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- **Acceptance criteria**: Given-When-Then (Gherkin) — executable via Cucumber/pytest-bdd.
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- **Job stories** (alternative): `When <situation>, I want to <motivation>, so I can <outcome>`.
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- **PRD** (Product Requirements Doc): living markdown in repo, AI-assisted draft.
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- **RFC / Design doc**: technical spec for engineering decisions.
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### 매 AI-aided workflow (2026)
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1. PM의 매 rough idea → Claude Opus 4.7 로 PRD draft.
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2. Engineering review → AI 의 매 ambiguity flags ("매 'fast'를 quantify").
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3. Gherkin scenarios 매 generate → directly executable.
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4. Trace matrix (req → test → code) 매 AI auto-link.
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5. Change requests → AI의 매 impact analysis (영향 받는 spec/test/code).
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### 매 응용
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1. SaaS feature spec.
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2. Embedded system safety (DO-178C, ISO 26262 traceability).
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3. RFP/contract scope.
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4. AI agent task specification (prompt = 매 spec).
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## 💻 패턴
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### User story + acceptance criteria
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```markdown
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## US-142: Email-based password reset
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**As a** registered user
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**I want** to reset my password via email
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**So that** I can regain access without contacting support
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### Acceptance Criteria (Gherkin)
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\`\`\`gherkin
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Feature: Password reset
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Scenario: Valid email triggers reset link
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Given a user with email "alice@example.com" exists
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When I POST /auth/reset with that email
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Then a reset email is sent within 30 seconds
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And the email contains a token valid for 1 hour
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Scenario: Unknown email returns generic success
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Given no user with email "ghost@example.com"
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When I POST /auth/reset with that email
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Then the response is 200 OK
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And no email is sent
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# Reason: avoid user enumeration
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\`\`\`
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### NFR
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- p95 endpoint latency < 300ms
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- Email delivery SLA: 99.5% within 60s
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- Token: 256-bit, single-use, 1h TTL
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```
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### pytest-bdd executable spec
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```python
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# features/password_reset.feature → tests/test_password_reset.py
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from pytest_bdd import scenarios, given, when, then
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scenarios("../features/password_reset.feature")
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@given('a user with email "alice@example.com" exists')
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def alice(db):
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db.users.insert(email="alice@example.com")
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@when('I POST /auth/reset with that email')
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def post_reset(client, ctx):
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ctx["resp"] = client.post("/auth/reset", json={"email": "alice@example.com"})
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@then('a reset email is sent within 30 seconds')
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def email_sent(mailbox):
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assert mailbox.last(timeout=30).to == "alice@example.com"
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```
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### NFR table (SLO format)
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```markdown
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| Category | Metric | Target | Measurement |
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|------------|-------------------------|-----------------|-------------|
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| Latency | p95 GET /search | < 200 ms | Prometheus |
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| Throughput | sustained POST /events | 10k req/s | k6 load test|
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| Avail. | API uptime (monthly) | 99.9% | StatusPage |
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| Security | OWASP Top 10 compliance | 100% checklist | annual audit|
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| A11y | WCAG 2.2 AA | 0 critical | axe-core CI |
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```
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### Traceability matrix
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```yaml
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# requirements/trace.yaml
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US-142:
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acceptance_tests: [test_password_reset.py::test_valid_email, test_password_reset.py::test_unknown_email]
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code: [src/auth/reset.py, src/email/templates/reset.html]
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related: [US-130, NFR-SEC-04]
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```
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### AI-assisted PRD draft prompt
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```python
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prompt = f"""You are a senior PM. Draft a PRD for: {feature_idea}
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Output sections:
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1. Problem (user pain quantified)
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2. Goals / non-goals
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3. User stories (INVEST format)
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4. Acceptance criteria (Gherkin)
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5. NFRs with measurable targets
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6. Open questions
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Flag every ambiguous adjective ('fast', 'easy') and demand a number."""
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```
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### Change-impact analysis
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```python
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def impact_of_change(req_id: str) -> dict:
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"""Walk trace matrix to find affected artifacts."""
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trace = yaml.safe_load(open("requirements/trace.yaml"))
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node = trace[req_id]
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return {
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"tests": node["acceptance_tests"],
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"code": node["code"],
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"downstream_reqs": [r for r, n in trace.items() if req_id in n.get("related", [])],
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}
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```
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## 매 결정 기준
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| 상황 | Approach |
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|---|---|
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| Agile feature | User stories + Gherkin AC |
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| Compliance-heavy (med, aero) | Formal SRS (IEEE 830) + traceability |
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| Internal tool | Lightweight one-pager PRD |
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| AI agent task | Structured prompt = spec |
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| Cross-team API | OpenAPI/AsyncAPI as contract |
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| Performance critical | Explicit SLO table |
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**기본값**: User story + Gherkin AC + NFR table; AI-drafted, human-reviewed.
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## 🔗 Graph
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- 변형: [[Use-Cases]]
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- 응용: [[BDD]] · [[TDD]]
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- Adjacent: [[RFC]] · [[OpenAPI]]
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## 🤖 LLM 활용
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**언제**: PRD draft, ambiguity detection, Gherkin generation, impact analysis.
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**언제 X**: regulated safety-critical (FDA, FAA) 매 final sign-off는 매 human SME — LLM의 매 unverified.
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## ❌ 안티패턴
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- **Vague adjectives**: "fast", "scalable" without numbers.
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- **Solution masquerading as requirement**: "Use Redis cache" (impl detail, not need).
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- **Untestable AC**: "should feel intuitive" → 매 measurable proxy 의 부재.
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- **Spec drift**: code changes 의 매 spec update 의 X — 매 living spec과 매 sync.
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- **Big bang requirements**: 매 모든 feature 의 매 upfront freeze → agile 의 violation.
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## 🧪 검증 / 중복
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- Verified (IEEE 830-1998 ⇒ ISO/IEC/IEEE 29148:2018, Cohn user stories, Cucumber BDD).
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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|---|---|
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — full rewrite covering FR/NFR, user stories, Gherkin, AI-aided spec |
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