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-engineering-metrics-dora
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title: Engineering Metrics (DORA)
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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: [DORA, DORA Metrics, Four Keys, DevOps Research and Assessment]
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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: [devops, metrics, dora, sre, 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: yaml
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framework: github-actions
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---
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# Engineering Metrics (DORA)
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## 매 한 줄
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> **"매 deployment frequency, lead time, change fail rate, MTTR — 4 metric 으로 매 engineering org 의 health 측정"**. 매 2014 Google DORA team 의 launch, 매 2021 SPACE framework 보완, 매 2026 GitHub/GitLab/Datadog 의 native dashboard 의 default.
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## 매 핵심
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### 매 Four Keys
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- **Deployment Frequency (DF)**: 매 production deploy 의 빈도. Elite = on-demand (multiple/day).
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- **Lead Time for Changes (LT)**: 매 commit → production. Elite = < 1 day.
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- **Change Failure Rate (CFR)**: 매 deploy 의 incident 유발 비율. Elite = 0–15%.
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- **Mean Time to Recovery (MTTR)**: 매 incident → restore. Elite = < 1 hour.
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### 매 Performance tier
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- **Elite**: DF on-demand · LT < 1day · CFR 0–15% · MTTR < 1h.
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- **High**: DF weekly–daily · LT 1day–1wk · CFR 16–30% · MTTR < 1day.
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- **Medium**: DF monthly · LT 1wk–1mo · CFR 16–30% · MTTR 1day–1wk.
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- **Low**: DF < monthly · LT > 1mo.
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### 매 응용
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1. Sprint retro 매 주 review.
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2. Quarterly engineering OKR target.
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3. Hiring/promo signal (team-level, 매 individual 아님).
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## 💻 패턴
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### GitHub Actions deployment frequency
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```yaml
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# .github/workflows/deploy.yml
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name: deploy
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on:
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push:
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branches: [main]
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jobs:
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deploy:
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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@v4
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- run: ./deploy.sh
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- name: Emit DORA event
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run: |
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curl -X POST https://api.dora-collector.internal/events \
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-H "Authorization: Bearer ${{ secrets.DORA_TOKEN }}" \
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-d '{"type":"deploy","sha":"${{ github.sha }}","ts":"'$(date -u +%FT%TZ)'"}'
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```
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### Lead time calculation (SQL)
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```sql
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-- commits joined with deploys
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SELECT
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PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY EXTRACT(EPOCH FROM (deploy_ts - commit_ts))/3600) AS p50_hours,
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PERCENTILE_CONT(0.95) WITHIN GROUP (ORDER BY EXTRACT(EPOCH FROM (deploy_ts - commit_ts))/3600) AS p95_hours
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FROM dora_events
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WHERE deploy_ts >= NOW() - INTERVAL '30 days';
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```
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### Change failure rate from incidents
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```python
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# rolling 30d CFR
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def cfr(deploys: list[dict], incidents: list[dict]) -> float:
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bad_deploys = {i["deploy_sha"] for i in incidents if i["caused_by_deploy"]}
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return len(bad_deploys) / max(len(deploys), 1)
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```
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### MTTR via PagerDuty
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```python
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import httpx, statistics
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def mttr(api_key: str, since: str) -> float:
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r = httpx.get("https://api.pagerduty.com/incidents",
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headers={"Authorization": f"Token token={api_key}"},
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params={"since": since, "statuses[]": "resolved"})
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durations = [(i["resolved_at_ts"] - i["created_at_ts"]) for i in r.json()["incidents"]]
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return statistics.median(durations) / 60 # minutes
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```
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### Four Keys dashboard (Datadog)
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```yaml
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# datadog-dora.yaml
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widgets:
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- title: Deployment Frequency
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query: "sum:dora.deploy{*}.as_count().rollup(sum, 86400)"
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- title: Lead Time p50
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query: "p50:dora.lead_time_seconds{*}"
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- title: CFR
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query: "sum:dora.deploy_failed{*} / sum:dora.deploy{*}"
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- title: MTTR p50
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query: "p50:dora.incident_resolve_seconds{*}"
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```
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### Trunk-based config (lead time 단축)
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```yaml
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# .github/branch-protection.yml
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required_status_checks:
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strict: true
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contexts: [ci/test, ci/lint]
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required_pull_request_reviews:
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required_approving_review_count: 1
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dismiss_stale_reviews: true
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restrictions: null # 매 직접 push 매 X — PR-only
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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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| Startup (<20 eng) | DF + LT 매 우선, MTTR 매 secondary |
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| Regulated industry | CFR 매 primary (release safety) |
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| Platform team | All 4, 매 weekly review |
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| Individual perf review | 매 X — team metric only |
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**기본값**: 매 four-keys-platform (Google open source) self-host + Grafana.
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## 🔗 Graph
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- 부모: [[DevOps]] · [[Site Reliability Engineering]]
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- 응용: [[Continuous Delivery]] · [[Continuous Integration]]
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## 🤖 LLM 활용
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**언제**: deploy log → metric extraction, incident root-cause 분류 (deploy 유발 여부).
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**언제 X**: 매 individual contributor scoring 매 X — DORA 매 team-level only.
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## ❌ 안티패턴
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- **Goodharting**: DF 만 chase 하고 quality 무시 → CFR 폭증.
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- **Individual scoring**: developer 별 LT 측정 → gaming (small commits 만).
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- **Vanity rollups**: org-wide average — 팀 distribution 의 hide.
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- **No CFR**: deploy 만 count, failure track X → false elite signal.
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## 🧪 검증 / 중복
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- Verified (DORA "State of DevOps" 2014–2024 reports, Google Cloud 공식).
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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 — DORA four-keys 정의 + dashboard pattern |
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