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Use when designing knowledge bases, RAG, wiki, knowledge graphs, memory for agents, or "how should we store docs for humans vs agents". Produces a clear consumer/form/retrieval decision. Avoids buzzword soup. Do NOT use for ordinary app CRUD databases.
기본적으로 소스를 먼저 확인하는 Prompt가 선택됩니다. 직접 명령으로 전환하거나 로컬 사본을 다운로드할 수도 있습니다.
설치 여부를 결정하기 전에 SKILL.md와 SkillsMP에 표시된 보조 파일을 읽어 보세요.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
직접 명령은 검토 Prompt를 거치지 않습니다. 실행하기 전에 소스를 확인하세요.
npx skills add https://github.com/AVIDS2/avid-skill --skill knowledge-architecture명령은 한 줄로 유지됩니다. 복사하기 전에 가로로 스크롤해 전체 내용을 확인하세요.
로컬 사본을 원하시나요? SkillsMP에서 현재 제공할 수 있는 파일을 다운로드하세요.
SKILL.md 표시 중
| name | knowledge-architecture |
| description | Use when designing knowledge bases, RAG, wiki, knowledge graphs, memory for agents, or "how should we store docs for humans vs agents". Produces a clear consumer/form/retrieval decision. Avoids buzzword soup. Do NOT use for ordinary app CRUD databases. |
Separate three decisions people often mash together: who consumes, what form, how retrieval works.
| Consumer | Optimize for |
|---|---|
| Humans | Readable structure, navigation, editability (Wiki / Markdown tree) |
| Agents | Chunkability, metadata, stable IDs, tool-friendly retrieval |
| Both (default for products) | Single source of truth humans edit + indexes agents query |
Default bias: coexistence — humans write Markdown/Wiki; agents get hybrid retrieval over the same source. Avoid a second shadow knowledge base unless required.
| Form | When |
|---|---|
| Markdown / Wiki | Most products; docs, runbooks, specs |
| Structured DB records | Strong schema entities (tickets, SKUs) |
| Knowledge graph | Relationships are first-class (org charts, law cites, multi-hop entity links) |
| Vector-only corpus | Large unstructured dump with weak structure needs |
MVP default: Markdown wiki (or /docs). Promote to graph when queries are relationship-heavy and hybrid RAG fails.
| Pattern | When |
|---|---|
| Full-text only | Small corpus, precise keywords |
| Vector RAG | Semantic questions over unstructured text |
| Hybrid (keyword + vector) | Default production RAG |
| Agentic RAG | Multi-step: plan → retrieve → critique → retrieve again |
| Graph RAG | Multi-hop entity/relation questions |
MVP default: hybrid retrieval. Use agentic RAG when single-shot retrieval quality is measurably insufficient.
## Consumers: human | agent | both
## Source form: markdown-wiki | db | graph | mixed
## Retrieval: fts | vector | hybrid | agentic | graph-rag
## Ingestion path
## Evaluation plan (5 real questions the system must answer)
## Non-goals
research-before-build, stack-defaultsreferences/knowledge-decision-tree.mdUse when choosing or confirming tech stack for a product or feature: frontend framework, database, auth, email, payments, hosting, search, or agent runtime. Applies AVID defaults (React/Vue, SQLite→Postgres/Supabase, Resend, Stripe, Playwright, Tavily, Context7) and records exceptions. Use during product-bootstrap or any greenfield/stack debate.
Use when starting product, feature, build, stack, research, agent-framework, or implementation work. Loads AVID personal workflow defaults and skill routing. Defaults are advisory and scene-dependent — not absolute hard blocks. Skip or adapt when the deployment target or user intent makes a default a bad fit.
Use when choosing or implementing agent runtimes: LangGraph workflows, Pi / pi-ai / embedded Pi harness agents, thin provider SDK loops, model API wire protocol (Chat Completions vs Responses vs Messages), or "which agent framework" decisions. Scene-based defaults — not a mandate to use Pi or LangGraph. Prefer Chat Completions for multi-model. Prefer docs lookup (Context7/official). Do NOT use for ordinary CRUD with no agent runtime.
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