semantic-article-grouping

name	semantic-article-grouping
description	Group raw articles by semantic topics, assess wiki value, and recommend actions

Semantic Article Grouping

Analyze raw newsletter articles and group them by semantic topics for wiki curation.

Workflow

1. Discover Substantive Articles

import os
raw_dir = os.path.expanduser("~/wiki/raw/articles")
files = [(f, os.path.getsize(os.path.join(raw_dir, f))) 
         for f in os.listdir(raw_dir) if f.endswith('.md') and os.path.getsize(os.path.join(raw_dir, f)) > 1000]
files.sort(key=lambda x: -x[1])  # Largest first

2. Extract Content Metadata

For each article:

• Read title, URL, key phrases
• Identify mentioned entities (people, companies, models, concepts)
• Match against existing wiki topics

3. Semantic Grouping Criteria

Group articles by:

• Shared entities (same person/company/model)
• Related concepts (agentic engineering ↔ harness engineering)
• Event clusters (model releases, leaks, announcements)
• Source themes (Simon Willison newsletter, Latent Space podcast)

4. Value Assessment Matrix

Rate each group for wiki inclusion:

• ★★★★★ = New concept page needed
• ★★★★☆ = Existing page update needed
• ★★★☆☆ = Covered by entity page
• ★★☆☆☆ = Minor mention only
• ★☆☆☆☆ = Not wiki-worthy

5. Output Format

### 📊 Group N: [Topic Name]
**代表トピック:** `[canonical-name]`

| 記事 | 内容 |
|------|------|
| [title] ([size]) | [1-sentence summary] |

**Wiki追加価値:** [rating] - [action recommendation]

6. Recommended Actions

• Create: New concept/entity pages for ★★★★★ groups
• Update: Existing pages for ★★★★☆ groups
• Archive: Move processed articles to processed/ directory
• Skip: Low-value articles remain in raw

Key Patterns to Recognize

Model Releases

• Company + model name + "launches", "releases", "announces"
• Group by company: OpenAI, Anthropic, Meta, Mistral, Google

Engineering Paradigms — TAXONOMY RULES (CRITICAL)

Agentic Engineering (concepts/agentic-engineering/) = 開発者のワークフロー

• Keywords: "how to use agents", "developer patterns", "TDD with agents", "cognitive debt"
• Sources: Simon Willison guides, practitioner blogs, developer workflow tips
• Focus: "人間がエージェントをどう活用するか"
• Examples: Red/Green TDD, First Run the Tests, Showboat, Cognitive Debt, Context Window Management

AI Agent Engineering (concepts/ai-agent-engineering/) = システムアーキテクチャ

• Keywords: "orchestration", "execution loop", "harness", "sandbox", "tool design", "computer environment"
• Sources: OpenAI Engineering blog, Anthropic Engineering blog, platform architecture docs
• Focus: "エージェント実行基盤をどう構築するか"
• Examples: Agent Loop Orchestration, Context Compaction, Container Context, Agent Skills, Security Patterns

Harness Engineering (concepts/harness-engineering/) = 制御・構造化（共通概念）

• Bridges both: how execution environments are designed to constrain and guide agents
• Sources: Ryan Lopopolo/Symphony, OpenAI Codex architecture
• Keywords: "generator-evaluator loop", "critique shadowing", "evaluation-first"

Agent Team / Swarm (concepts/agent-team-swarm/) = 複数Agent協調・オーケストレーション

• Keywords: "multi-agent", "agent team", "swarm", "orchestration platform", "managed agents", "autonomous runs", "work management", "dark factory", "software factory"
• Sources: OpenAI Symphony, Anthropic Managed Agents, StrongDM Attractor, Dan Shapiro's 5-level model
• Focus: "複数Agentをどう協調させ、作業を自律管理するか"
• Examples: OpenAI Symphony (WORKFLOW.md駆動), Anthropic Managed Agents (Brain/Hands/Session分離), StrongDM Dark Factory (完全自律開発)
• 5-Level Model: L1 Spicy Autocomplete → L2 Chat-Assisted → L3 Agent-Assisted → L4 Engineering Team → L5 Dark Factory
• Key distinction: L4 manages agents (Symphony/Managed Agents), L5 eliminates human review entirely (Dark Factory)

Security/Events

• Leaks, controversies, policy changes
• Connect to existing safety concepts

Tool Ecosystem

• New frameworks, libraries, platforms
• Connect to existing entity pages

OpenAI vs Anthropic Platform Comparison

When analyzing platform articles, note the architectural approach:

Dimension	OpenAI	Anthropic
実行環境	マネージドコンテナ提供	開発者自前のharness設計
スキル	SKILL.mdバンドル（公式API）	ツール定義（JSON schema）
コンテキスト	ネイティブcompaction	開発者実装
セキュリティ	サイドカーエグレスプロキシ	開発者責任

Integration Points

• After grouping: use llm-wiki skill to create/update pages
• After processing: update wiki/index.md and wiki/log.md
• Commit: cd ~/ai-topics-cn && git add wiki/ && git commit -m "wiki: [action]" && git push