content-pipeline

---

title: Content Pipeline name: content-pipeline description: {{AGENT_NAME}} content pipeline - editorial topic selection, draft writing, Korean humanization, review-ready publishing type: document space: concept tags: [concept] created: 2026-05-22 updated: 2026-06-14

links:

• "[[@identity/persona/writing-style-guide]]"
• "[[@memory/kanban/KANBAN_INDEX]]"
• "[[@memory/growth/INTEGRATION_PROTOCOL]]"
• "[[skills/content-writer]]"
• "[[skills/kanban-worker]]"
• "[[@identity/brain/rules]]"

---

Content Pipeline Skill

Aggregator pattern: 이 skill은 content를 수집하지 않음. monitoring — watch, don't prompt.

• Synthesize in batches. One cycle produces blog draft + X thread + LinkedIn.
• Track narrative arc. Posts accumulate into a story. Season/episode structure keeps continuity.
• Start simple. One agent profile + cron job + one tracking file. No multi-stage pipeline unless proven necessary.

Modes

Mode A: Aggregator (external → blog)

Collect from external sources (trend-harvester, SEO-harvester), select topics, assign to content-writer via kanban. Suitable for: trend posts, SEO-optimized evergreen, tool roundups.

Mode B: CMO Agent (internal work → content)

Single autonomous agent profile (content-manager) observes Drew's recent work and produces multi-format content. Runs daily at 12:00 KST via cron.

Trigger: Cron (0 12 * * *), deliver to Discord #content-channel. Profile: ~/.{{AGENT_NAME_LOWER}}/agents/content-manager.md (deepseek-v4-pro, tools: terminal, file, search, session_search, web) Skill prerequisites: content-pipeline (this skill), SVG knowledge, Excalidraw, Mermaid. Suitable for: build logs, troubleshooting deep-dives, architecture decisions, project retrospectives.

See references/cmo-agent-mode.md for the full implementation guide.

Mode B requires these knowledge base files (in P4-cortex/content/):

• brand-guide.md — brand positioning, voice, audience
• glossary.md — project terms ({{AGENT_NAME}}, M-LOG, PDC...)
• content-inventory.md — published/drafted content for dedup
• narrative_arc.md — episode tracking, season structure, continuity

The agent reads all four at the start of every cycle before gathering context.

---

Editorial North Star

YOUR_DOMAIN은 자동 뉴스 블로그가 아니라 Drew가 AI, 도구, 코드, 글쓰기, 시스템 설계를 통해 어떻게 사고하고 만드는지를 보여주는 개인 작업실이다.

공개 후보가 되는 글은 반드시 아래 등식을 만족해야 한다:

public-worthy content = 기록 + 해석 + 재사용 가능한 통찰

단순한 수집 결과, 출시 소식, 활동 보고, 링크 요약은 공개 draft로 만들지 않는다. 그런 항목은 raw 또는 archive에 남겨도 되지만 content board task로 승격하지 않는다.

Editorial State Machine

raw source
  ↓ editorial_screen
candidate
  ↓ enough Drew-angle + reader value
draft              → status: draft      → Quartz EXCLUDE
  ↓ human review
published          → status: published  → Quartz INCLUDE
  ↓ rework needed
in_review          → status: in_review  → Quartz EXCLUDE
  ↓ no longer useful
archived           → status: archived   → Quartz EXCLUDE

content-pipeline은 candidate → draft까지만 자동화한다. published 전환은 사용자가 직접 검토한 뒤에만 한다.

Triggers

• Mode A (Aggregator): Cron job 3시간마다 (0 */3 * * * KST)
• Mode B (CMO Agent): Cron job daily (0 12 * * *), content-manager agent profile
  • One story per cycle. SILENT if nothing new since last run.
  • Material-driven cadence: backlog clears one post per day, not batched.

See references/cmo-agent-mode.md for the full Mode B implementation.

Approach & Decision-Making

When making technical/content recommendations for Drew (site platform, theme, tools, services):

1. Try first, evaluate later. Propose a concrete implementation path, apply it to the current environment, and test. Do NOT decide based on imagination, brand reputation, or hypotheticals alone. Retreat only if the tested approach proves genuinely inefficient.

2. Research, don't pretend. If you don't have first-hand experience with a tool or technology, say "let me search for that" — not "I think X is good." The user will call out fake expertise immediately. When asked for a recommendation:

   • Search the web for current options
   • Test the top candidate in the user's environment
   • Report what you found and what worked
   • Don't generate comparison tables from hearsay or brand recall

3. Don't fear change. Changing platforms (Quartz → WordPress), tools, or approaches is not inherently bad. Evaluate the migration cost against the benefit honestly, and if the direction makes sense, execute it rather than listing objections.

4. When asked for a recommendation, search first. Before saying "I recommend X," search for current options, pick 2-3 concrete candidates, install/test the most promising one, and report results. Let real testing guide the decision, not your training data.

5. Don't list objections for a direction the user is already committed to. If the user says "let's use WordPress" or "let's run Huly on NAS," don't produce a comparison table of alternatives. Instead:

   • Say "let's check if it works" and test it
   • Surface blocking issues only after the test fails, not before
   • Default to "yes, let's try that" over "here are the trade-offs"

These rules apply any time the user asks "which X should I use" or "what do you recommend."

Patching & Conventions

When updating or extending skills, follow these conventions:

Reference File Updates

• references/ files preserve session-specific detail. When a one-time setup or workflow is discovered, add a reference file rather than bloating SKILL.md. Each reference file starts with a one-line summary of what it covers.
• Scripts live in scripts/. Anything the agent should run rather than hand-type. Scripts are documented in SKILL.md with their usage example.
• Templates live in templates/. Boilerplate configs, scaffolding, starter files meant to be copied and modified.

When to Add vs. Update

• Add a reference file when a new technique, tool, or workflow was discovered during the session (e.g., fixing NAS SSH, setting up WordPress MCP).
• Update SKILL.md when the core workflow changes (e.g., a new Mode, a step order change, a new image type).
• Update memory when a USER preference or environment fact is learned (e.g., "user prefers trying first over analysis").
• Don't save if nothing was learned that a future session would benefit from.

---

Phase 1: Source Tally (Aggregator Mode)

직접 수집하지 않음. 기존 cron output 3개를 읽음.

1. Trend Harvester → analyzed/keep

위치: ~/.{{AGENT_NAME_LOWER}}/P4-cortex/growth/trend-harvester/analyzed/keep/
형식: JSON (item{name,description,url,source}, total_score, decision)
필터: decision == "keep", scored_at 최근 48시간
선별: 상위 5개 → topic 후보

# 실행
ls -t ~/.{{AGENT_NAME_LOWER}}/P4-cortex/growth/trend-harvester/analyzed/keep/*.json | head -10
# → 상위 10개 JSON 파일 경로

JSON 파싱:

import json, pathlib
keep_dir = pathlib.Path("~/.{{AGENT_NAME_LOWER}}/P4-cortex/growth/trend-harvester/analyzed/keep")
files = sorted(keep_dir.glob("*.json"), key=lambda p: p.stat().st_mtime, reverse=True)[:10]
for f in files:
    d = json.load(open(f))
    item = d["item"]
    print(f"- {item['name']}: score={d['total_score']:.1f}, source={item['source']}")

2. SEO Harvester → report.json

위치: ~/.{{AGENT_NAME_LOWER}}/P2-hippocampus/knowledge/seo-articles/report.json
형식: JSON (articles[{title,url,keyword,score}])
선별: score ≥ 0.7, keyword 명확한 것 상위 3개

cat ~/.{{AGENT_NAME_LOWER}}/P2-hippocampus/knowledge/seo-articles/report.json | python3 -c "
import json, sys
d = json.load(sys.stdin)
articles = d.get('articles', [])
scored = [(a, a.get('score', 0)) for a in articles if a.get('score', 0) >= 0.7]
for a, s in sorted(scored, key=lambda x: -x[1])[:5]:
    print(f'- {a[\"title\"]} | keyword={a.get(\"keyword\",\"?\")} | score={s:.2f}')
"

3. Activity Logger → kanban tasks (last 24h)

위치: {{AGENT_NAME_LOWER}}_tasks.db (board=default, trigger_source=activity_logger)
쿼리: SELECT title, body FROM tasks
      WHERE trigger_source = 'activity_logger'
      AND status = 'completed'
      AND created_at > datetime('now', '-24 hours')
선별: conversation insight 관련 것 (implement/build/create 등 포함) 상위 3개

import sqlite3, pathlib
db = pathlib.Path.home() / ".{{AGENT_NAME_LOWER}}/P2-hippocampus/kanban/state/{{AGENT_NAME_LOWER}}_tasks.db"
conn = sqlite3.connect(str(db))
rows = conn.execute("""
    SELECT title, body FROM tasks
    WHERE trigger_source = 'activity_logger'
    AND status = 'completed'
    AND created_at > datetime('now', '-24 hours')
    ORDER BY created_at DESC
    LIMIT 10
""").fetchall()
for title, body in rows:
    print(f"- [{title}] {body[:100] if body else ''}")
conn.close()

---

Phase 2: Topic Selection

세 source에서 합산 최대 3개 topic 선별. 기본값은 0개다. 충분히 좋은 후보가 없으면 [SILENT]가 올바른 결과다.

출처	Topic 유형	Prefix
Trend Harvester	[draft-trend]	기술 동향
SEO Harvester	[draft-seo]	검색 최적화
Activity Logger	[draft-conversation]	작업 insight

Editorial Gate

각 후보는 0~2점으로 평가한다. 총점 7점 이상만 content board task로 만든다.

기준	0점	1점	2점
Drew-angle	Drew의 작업/판단과 무관	약하게 연결됨	Drew의 작업 방식, 도구, 포트폴리오와 직접 연결
Insight	요약/소식 수준	관찰은 있으나 일반화 약함	독자가 재사용할 수 있는 판단 기준이 있음
Evidence	출처/맥락 불충분	기본 출처 있음	출처 + 실제 작업 맥락 또는 artifact 있음
Portfolio value	지나가는 메모	blog note로는 가능	portfolio/insight 축에 남겨도 가치 있음
Specificity	일반론, 키워드 나열	어느 정도 구체적	구체적 사례, 프로젝트, 도구, 실패/결정 포함

자동 reject 규칙:

• 단순 제품/도구 출시 요약
• Drew가 직접 써본 흔적이나 판단이 없는 외부 트렌드
• SEO keyword만 좋고 관점이 없는 주제
• 이미 최근 30일 내 같은 주장으로 다룬 주제
• "AI가 대단하다/위험하다" 수준의 일반론

source별 우선순위:

1. [draft-conversation]: Drew의 작업에서 나온 insight면 우선.
2. [draft-trend]: {{AGENT_NAME}}, YOUR_DOMAIN, agent tooling, creative coding, publishing system과 연결될 때만.
3. [draft-seo]: 검색 유입보다 사이트 정체성에 맞는 evergreen 주제일 때만.

최대 3개. 품질 > 수량.

---

Phase 3: Kanban Task Creation

선별한 topic마다 content board에 task 생성:

kanban_create(
    title=f"[draft-{type}] {topic_title}",
    body=f"""## Topic
{topic_description}

## Editorial Decision
- score: {score}/10
- publish_intent: blog | insight | portfolio | archive
- Drew-angle: {why_this_matters_to_drew}
- reusable_insight: {one_sentence_reader_value}
- reject_if_missing: if this cannot show Drew's judgment, archive instead of drafting

## Content Source
- source: {source_name}
- collected_at: {YYYY-MM-DD HH:MM}

## 글쓰기 방향
- 톤: writing-style-guide.md hybrid approach 따르기
  - 톤: 휴머너드 말투 (긴 플로우, Bold 강조, "당신" 직접호칭, 1인칭 "저"/"나")
  - SEO: 기존 방식 유지 (aliases, 해시태그, SEO 키워드 섹션)
- 제목: 질문 또는 provocative statement
- 도입: Bold 훅 한 문장 → 10~20문장 플로우

## Frontmatter

title: {title} type: document space: concept tags: [blog, {category}] aliases: ['/blog/{slug}'] created: {YYYY-MM-DD} status: draft links:

• "[[@identity/persona/writing-style-guide]]"

## 작성 시 반드시 확인할 것
1. forbidden.patterns grep → 0건
2. Bold 섹션 강조 2~4개 존재
3. "당신" 직접호칭 + 1인칭 "저"/"나" 포함
4. 본문 날짜 (X월 X일) 없음
5. SEO 키워드 3~7개, 해시태그 8~14개
6. aliases in frontmatter
7. 기록 + 해석 + 재사용 가능한 통찰이 모두 있음
""",
    board="content",
    trigger_source="content_pipeline",
    priority=1,
    idempotency_key=f"{YYYY-MM-DD}-{slug}",
)

Title prefix 규칙:

• 트렌드 기반: [draft-trend]
• SEO 키워드 기반: [draft-seo]
• 대화/경험 기반: [draft-conversation]

Draft 파일 위치: memories/insights/YYYY-MM-{slug}.md

• 예: memories/insights/2026-05-gemini-cli-shutdown.md
• Obsidian에서 직접 확인 가능: ~/.{{AGENT_NAME_LOWER}}/P2-hippocampus/memories/insights/
• Kanban 대시보드 연동: body에 ## Draft 파일 위치 절대경로가 있으면 대시보드 카드에 📄 Obsidian 링크가 자동 생성됨. 카드 클릭 → Description 탭 하단 "📄 Open in Obsidian" 버튼 클릭 → Obsidian에서 draft 파일 열림.
• YOUR_DOMAIN의 /blog/{slug} 또는 /blog/YYYY/{slug} 경로로 Quartz에 의해 공개됨. Raw monthly log는 공개하지 않음.

---

Phase 4: Draft Writing ({{AGENT_NAME}} Worker)

kanban-dispatcher가 ready task를 worker에 배분.

4-1. Task Claim

kanban_claim(task_id, ttl_seconds=1800)

4-2. Research (if needed)

Topic 관련 정보가 충분하지 않으면 web search로 보강. 출처: URL, source-date 명시.

4-3. Draft 작성

memories/insights/에 Markdown 파일 생성.

writing-style-guide.md 적용 (hybrid approach):

• 톤: 휴머너드 말투 (긴 플로우, Bold 강조, "당신" 직접호칭, 1인칭 "저"/"나")
• SEO: 기존 방식 유지 (aliases, 해시태그, SEO 키워드 섹션, frontmatter)

** ReefWatch 스타일 글 구조 (기술 심화 글용):**

ReefWatch 아티클 (dev.to/siiddhantt/building-reefwatch)의 7가지 핵심 포인트를 적용한 템플릿:

1. 문제 프레이밍 — 공감되는 상황부터 시작. Bad: "이 글은 ~에 대해 알아봅니다." Good: 구체적 경험/상황 → 문제 발견
2. 강한 주장 (Bold) — 기존 방식의 한계를 Bold로. 예: "But that is not triage. That is a polished to-do list."
3. 디자인 제약 (emphasis) — 핵심 원칙을 emphasis로 강조. 예: "The design constraint from the start was simple: no evidence, no answer."
4. One-sentence 요약 (blockquote) — 한 문장으로 전체 정의. 블록쿼트로 시각적으로突出
5. 이미지 배치 — "설명 전에 보여주기" — 개념 설명 직전에 시각 자료. Obsidian: ![[image.png|너비]]
6. "What This Guide Builds" 테이블 — 글 도입부에 outcomes를 테이블로 제시. 독자가 첫 스캔에서 판단
7. Build Path / 구조 테이블 — 순서를 시각화. 다단계 컴포넌트 설명에 효과적

** ReefWatch 확장 템플릿:**

# {제목: 질문 또는 provocative statement}

{커버 이미지: ![]() — 16:9 landscape, 글 전체를 대표하는 시각적}

**{문제 프레이밍 — Bold 훅}**
{구체적 상황 묘사. 독자가 공감할 수 있는 경험/관찰}

**{강한 주장 — Bold 한 문장}**
{기존 방식의 한계 또는 문제의 본질}

{분석 플로우: 10~20문장. 경험 → 관찰 → 분석}

**{중간 핵심 강조 — Bold}**
{구체적 해결책 또는 인사이트}

{이어지는 플로우}

> {One-sentence 정의 — 블록쿼트로 한 문장 요약}

## What This Guide Builds

| 당신은 이것을 할 수 있게 된다 |
|---|
| {outcome 1} |
| {outcome 2} |
| {outcome 3} |

{구현/설계 섹션}

{이미지: ![]() — 플로우/아키텍처 다이어그램, 설명 전에 배치}

{이어지는 설명}

**{마무리 강조 — Bold 한 문장}**
{결론 또는 행동 유도}

---

**이런 분들께 추천**
- {타겟 독자}

**SEO 키워드**: {키워드 3~7개}
**#해시태그**: #{태그1} #{태그2} ...

** ReefWatch 스타일 글쓰기 순서:**

1. 문제 상황 묘사 (Opening) — Bold 훅
2. 기존 방식의 한계 (Bold 주장)
3. 해결 원칙 (emphasis)
4. 구체적 구현/해결책
5. 구조/순서 정리 (표 또는 트리)
6. 마무리 행동 유도

기술 심화 글이 아닌 경우 (트렌드/단편): 확장 템플릿 대신 기본 구조(문제 프레이밍 → Bold 주장 → Bold 마무리)만 사용. "What This Guide Builds" 테이블은 생략 가능.

---

Image Types in Blog Posts

Blog posts use THREE types of visual content, each with a different production path:

Type	Production	Cost	When
Mermaid (inline)	```mermaid code blocks — Quartz renders to SVG	$0	Flows, sequences, state machines
Excalidraw PNG (exported)	.excalidraw.json → excalidraw-to-png.js → .png	$0	Architecture diagrams, before/after, data flow
SVG Cover (inline)	Model writes SVG XML → saved as .svg	$0	Hero/banner, article cover image

SVG is the primary cover/hero image format — model writes SVG XML directly. Mermaid is for inline flow diagrams. Excalidraw is for complex architecture visuals.

SVG Meme Templates (Mode B only)

For stories with a natural humor angle, create a meme SVG alongside the cover. Memes make technical content more approachable on social/X.

Supported templates:

Template	Use Case	Structure
Drake Reject/Approve	Before/after comparison	Two panels: red ✗ (old) → green ✓ (new)
"This is fine"	Recognizable pain/bug	Burning room, "it's fine" caption
Galaxy Brain	Escalating understanding	4 levels of insight, last one mind-blowing
Distracted Boyfriend	Three-way comparison	3 labeled elements: old → new → shiny

Save as YYYY-MM-DD-slug-meme.svg. Embed optionally: ![[slug-meme.svg|600]]

Pitfall: Don't force a meme where none fits. If the story is serious (incident, security, reflection), skip it. Memes are for "this is ridiculous" or "this pattern is obvious in hindsight" angles only.

⚠️ Pitfall: Do not conflate Mermaid with exported images. Mermaid renders inline as SVG. Excalidraw PNGs are separate files. The blog post needs BOTH — Mermaid for inline flows, Excalidraw PNG for architecture visuals.

Excalidraw → PNG Pipeline

The content-manager creates .excalidraw.json files for complex architecture. The full pipeline is:

Step 1: Create JSON → Validate

Create .excalidraw.json with proper JSON structure. Must validate before proceeding:

python3 -c "import json; json.load(open('diagram.excalidraw.json'))" && echo "VALID" || echo "INVALID"

Common pitfall: trailing comma in the last element of an array or object. JSON does not allow trailing commas. Use write_file which includes a JSON lint step, or run the validation above.

Step 2: Create .excalidraw binary

excalidraw create diagram.excalidraw.json -o diagram.excalidraw
# Requires: npm install -g excalidraw-cli (Homebrew)

This step converts the JSON into a binary .excalidraw file. This is a required intermediate step — the PNG script below needs it.

Step 3: Export to URL

excalidraw export diagram.excalidraw
# Returns: URL: https://excalidraw.com/#json=<id>,<key>

Uploads to excalidraw.com, returns a shareable URL. Requires internet access.

Step 4: Screenshot to PNG

node ~/.{{AGENT_NAME_LOWER}}/scripts/excalidraw-to-png.js \
  /path/to/diagram.excalidraw.json \
  /path/to/diagram.png

This runs Puppeteer (headless Chrome) to open the URL in ?embed=1 mode and screenshot the canvas.

Setup:

npm install -g excalidraw-cli              # CLI for upload/export (Step 2-3)
cd ~/.{{AGENT_NAME_LOWER}}/scripts           # local install for puppeteer (Step 4)
npm install puppeteer

Runtime requirements: Node.js, internet access to excalidraw.com, puppeteer in ~/.{{AGENT_NAME_LOWER}}/scripts/node_modules/.

Shortcut — the excalidraw-to-png.js script combines Steps 3+4 (export + screenshot). You still need Step 2 (excalidraw create) separately.

Embed in blog post:

![[diagram.png|700]]
*{caption}*

SVG Cover Image (Generated Inline, $0)

The content-manager generates an SVG cover image for each blog post. SVG is XML text — the model writes it directly. No API calls, no extra cost.

Template (1200×630, YOUR_DOMAIN dark theme):

<svg xmlns="http://www.w3.org/2000/svg" width="1200" height="630">
  <defs>
    <linearGradient id="bg" x1="0%" y1="0%" x2="100%" y2="100%">
      <stop offset="0%" style="stop-color:#0f0f1a"/>
      <stop offset="100%" style="stop-color:#1a1a2e"/>
    </linearGradient>
  </defs>
  <rect width="1200" height="630" fill="url(#bg)"/>
  <!-- title, subtitle, illustration, tags, date -->
</svg>

Design rules:

• Background: #0f0f1a → #1a1a2e gradient
• Accent: #7b5f3d (amber — brand color)
• Secondary: #4a90d9 (blue) or #50c878 (teal)
• Text: #e8e4df (warm white), #8a8680 (muted)
• Subtle grid: #ffffff at 3% opacity
• Include: title, subtitle, simple architecture illustration, tags, date
• Embed as: ![[YYYY-MM-DD-slug-cover.svg|800]]

The SVG replaces the old <!-- COVER: ... --> HTML comment pattern (deprecated). No paid API needed — the model writes SVG markup directly.

Pitfall: Do NOT fall back to paid image APIs (FAL, OpenAI, Gemini Image). The user explicitly rejected these. SVG generation is the designated zero-cost approach. If the SVG is too complex, simplify it — don't suggest payment.

X Thread Production

Every blog post gets a companion X thread. The thread is a self-contained narrative — it should be understandable without reading the blog post, while linking back to it.

Thread Structure (12 tweets — sweet spot for читатель retention)

1/HOOK — Bold claim or surprising question. Make the reader pause.
        Example: "\"Is this even working?\" This question found 3 silently broken
        bugs. Here's what happened."
2-3/  — Problem setup. What was supposed to work, what was actually happening.
         One tweet per bug or per layer of the problem.
4-7/  — The discovery process. How you found it, what the root cause was.
         Concrete details matter: error codes, line counts, hours stranded.
8-9/  — The pattern. What these bugs share. The lesson that generalizes beyond
         your specific setup.
10-11/— The fix. What changed. Keep it short — the detail is in the blog post.
12-13/— Broader implication + CTA. "This is what taste in engineering looks like."
         Link to the blog post.

Tweet Content Rules

• Each tweet is self-contained. Don't assume the reader saw the previous tweet (but do assume Twitter threading renders them in order).
• Korean only (following brand guide). English terms allowed for proper nouns (PYTHONPATH, GraphQL, etc.)
• Use emoji sparingly — 🧵 at the end of tweet 1, that's it. Bug emoji (🐛) OK for bug posts.
• One tweet = one idea. Don't cram 3 bugs into one tweet.
• Link in last tweet only (or penultimate). Earlier tweets that link cannibalize engagement.
• No hashtags in tweet body (they look spammy). Hashtags in last tweet only if at all.

Thread Ending

Last tweet: Season/Episode tag (e.g., "Season 1: Taste Engineering — Episode 3") + link.

X Thread File Convention

Save as YYYY-MM-DD-slug-thread.txt in memories/insights/. Plain text, one tweet per paragraph separated by blank lines. The numbered "N/15" prefix is added by the human on posting.

Hooked question

2/ Bug description
3/ Pattern discovery
...
Last/ Link + season tag

Pitfall: Don't write the "N/15" counting in the file — the human decides the final tweet count. Write the content; the /N comes from the posting interface.

Cover images are <!-- COVER: description --> HTML comments — no image generation. The content-manager describes the ideal image; a human or future tool generates it.

This pattern is deprecated in favor of SVG cover generation above. Keep only if SVG generation fails (unlikely — SVG is just XML text).

이미지 배치 원칙 — "설명 전에 보여주기":

1. 섹션 내에서 핵심 개념을 먼저 설명하면 독자가 그림을 보면서 내용을 자연스럽게 이해함
2. 다이어그램 → 설명 순서: "![[flow-diagram.png]]" → "위 그림은 X의 구조를 보여준다"
3. 스크린샷 → 설명 순서: "![[workspace-screenshot.png]]" → "실제 워크스페이스는 이렇다"
4. 비교 이미지 → 설명 순서: "![[before-after.png]]" → "바꾸기 전/후 차이다"

기술 심화 글의 이미지 전략:

• 1개 필수 — 커버 이미지 또는 주요 다이어그램 (글 도입부 근처)
• 1~2개 선택 — 아키텍처 플로우, 비교 시각화, UI 모의 등
• 너비 조절 — Obsidian embed의 |300 |400 |600으로 본문 폭에 맞춤
• 너무 많지 않기 — 3개 이상이면 집중력 분산, 1~2개가 이상적

예시 — 트렌드 글:

# AI 에이전트, 증거 없이 답하지 마라

**문제 프레이밍**
당신이 AI 에이전트를 구축한다고 치자. 모니터링은 PagerDuty에서 하고, 에러는 Sentry에 있고, 배포는 GitHub Actions에서 됐다. 문제는 여러 곳에 흩어져 있는데, 일반 챗봇은 "Sentry에서 에러를 확인해보세요"라고만 말할 수 있다.

**강한 주장**
하지만 그것은 triage가 아니다. 그건 정돈된 할 일 목록일 뿐이다.

**디자인 제약**
원칙은 단순했다: 증거 없이는 답하지 않는다.

**해결책**
이 원칙을 지키기 위해 Coral을 데이터 플레인으로 사용하면...

4-4. Language Polish — DeepSeek 한글 윤문 (Aggregator Mode Only)

AI 티 제거 + 한글 교정을 DeepSeek로 윤문. CMO Agent mode에서는 skip (content-manager가 직접 작성).

윤문 철학 — 역할 확인, 삭제 규칙이 아니다.

• "이 표현을 지워야 하나?" → "이 표현이 여기서 무슨 일을 하나?"로 바꿔서 판단
• -적, 의, 들, 것, 있는/있다는, 있었다 등은 패턴 매칭으로 무조건 삭제하지 않고, 의미·리듬·강조·장르에 필요한 역할을 하는지 확인
• 개념어·전문용어·대조·소유·출처·실제 복수·고정 표현이면 유지

변경률 가드:

• 목표: 5~30%. 30% 초과 시 과윤문 가능성 재검토
• 50% 초과 시 강제 중단, 원문 유지, summary에 "변경률 50%+ — 사람 검토 권고" 표기

Vault에서 API 키 조회:

import sys, os
sys.path.insert(0, os.path.expanduser("~/.{{AGENT_NAME_LOWER}}"))
from modules.secrets_vault import SecretsVault
vault = SecretsVault()
api_key = vault.resolve("vault_9fa1b5bb")  # DEEPSEEK_API_KEY

실행:

DEEPSEEK_API_KEY="<vault에서 조회한 키>" \
python3 ~/.{{AGENT_NAME_LOWER}}/P4-cortex/scripts/humanize_korean.py \
    ~/.{{AGENT_NAME_LOWER}}/P2-hippocampus/memories/insights/{YYYY-MM}-{slug}.md \
    ~/.{{AGENT_NAME_LOWER}}/P2-hippocampus/memories/insights/{YYYY-MM}-{slug}_polished.md

스크립트: ~/.{{AGENT_NAME_LOWER}}/P4-cortex/scripts/humanize_korean.py

• DeepSeek API (deepseek-chat) 호출
• AI 패턴 제거: ~에 대해, ~라고 생각한다, 입니다/습니다 ending 과잉, "//" 스타일
• Markdown formatting 보존
• 원본 손상 없음 (별도 파일로 출력)
• temperature=0.3 (자연스러운 variation, 사실 왜곡 방지)

실행 후:

1. _polished.md 파일이 생성됨 → 원본과 교체 (rename)
2. 이후 forbidden.patterns grep 실행

Vault ref:

서비스	ref	확인
DeepSeek API	vault_9fa1b5bb	vault.list("api_key")로 확인

Vault에 키 등록 (최초 1회):

from modules.secrets_vault import vault
vault.register("DEEPSEEK_API_KEY", os.getenv("DEEPSEEK_API_KEY", "sk-your-key-here"), category="api_key")

4-5. Language Polish 체크리스트 (DeepSeek 윤문 후)

DeepSeek 윤문을 사용할 경우 이 단계는 skip — DeepSeek가 처리함.

# 1. 불필요한 한문/한자/일어 → 한글 변환
# 한자: "倫理" → "윤리", "機能" → "기능", "存在" → "존재"
# 한문: "不可以" → "불가", "，要注意" → "주요"
# 일어: "ceras" → 없음 (외래어として以外), "ニーズ" → "니즈"

# 2. 비문检查 — 읽기 어려운 문장 reformulate
# 3. 오탈자 자동 수정
# 4. writing-style-guide forbidden.patterns 재확인

Polish 체크리스트 (DeepSeek 윤문 후):

[ ] humanize_korean.py 실행 완료
[ ] _polished.md → 원본 파일로 rename
[ ] forbidden.patterns grep → 0건
[ ] 변경률 5~30% (30%+ = 과윤문 검토, 50%+ = 중단)
[ ] Sunny 역할 확인: 표현이 무슨 일을 하는가? (의미·리듬·강조·정밀도·장르)

원문 예시 (수정 전):

이 功能은 您的 жизнь에 매우 重要합니다不可以

수정 후 (DeepSeek):

이 기능은 당신의 삶에 매우 중요합니다

---

실패 시: 원본 draft 파일 그대로 유지 (작업 실패가 아님). Phase 5로 진행.

4-6. Kanban Complete (Aggregator Mode Only)

draft_file = f"~/.{{AGENT_NAME_LOWER}}/P2-hippocampus/memories/insights/{YYYY-MM}-{slug}.md"
kanban_complete(
    task_id,
    result=f"Draft file: {draft_file}",
    summary=f"Draft created: {title}",
    metadata={"draft_path": f"P2-hippocampus/memories/insights/{YYYY-MM}-{slug}.md"}
)

result에는 반드시 절대경로 포함 — Phase 5 Periodic Delivery의 SQLite regex가 ~ 또는 ~/로 시작하는 경로를 추출함. Phase 5 regex: r'memories/insights/(20\d{2}-\d{2}-[^.]+\.md)' 또는 r'~/.{{AGENT_NAME_LOWER}}/P2-hippocampus/memories/insights/(20\d{2}-\d{2}-[^.]+\.md)'

---

4-7. Post-production: narrative_arc + content-inventory Sync

After every blog post + X thread, update BOTH tracking files before completing the cycle.

narrative_arc.md (P4-cortex/content/narrative_arc.md) — Two updates:

1. Episodes table: add new row
2. Decision log: add entry explaining why

content-inventory.md (P4-cortex/content/content-inventory.md) — Three updates:

1. Published table: add post row
2. Drafts table: add all supporting files
3. Topics Covered: add 1-2 dedup bullets

Pitfall: Do not list the same draft file in both Published and Drafts tables.

---

Phase 5: User Review

Review Steps (YOUR_USERNAME)

1. Obsidian에서 memories/insights/ 확인
2. draft 파일 열기
3. Editorial Gate 재확인:
   • Drew-angle이 실제로 드러나는가?
   • 독자가 가져갈 판단 기준이 있는가?
   • 단순 트렌드 요약이 아니라 해석이 있는가?
   • 6개월 뒤에도 portfolio/insight 기록으로 의미가 있는가?
4. 오탈자·내용 수정
5. frontmatter 확인:
   • 통과: status: draft → status: published
   • 보류: status: draft → status: in_review
   • 폐기: status: draft → status: archived
   • publish_date: {YYYY-MM-DD} 추가
6. Quartz rebuild (자동 또는 수동)

---

Quality Gate (writer용)

Draft 작성 완료 시 확인. 모든 패턴 매칭은 "삭제 규칙이 아니라 역할 확인" — 표현이 여기서 무슨 일을 하는지 먼저 판단하고 수정할 것.

1. Language Polish (4-4 단계) 먼저 수행 — 한자/한문/일어→한글 변환, 비문 수정
2. forbidden.patterns grep → 0건
3. Bold 섹션 강조 2~4개 존재
4. "당신" 직접호칭 + 1인칭 "저"/"나" 포함
5. 본문 날짜 없음 (X월 X일 금지)
6. 출처 없는 주장 없음 (모르면 "확인 필요" 표기)
7. SEO 키워드 37개, 해시태그 814개
8. aliases in frontmatter (/blog/{slug})
9. Editorial Gate: Drew-angle + reusable insight + specificity 통과

Polish 체크리스트 출력 예시:

Language Polish: DONE
  한자→한글: 3건 변환
  한문 표현: 1건 변환
  일어 외래어: 0건
  비문 수정: 2건
Forbidden: 0건
Bold sections: 3
Direct address ("당신"): YES
1인칭 ("저"/"나"): YES
Dates in body: 0
SEO keywords: 5
해시태그: 11
aliases: /blog/gemini-cli-shutdown
PASS

## Content Pipeline — YYYY-MM-DD HH:MM KST

Topics selected: N

| # | Source | Topic | Task ID | Draft File |
|---|--------|-------|---------|------------|
| 1 | trend | {title} | {id} | ~/.{{AGENT_NAME_LOWER}}/P2-hippocampus/memories/insights/{YYYY-MM}-{slug}.md |
| 2 | seo | {title} | {id} | ~/.{{AGENT_NAME_LOWER}}/P2-hippocampus/memories/insights/{YYYY-MM}-{slug}.md |
| 3 | conversation | {title} | {id} | — (in progress) |

Worker 배분: kanban-dispatcher-content가 5분마다 ready task를 worker에 배분
Review at: Obsidian → P2-hippocampus → memories → insights

Phase 3 완료 Delivery (Phase 3 후 즉시 출력)

## Content Pipeline — YYYY-MM-DD HH:MM KST

Topics selected: N (task created)

| # | Source | Topic | Task ID | Draft File |
|---|--------|-------|---------|------------|
| 1 | trend | {title} | {id} | ~/.{{AGENT_NAME_LOWER}}/P2-hippocampus/memories/insights/{YYYY-MM}-{slug}.md |
| 2 | seo | {title} | {id} | ~/.{{AGENT_NAME_LOWER}}/P2-hippocampus/memories/insights/{YYYY-MM}-{slug}.md |
| 3 | conversation | {title} | {id} | — (in progress) |

Worker 배분: kanban-dispatcher-content가 5분마다 ready task를 worker에 배분
Review at: Obsidian → P2-hippocampus → memories → insights

Topics selected=0이면 [SILENT].

---

Phase 3 Periodic Delivery (매 cron tick마다, 완료된 task 확인)

content-pipeline cron job이 매 실행마다 content board의 completed task를 조회해 draft 파일 위치를 보고:

import sqlite3, os, re, pathlib, glob

DB = os.path.expanduser("~/.{{AGENT_NAME_LOWER}}/P2-hippocampus/kanban/state/{{AGENT_NAME_LOWER}}_tasks.db")
INSIGHTS = os.path.expanduser("~/.{{AGENT_NAME_LOWER}}/P2-hippocampus/memories/insights/")
conn = sqlite3.connect(DB)
rows = conn.execute("""
    SELECT id, title, body, completed_at
    FROM tasks
    WHERE trigger_source = 'content_pipeline'
      AND board = 'content'
      AND status = 'completed'
      AND completed_at > datetime('now', '-72 hours')
    ORDER BY completed_at DESC
""").fetchall()
conn.close()

drafts = []
for task_id, title, body, completed_at in rows:
    draft_path = "— (path not recorded)"
    if body:
        # body: "Draft file: ~/.{{AGENT_NAME_LOWER}}/P2-hippocampus/memories/insights/2026-05-{slug}.md"
        m = re.search(r'P2-hippocampus/memories/insights/(\d{4}-\d{2}-[^/]+\.md)', body)
        if m:
            draft_path = os.path.join(INSIGHTS, m.group(1))
            if not os.path.exists(draft_path):
                date_prefix = m.group(1)[:7]
                matches = glob.glob(os.path.join(INSIGHTS, f"{date_prefix}-*.md"))
                draft_path = max(matches, key=os.path.getmtime) if matches else f"~/.{{AGENT_NAME_LOWER}}/P2-hippocampus/memories/insights/{m.group(1)}"
    drafts.append((title, task_id, draft_path))

완료된 task가 있으면这份 delivery 출력:

## Content Pipeline Update — YYYY-MM-DD HH:MM KST

Draft files ready for review:

| # | Topic | Task ID | Draft File |
|---|-------|---------|------------|
| 1 | {title} | {id} | ~/.{{AGENT_NAME_LOWER}}/P2-hippocampus/memories/insights/{filename}.md |
| 2 | {title} | {id} | ~/.{{AGENT_NAME_LOWER}}/P2-hippocampus/memories/insights/{filename}.md |

Review at: Obsidian → P2-hippocampus → memories → insights

완료된 task가 없으면 (completed_at이 72시간 내 없으면) [SILENT].

## Content Pipeline — YYYY-MM-DD HH:MM KST

Topics selected: N

| # | Source | Topic | Task ID | Draft File |
|---|--------|-------|---------|------------|
| 1 | trend | {title} | {task_id} | ~/.{{AGENT_NAME_LOWER}}/P2-hippocampus/memories/insights/{filename}.md |
| 2 | seo | {title} | {task_id} | ~/.{{AGENT_NAME_LOWER}}/P2-hippocampus/memories/insights/{filename}.md |
| 3 | conversation | {title} | {task_id} | — (in progress) |

Review at: Obsidian → P2-hippocampus → memories → insights

Draft File 列은 task body에서 P2-hippocampus/memories/insights/ 경로를 추출 — 있으면 절대경로로 표시, 없으면 "— (in progress)" 표시.

---

Phase 6: Publishing (Quartz Auto-Deploy — No Git)

Publishing Flow

Obsidian에서 status: published로 바꾸면 → fswatch가 vault 변경 감지 → quartz build → wrangler pages deploy → YOUR_DOMAIN 게시 (약 10초 내외)

YOUR_USERNAME (Obsidian에서 draft 편집)
  → frontmatter: status: published, publish_date: YYYY-MM-DD
  → fswatch가 vault 파일 변경 감지 (LaunchAgent 자동 실행)
    → 5초 debounce 후 quartz build
      → wrangler pages deploy public/ --project-name=YOUR_SITE
        → Cloudflare Pages에 directory deploy (git 불필요)
          → YOUR_DOMAIN 게시

wrangler pages deploy는 git 없이 directory를 직접 Cloudflare Pages에 올립니다. git repo 아니어도 동작, git commit 불필요.

Infrastructure

Component	Details
fswatch LaunchAgent	com.{{AGENT_NAME_LOWER}}.quartz-fswatch (PID 5247 ✅)
fswatch script	~/.{{AGENT_NAME_LOWER}}/P4-cortex/scripts/quartz-fswatch.sh
Watched dirs	memories/insights, P4-cortex/growth, P4-cortex/knowledge, YOUR_SITE/content
Debounce	5초 (변경 후 5초内有 추가 변경이면 다시 5초 대기)
Build	cd YOUR_SITE && npx quartz build --concurrency=4
Deploy	wrangler pages deploy public/ --project-name=YOUR_SITE (git 불필요)
Deploy LaunchAgent	com.{{AGENT_NAME_LOWER}}.quartz-deploy (runs on-demand via wrapper script)
CF Account ID	dc0199b6b6c27bc9bb2f3201d47cb643
CF Project	YOUR_SITE
Site URL	https://YOUR_DOMAIN

LaunchAgent States

fswatch:
  5247  running  com.{{AGENT_NAME_LOWER}}.quartz-fswatch   ← vault 변경 감지
  63582 running  com.{{AGENT_NAME_LOWER}}.quartz-deploy    ← (KeepAlive, 필요시 실행)

YOUR_DOMAIN 실시간 게시 상태:
  vault 파일 변경 → fswatch 감지 → 5초 debounce → quartz build → wrangler deploy → ~3초 후 게시

Managing LaunchAgents

# 상태 확인
launchctl list | grep quartz

# fswatch 재시작
launchctl unload ~/Library/LaunchAgents/com.{{AGENT_NAME_LOWER}}.quartz-fswatch.plist
launchctl load -w ~/Library/LaunchAgents/com.{{AGENT_NAME_LOWER}}.quartz-fswatch.plist

# 로그 확인
tail -f ~/Library/Logs/quartz-fswatch.log
tail -f ~/Library/Logs/quartz-deploy.log

Content → Site Mapping

Content-manager drafts live at ~/.{{AGENT_NAME_LOWER}}/P2-hippocampus/memories/insights/. The Quartz site maps this directory into its content tree via symlink:

# Current mapping (June 2026):
YOUR_SITE/content/insights → P2-hippocampus/memories/insights/  # drafts + SVGs + PNGs

⚠️ Pitfall: The symlink was previously pointing to P4-cortex/knowledge (wrong dir). If drafts aren't appearing in the build, check:

readlink ~/.{{AGENT_NAME_LOWER}}/YOUR_SITE/content/insights
# Should point to: ~/.{{AGENT_NAME_LOWER}}/P2-hippocampus/memories/insights

Deploy Loop Prevention

The fswatch → quartz build → wrangler deploy pipeline can enter an infinite loop if build output triggers another fswatch event. Symptoms: deploy log repeating every 10 seconds, launch agents showing error exit codes.

Fix: The deploy script should check if a build is already running before starting a new one. Use a lock file:

LOCKFILE=/tmp/quartz-deploy.lock
if [ -f "$LOCKFILE" ] && [ $(($(date +%s) - $(cat "$LOCKFILE"))) -lt 60 ]; then
    log "Deploy already running, skipping"
    exit 0
fi
date +%s > "$LOCKFILE"
# ... build + deploy ...
rm -f "$LOCKFILE"

[ ] 오탈자·내용 수정
[ ] frontmatter: status: published (draft → published)
[ ] frontmatter: publish_date: YYYY-MM-DD 추가
[ ] aliases: ['/blog/YYYY/slug'] 또는 ['/blog/slug'] 설정 (SEO-friendly URL)
[ ] fswatch가 자동 감지 → YOUR_DOMAIN 게시 (~10초)
[ ] YOUR_DOMAIN에서 게시 확인

Manual Trigger (LaunchAgent 없이)

# 수동 빌드 + 배포 (fswatch 통하지 않고)
bash ~/.{{AGENT_NAME_LOWER}}/P4-cortex/scripts/quartz-deploy.sh

# 또는 wrangler 직접
cd ~/.{{AGENT_NAME_LOWER}}/YOUR_SITE
npx quartz build
wrangler pages deploy public/ --project-name=YOUR_SITE

Configuration Files

~/.{{AGENT_NAME_LOWER}}/YOUR_SITE/
├── wrangler.toml              ← project_name = "YOUR_SITE"
└── .wrangler.jsonv2          ← account_id, CF Pages project 설정

~/.{{AGENT_NAME_LOWER}}/P4-cortex/scripts/
├── quartz-deploy.sh          ← build + wrangler deploy 스크립트
└── quartz-fswatch.sh         ← fswatch 파일 변경 감지 → debounce → deploy

Diagrams & Images in Blog Posts

Mermaid Diagrams (Inline, Auto-Rendered by Quartz)

Quartz at YOUR_DOMAIN has Mermaid enabled (quartz/components/scripts/mermaid.inline.ts). Write ````mermaid code blocks directly in blog post markdown:

```mermaid
graph TD
    subgraph "Source"
        A[Git] --> D
        B[Kanban] --> D
    end
    D{Agent} --> E[Blog Draft]
    D --> F[X Thread]

Common types: `graph TD` (top-down), `graph LR` (left-right), `sequenceDiagram`, `flowchart TD`.

### Excalidraw Diagrams (Hand-Drawn Architecture Style)

For complex architecture/flow diagrams, create an `.excalidraw.json` companion file. Follows the visual style from the ReefWatch article (dev.to/siiddhantt/building-reefwatch).

The blog post references it as: `<!-- EXCALIDRAW: slug.excalidraw.json -- architecture diagram -->`

The JSON opens in Obsidian (Excalidraw plugin) or excalidraw.com for PNG export.

### Cover Image Descriptions

Add an HTML comment describing the ideal cover image:

```markdown
<!-- COVER: Dark mode dev dashboard showing architecture, amber accents, YOUR_DOMAIN style. -->

Placeholder — image generated later by image_gen tool or human designer.

---

Obsidian Publishing Workflow

Step-by-Step

1. Draft 작성 (Phase 4) — memories/insights/에draft 파일 생성
2. Obsidian에서 편집 — 오탈자·内容の견·글쓰기 교정
3. Publication 준비 — frontmatter에서:

   status: published
   publish_date: 2026-05-27
   

4. fswatch가 자동 감지 — vault 파일 변경 → quartz build → Cloudflare Pages 배포
5. YOUR_DOMAIN에서 확인 — 1~2분 내 게시

Alias 규칙 (SEO-Friendly URL)

Frontmatter	Site URL
aliases: ['/blog/2026/agent-workflow']	YOUR_DOMAIN/blog/2026/agent-workflow
aliases: ['/projects/kanban-review']	YOUR_DOMAIN/projects/kanban-review
aliases: ['/lab/dream-system']	YOUR_DOMAIN/lab/dream-system

Slug 생성 규칙:

• Timestamp 제거: KANBAN-REVIEW-20260520 → kanban-review
• 소문자 + kebab: Open Crab Ontology → open-crab-ontology
• 의미 없는 단어 제거: The, A, An

---

WordPress Publishing (Mode B)

Drafts can be pushed to the YOUR_DOMAIN WordPress site via the WordPress MCP server.

Review & Approve Workflow

content-manager draft → memories/insights/ 저장
  → (future) Huly issue 생성 (status: Todo)
  → Drew reviews in Huly → Done
  → publisher cron → WordPress MCP push

See references/wordpress-publish-workflow.md for the full implementation guide.

Related

• [[skills/wordpress-cms]] — WordPress Docker + Blocksy + MCP setup
• [[writing-style-guide]] — Writing tone & rules
• [[@memory/kanban/KANBAN_INDEX]] — Kanban board integration
• [[skills/kanban-worker]] — Worker execution model
• [[skills/content-writer]] — Draft writing skill (별도)

---

⚠️ Path Pitfalls (all modes)

Draft paths in agent profiles, cron prompts, and workflow documents must be absolute paths (~/.{{AGENT_NAME_LOWER}}/P2-hippocampus/memories/insights/). Do NOT use:

• Relative paths like memories/insights/ — the agent's cwd may not resolve correctly
• ~ expansion like ~/.{{AGENT_NAME_LOWER}}/... — some contexts (cron, subagent spawned by dispatcher) don't expand tilde

✅ Safe: ~/.{{AGENT_NAME_LOWER}}/P2-hippocampus/memories/insights/filename.md ❌ Unsafe: memories/insights/filename.md ❌ Unsafe: ~/.{{AGENT_NAME_LOWER}}/P2-hippocampus/memories/insights/filename.md