Rewrite public copy around reader decisions

ANTI-PATTERNS.md

@@ -1,6 +1,6 @@
 # Loop Engineering Anti-Patterns
 
-Use this page to reject unsafe, vague, or misleading loop designs.
+Reject loop designs that hide their objective, evidence gate, state, budget, permissions, or human exit.
 
 ## Prompt Loop With No Contract
 

CONTRIBUTING.md

@@ -2,7 +2,7 @@
 
 Contribute a canonical source, an evidence-backed correction, or a loop you have run.
 
-Scope is intentionally narrow: recurring AI-agent and coding-agent systems with explicit triggers, work intake, verification, durable state, budgets, escalation, and exit.
+Contributions must address recurring AI-agent or coding-agent systems with explicit triggers, work intake, verification, durable state, budgets, escalation, and exit.
 
 Before submitting, read the [curation standard](meta/CURATION.md). It defines scope, evidence tiers, annotation rules, and correction handling.
 
@@ -119,7 +119,9 @@ Use these labels in PR descriptions and issue suggestions when they help reviewe
 
 ## Annotation Style
 
-Keep annotations short, specific, and builder-oriented.
+Write for the person deciding whether to open or use the source. Lead with the concrete contribution, result, mechanism, or limitation; keep annotations short, specific, and builder-oriented.
+
+Avoid repository-centered or self-referential openings such as "this paper explores," "this page provides," or "this list includes." Name what the work does instead. Replace praise such as "excellent," "comprehensive," or "important" with inspectable evidence, reported measurements, or a precise use case.
 
 Use the resource type legend from `README.md`:
 

FUTURE-DIRECTIONS.md

@@ -1,8 +1,8 @@
 # Future Directions
 
-Loop Engineering is still an emerging practice. This agenda turns its largest open gaps into falsifiable research questions, buildable infrastructure, and testable product work. It is a guide to useful next contributions, not a prediction that every direction will succeed.
+Move Loop Engineering forward through falsifiable research, dependable infrastructure, and testable product work. The strongest contributions make recurring agent loops **more measurable, portable, recoverable, economical, or governable**.
 
-Prioritize work that makes at least one recurring agent loop **more measurable, portable, recoverable, economical, or governable**. A credible result should expose the trigger, permissions, external verification gate, durable state, budget, escalation path, and failure evidence.
+A credible result exposes the trigger, permissions, external verification gate, durable state, budget, escalation path, and failure evidence. Negative results matter when they reveal where a loop, verifier, state model, or operating assumption breaks.
 
 ## For Researchers
 

QUOTES.md

@@ -1,6 +1,6 @@
 # Sourced Signals And Quotes
 
-This page records short sourced signals around the emerging Loop Engineering concept. It is not a substitute for the original sources. Use it to understand provenance, then read the linked material directly.
+Trace the emerging Loop Engineering concept through short, sourced signals, then read the linked material for its full context.
 
 ## Addy Osmani
 
@@ -14,7 +14,7 @@ The same article states:
 
 > "Loop engineering sits one floor above the harness."
 
-Why it matters: This anchors the repository's four-layer framing: prompt, context, and harness engineering improve agent runs; Loop Engineering governs repeated agent work over time.
+Why it matters: The statement anchors the four-layer model: prompt, context, and harness engineering improve agent runs; Loop Engineering governs repeated agent work over time.
 
 ## Peter Steinberger
 

README.de.md

@@ -1,6 +1,6 @@
 # Awesome Loop Engineering
 
-<!-- last-synced: 2026-07-12 -->
+<!-- last-synced: 2026-07-17 -->
 
 <p align="center">
   <img src="assets/awesome-loop-engineering-cover.png" alt="Awesome Loop Engineering cover" width="100%">
@@ -20,15 +20,15 @@
   <a href="https://huggingface.co/datasets/cy0307/awesome-loop-engineering">Hugging-Face-Spiegel</a>
 </p>
 
-> Eine kuratierte, umsetzungsorientierte Liste für **Loop Engineering**: die Ebene oberhalb von Prompt Engineering, Context Engineering und Harness Engineering zum Entwerfen wiederkehrender KI-Agenten-Systeme.
+> Entwirf wiederkehrende KI-Agenten-Systeme mit klaren Auslösern, externer Verifikation, dauerhaftem Zustand, begrenzten Budgets und menschlicher Übergabe.
 
 Prompt Engineering verbessert, was man dem Modell sagt. Context Engineering verbessert, was das Modell sehen kann. Harness Engineering verbessert Werkzeuge, Berechtigungen, Sandboxes und Prüfungen rund um einen einzelnen Agentenlauf. **Loop Engineering liegt über allen drei Ebenen**: Es entwirft Systeme, die Agenten auslösen, überwachen, Ergebnisse prüfen, Zustand speichern und erneut laufen.
 
 Ein Loop entdeckt Arbeit, übergibt sie an einen oder mehrere Agenten, prüft das Ergebnis, speichert Zustand, entscheidet den nächsten Schritt und läuft erneut nach Zeitplan oder bis ein überprüfbares Ziel erreicht ist.
 
-Dieses Repository behandelt die neue Bedeutung von Loop Engineering im Kontext von KI-Agenten und Coding Agents. Es geht nicht um Software-Event-Loops, Regelungstechnik, Growth Loops, generische Automatisierung oder nicht-KI-bezogene Feedback-Loops.
+Loop Engineering umfasst wiederkehrende KI-Agenten- und Coding-Agent-Systeme mit expliziten Auslösern, Verifikation und dauerhaftem Zustand. Software-Event-Loops, Regelungstechnik, Growth Loops, generische Automatisierung und nicht-KI-bezogene Feedback-Loops gehören nicht dazu.
 
-Neben 545 kuratierten Ressourcen bietet dieses Repository 20 Loop-Patterns, einen schemavalidierten Loop-Contract pro Pattern, 8 Runtime-Starter (3 ausführbare Programme und 5 Copy-and-paste-Vorlagen), eine Community-Galerie und 8 Sprachen.
+Starte mit 545 geprüften Ressourcen, 20 operativen Loop-Patterns, 20 schemavalidierten Loop-Contracts und 8 Runtime-Startern (3 ausführbare Programme und 5 anpassbare Vorlagen), ergänzt durch eine Community-Galerie und 8 Sprachen.
 
 ## Mentales Modell
 

README.es.md

@@ -1,6 +1,6 @@
 # Awesome Loop Engineering
 
-<!-- last-synced: 2026-07-12 -->
+<!-- last-synced: 2026-07-17 -->
 
 <p align="center">
   <img src="assets/awesome-loop-engineering-cover.png" alt="Awesome Loop Engineering cover" width="100%">
@@ -20,15 +20,15 @@
   <a href="https://huggingface.co/datasets/cy0307/awesome-loop-engineering">Espejo en Hugging Face</a>
 </p>
 
-> Una lista curada y orientada a la implementación para **Loop Engineering**: la capa por encima de prompt, context y harness engineering para diseñar sistemas recurrentes de agentes de IA.
+> Diseña sistemas recurrentes de agentes de IA con disparadores explícitos, verificación externa, estado duradero, presupuestos limitados y traspaso a personas.
 
 Prompt engineering mejora lo que le pides al modelo. Context engineering mejora lo que el modelo puede ver. Harness engineering mejora las herramientas, permisos, sandboxes y verificaciones alrededor de una ejecución de agente. **Loop Engineering está por encima de las tres**: diseña sistemas que activan agentes, los supervisan, verifican resultados, guardan estado y vuelven a ejecutarse.
 
 Un loop descubre trabajo, lo delega a uno o más agentes, verifica el resultado, registra estado, decide la siguiente acción y vuelve a ejecutarse con una cadencia o hasta alcanzar un objetivo verificable.
 
-Este repositorio se centra en el significado nuevo de Loop Engineering para agentes de IA y coding agents. No trata sobre event loops de software, teoría de control, growth loops, automatización genérica o feedback loops no relacionados con IA.
+Loop Engineering abarca sistemas recurrentes de agentes de IA y coding agents con disparadores explícitos, verificación y estado duradero. No incluye event loops de software, teoría de control, growth loops, automatización genérica ni feedback loops no relacionados con IA.
 
-Además de 545 recursos curados, este repositorio incluye 20 patrones de loop, un loop contract validado por esquema para cada patrón, 8 puntos de partida de runtime (3 ejecutables y 5 plantillas listas para adaptar), una galería comunitaria y 8 idiomas.
+Empieza con 545 recursos auditados, 20 patrones operativos, 20 loop contracts validados por esquema y 8 puntos de partida de runtime (3 ejecutables y 5 plantillas listas para adaptar), además de una galería comunitaria y 8 idiomas.
 
 ## Modelo Mental
 

README.fr.md

@@ -1,6 +1,6 @@
 # Awesome Loop Engineering
 
-<!-- last-synced: 2026-07-12 -->
+<!-- last-synced: 2026-07-17 -->
 
 <p align="center">
   <img src="assets/awesome-loop-engineering-cover.png" alt="Awesome Loop Engineering cover" width="100%">
@@ -20,15 +20,15 @@
   <a href="https://huggingface.co/datasets/cy0307/awesome-loop-engineering">Miroir Hugging Face</a>
 </p>
 
-> Une liste organisée et orientée implémentation pour **Loop Engineering** : la couche au-dessus du prompt engineering, du context engineering et du harness engineering pour concevoir des systèmes récurrents d'agents IA.
+> Concevez des systèmes récurrents d'agents IA avec des déclencheurs explicites, une vérification externe, un état durable, des budgets bornés et un relais humain.
 
 Le prompt engineering améliore ce que l'on demande au modèle. Le context engineering améliore ce que le modèle peut voir. Le harness engineering améliore les outils, permissions, sandboxes et vérifications autour d'une exécution d'agent. **Loop Engineering se situe au-dessus des trois** : il conçoit des systèmes qui déclenchent des agents, les supervisent, vérifient les résultats, persistent l'état et relancent le travail.
 
 Un loop découvre du travail, le délègue à un ou plusieurs agents, vérifie le résultat, enregistre l'état, décide de l'action suivante et se relance selon une cadence ou jusqu'à atteindre un objectif vérifiable.
 
-Ce dépôt concerne le nouveau sens de Loop Engineering dans le contexte des agents IA et coding agents. Il ne concerne pas les event loops logiciels, la théorie du contrôle, les growth loops, l'automatisation générique ou les feedback loops non liés à l'IA.
+Loop Engineering couvre les systèmes récurrents d'agents IA et de coding agents avec des déclencheurs explicites, une vérification et un état durable. Il n'inclut ni les event loops logiciels, ni la théorie du contrôle, ni les growth loops, ni l'automatisation générique, ni les feedback loops sans rapport avec l'IA.
 
-Au-delà de 545 ressources sélectionnées, ce dépôt fournit 20 patterns de loop, un loop contract validé par schéma pour chaque pattern, 8 points de départ d'exécution (3 exécutables et 5 modèles prêts à adapter), une galerie communautaire et 8 langues.
+Commencez avec 545 ressources auditées, 20 patterns opérationnels, 20 loop contracts validés par schéma et 8 points de départ d'exécution (3 exécutables et 5 modèles prêts à adapter), ainsi qu'une galerie communautaire et 8 langues.
 
 ## Modèle Mental
 

README.ja.md

@@ -1,6 +1,6 @@
 # Awesome Loop Engineering
 
-<!-- last-synced: 2026-07-12 -->
+<!-- last-synced: 2026-07-17 -->
 
 <p align="center">
   <img src="assets/awesome-loop-engineering-cover.png" alt="Awesome Loop Engineering cover" width="100%">
@@ -20,15 +20,15 @@
   <a href="https://huggingface.co/datasets/cy0307/awesome-loop-engineering">Hugging Face ミラー</a>
 </p>
 
-> **Loop Engineering** のための、実装志向のキュレーションリストです。Loop Engineering は prompt、context、harness engineering の上位レイヤーとして、繰り返し実行される AI-agent システムを設計します。
+> 明示的なトリガー、外部検証、永続状態、制限付き予算、人間への引き継ぎを備えた、繰り返し実行される AI-agent システムを設計します。
 
 Prompt engineering はモデルに何を依頼するかを改善します。Context engineering はモデルが何を見られるかを改善します。Harness engineering は 1 回の agent 実行を取り巻くツール、権限、sandbox、検証を改善します。**Loop Engineering はその上位にあります**。agent を起動し、監督し、結果を検証し、状態を保存し、再実行するシステムを設計する実践です。
 
 Loop は作業を発見し、1 つ以上の agents に委任し、結果を確認し、状態を記録し、次の行動を決め、一定の cadence または検証可能な目標に到達するまで再実行されます。
 
-このリポジトリは AI agents / coding agents における新しい意味の Loop Engineering に限定しています。software event loop、制御理論、growth loop、一般的な workflow automation、非 AI の feedback loop は対象外です。
+Loop Engineering は、明示的なトリガー、検証、永続状態を備えた反復型 AI agent / coding agent システムを対象とします。software event loop、制御理論、growth loop、一般的な workflow automation、非 AI の feedback loop は含みません。
 
-545 件の精選リソースに加えて、本リポジトリは 20 個の loop パターン、各パターンに対応する schema 検証済み loop contract、8 個の runtime starter（3 個の実行ファイルと 5 個の適応可能なテンプレート）、コミュニティ gallery、8 言語を提供します。
+545 件の監査済みリソース、20 個の運用パターン、20 個の schema 検証済み loop contract、8 個の runtime starter（3 個の実行ファイルと 5 個の適応可能なテンプレート）から始められます。コミュニティ gallery と 8 言語の導入も利用できます。
 
 ## メンタルモデル
 

README.ko.md

@@ -1,6 +1,6 @@
 # Awesome Loop Engineering
 
-<!-- last-synced: 2026-07-12 -->
+<!-- last-synced: 2026-07-17 -->
 
 <p align="center">
   <img src="assets/awesome-loop-engineering-cover.png" alt="Awesome Loop Engineering cover" width="100%">
@@ -20,15 +20,15 @@
   <a href="https://huggingface.co/datasets/cy0307/awesome-loop-engineering">Hugging Face 미러</a>
 </p>
 
-> **Loop Engineering** 을 위한 구현 중심 큐레이션 목록입니다. Loop Engineering 은 prompt, context, harness engineering 위의 레이어로, 반복 실행되는 AI-agent 시스템을 설계합니다.
+> 명시적인 트리거, 외부 검증, 지속 상태, 제한된 예산, 사람에게 넘기는 절차를 갖춘 반복 AI-agent 시스템을 설계합니다.
 
 Prompt engineering 은 모델에게 무엇을 요청할지 개선합니다. Context engineering 은 모델이 무엇을 볼 수 있는지 개선합니다. Harness engineering 은 한 번의 agent 실행을 둘러싼 도구, 권한, sandbox, 검증을 개선합니다. **Loop Engineering 은 이 세 레이어 위에 있습니다**. agent 를 실행하고, 감독하고, 결과를 검증하고, 상태를 저장하고, 다시 실행하는 시스템을 설계하는 실천입니다.
 
 Loop 는 작업을 발견하고, 하나 이상의 agents 에게 위임하고, 결과를 확인하고, 상태를 기록하고, 다음 행동을 결정한 뒤, 일정 cadence 또는 검증 가능한 목표에 도달할 때까지 다시 실행됩니다.
 
-이 저장소는 AI agents / coding agents 맥락의 새로운 Loop Engineering 의미에만 집중합니다. software event loop, control theory, growth loop, 일반적인 workflow automation, 비 AI feedback loop 는 범위에 포함하지 않습니다.
+Loop Engineering 은 명시적 트리거, 검증, 지속 상태를 갖춘 반복 AI agent / coding agent 시스템을 다룹니다. software event loop, control theory, growth loop, 일반적인 workflow automation, 비 AI feedback loop 는 포함하지 않습니다.
 
-545개의 엄선된 리소스 외에도 이 저장소는 20개의 loop 패턴, 각 패턴에 대한 스키마 검증 loop contract, 8개의 runtime starter(실행 파일 3개와 적용 가능한 템플릿 5개), 커뮤니티 gallery, 8개 언어를 제공합니다.
+545개의 감사된 리소스, 20개의 운영 loop 패턴, 20개의 스키마 검증 loop contract, 8개의 runtime starter(실행 파일 3개와 적용 가능한 템플릿 5개)로 바로 시작할 수 있습니다. 커뮤니티 gallery 와 8개 언어 안내도 제공합니다.
 
 ## 멘탈 모델
 

README.md

@@ -50,7 +50,7 @@ configs:
 
 ## Dataset Summary
 
-Each row represents one resource from the canonical English field guide. It pairs a concise editorial assessment with bibliographic metadata, lifecycle and audience facets, evidence classification, audit status, and point-in-time repository statistics when applicable.
+Each row pairs one source with a concise editorial assessment, bibliographic metadata, lifecycle and audience facets, evidence classification, audit status, and point-in-time repository statistics when applicable.
 
 Current release: **v0.7.0**
 
@@ -104,7 +104,7 @@ Do not use `signal_strength`, GitHub stars, forks, or inclusion in this collecti
 
 ## Future Directions
 
-The project maintains a role-based agenda for moving Loop Engineering forward:
+Choose a role to find the highest-value open questions and build targets:
 
 | Audience | Priority categories |
 | --- | --- |
@@ -121,7 +121,7 @@ The primary configuration is `resources`, with one `train` split backed by `data
 | Field group | Fields | What it describes |
 | --- | --- | --- |
 | Identity | `row_id`, `title`, `url`, `canonical_url`, `resource_type`, `domain` | What the resource is and where it lives. |
-| Editorial assessment | `annotation`, `key_contribution`, `novelty`, `impact` | Why the resource matters in this collection. |
+| Editorial assessment | `annotation`, `key_contribution`, `novelty`, `impact` | What the resource contributes to recurring agent systems. |
 | Navigation | `section`, `collection`, `user_goal`, `lifecycle_stages`, `audience` | Where the resource fits and who it serves. |
 | Evidence | `evidence_class`, `signal`, `signal_strength`, `source_status`, `audited_at` | What kind of evidence or provenance is available. |
 | Publication | `authors`, `publication_date`, `publication_year`, `publication_venue`, `publisher`, `doi`, `arxiv_id`, `primary_category` | Bibliographic data exposed by the canonical source. |
@@ -133,7 +133,7 @@ The complete field-by-field schema is documented in [`data/README.md`](https://g
 
 ## Curation And Provenance
 
-The collection prioritizes primary papers, official documentation, project repositories, and implementation-heavy practitioner sources. Every released row must pass the public [curation standard](https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/meta/CURATION.md).
+Start with primary papers, official documentation, project repositories, and implementation-heavy practitioner sources. Every released row must pass the public [curation standard](https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/meta/CURATION.md).
 
 Annotations are original syntheses, not copied abstracts or claims of author endorsement. Automation may assist discovery, URL resolution, duplicate detection, bibliographic extraction, repository statistics, and draft normalization. The canonical source remains the evidence; the maintainer owns each released inclusion decision and annotation.
 

README.pt-BR.md

@@ -1,6 +1,6 @@
 # Awesome Loop Engineering
 
-<!-- last-synced: 2026-07-12 -->
+<!-- last-synced: 2026-07-17 -->
 
 <p align="center">
   <img src="assets/awesome-loop-engineering-cover.png" alt="Awesome Loop Engineering cover" width="100%">
@@ -20,15 +20,15 @@
   <a href="https://huggingface.co/datasets/cy0307/awesome-loop-engineering">Espelho no Hugging Face</a>
 </p>
 
-> Uma lista curada e orientada à implementação para **Loop Engineering**: a camada acima de prompt, context e harness engineering para projetar sistemas recorrentes de agentes de IA.
+> Projete sistemas recorrentes de agentes de IA com gatilhos explícitos, verificação externa, estado durável, orçamentos limitados e transferência para pessoas.
 
 Prompt engineering melhora o que você pede ao modelo. Context engineering melhora o que o modelo consegue ver. Harness engineering melhora ferramentas, permissões, sandboxes e verificações ao redor de uma execução de agente. **Loop Engineering fica acima das três**: projeta sistemas que acionam agentes, supervisionam, verificam resultados, persistem estado e executam novamente.
 
 Um loop descobre trabalho, delega para um ou mais agentes, verifica o resultado, registra estado, decide a próxima ação e executa novamente em uma cadência ou até atingir um objetivo verificável.
 
-Este repositório trata do novo significado de Loop Engineering no contexto de AI agents e coding agents. Ele não trata de event loops de software, teoria de controle, growth loops, automação genérica ou feedback loops não relacionados a IA.
+Loop Engineering abrange sistemas recorrentes de AI agents e coding agents com gatilhos explícitos, verificação e estado durável. Não inclui event loops de software, teoria de controle, growth loops, automação genérica nem feedback loops sem relação com IA.
 
-Além de 545 recursos curados, este repositório inclui 20 patterns de loop, um loop contract validado por schema para cada pattern, 8 starters de runtime (3 executáveis e 5 templates prontos para adaptação), uma galeria comunitária e 8 idiomas.
+Comece com 545 recursos auditados, 20 patterns operacionais, 20 loop contracts validados por schema e 8 starters de runtime (3 executáveis e 5 templates prontos para adaptação), além de uma galeria comunitária e 8 idiomas.
 
 ## Modelo Mental
 

README.zh-CN.md

@@ -24,15 +24,15 @@
   <a href="https://huggingface.co/datasets/cy0307/awesome-loop-engineering">Hugging Face 镜像</a>
 </p>
 
-> 面向 **Loop Engineering** 的精选资源列表：它位于 prompt、context 和 harness engineering 之上，关注如何设计可重复运行的 AI-agent 系统。
+> 设计可重复运行的 AI-agent 系统：明确触发条件、外部验证、持久状态、资源预算和人类接管机制。
 
 Prompt engineering 改进你对模型说什么。Context engineering 改进模型能看到什么。Harness engineering 改进单次 agent 运行周围的工具、权限、沙箱和检查。**Loop Engineering 位于三者之上**：人不再逐轮手动提示 agent，而是设计一个循环系统，让它负责提示、监督、验证、更新状态并再次触发 agents。
 
 一个 loop 会发现工作、分派给一个或多个 agents、检查结果、记录状态、决定下一步，并按照节奏或直到满足可验证目标为止持续运行。
 
-本仓库不收录软件事件循环、控制论、增长循环、通用 workflow automation 或非 AI feedback loop。
+Loop Engineering 专指具备明确触发、外部验证和持久状态的可重复 AI-agent 与 coding-agent 系统，不包括软件事件循环、控制论、增长循环、通用 workflow automation 或非 AI feedback loop。
 
-除资源列表外，本仓库还维护 545 条经过审阅的资源记录，并提供 20 个 loop 模式、20 个经 schema 校验的 loop contracts、8 个 runtime starters（3 个可直接执行，5 个可复制改造）、社区 gallery，以及 8 个语言入口。
+可直接从 545 条经审核资源、20 个操作模式、20 个经 schema 校验的 loop contracts 和 8 个 runtime starters（3 个可直接执行，5 个可复制改造）开始，并通过社区 gallery 和 8 种语言入口继续探索。
 
 ## 一句话定位
 

ROADMAP.md

@@ -1,8 +1,8 @@
 # Roadmap
 
-The roadmap prioritizes stronger evidence, runnable implementations, and real operating results.
+Prioritize stronger evidence, runnable implementations, and measured operating results.
 
-The role-based [Future Directions agenda](FUTURE-DIRECTIONS.md) complements this project roadmap with research questions, infrastructure priorities, application opportunities, measurable outputs, and starter projects.
+Use the role-based [Future Directions agenda](FUTURE-DIRECTIONS.md) to choose research questions, infrastructure priorities, application opportunities, measurable outputs, and starter projects.
 
 ## Near Term
 
@@ -23,7 +23,7 @@ Next pattern-library work should prioritize variants backed by operational evide
 
 - Publish a concise monthly Discussions digest with corrected annotations, new primary sources, and open contributor tasks.
 - Keep several narrowly scoped `good first issue` and `help wanted` tasks available for source audits, translations, runnable examples, and gallery case studies.
-- Ask cited authors to review the repository's characterization of their work; request corrections, not promotion or stars.
+- Ask cited authors to review their annotations; request corrections, not promotion or stars.
 - Track qualified traffic, forks, watchers, and external contributions after each launch channel while GitHub traffic data is still available.
 
 ## Gallery
@@ -42,7 +42,7 @@ The gallery should grow from reference examples into public or anonymized case s
 
 - Keep CI dependency-light and easy for contributors to run locally.
 - Keep all resource annotations tied to recurring agent systems, not generic AI-agent interest.
-- Keep public claims conservative: this repository is an early curated field guide, not a finished standard.
+- Describe Awesome Loop Engineering as an early field guide, not a finished standard.
 - Preserve clean owner-only commit identity for `main`.
 
 ## Open Questions

SECURITY.md

@@ -1,6 +1,6 @@
 # Security Policy
 
-This repository is a curated documentation and resource list. It does not ship a production service, package, or runtime. Security concerns can still appear in examples, scripts, links, or contribution material.
+Awesome Loop Engineering ships documentation, data, examples, and scripts rather than a production service or hosted runtime. Security concerns can still appear in executable starters, schemas, links, or contributed material.
 
 ## Please Report
 

SUPPORT.md

@@ -1,6 +1,6 @@
 # Support
 
-This repository is maintained as a public field guide and curated resource list for Loop Engineering.
+Get help with the Loop Engineering field guide, resource data, patterns, contracts, and runtime starters through the public channels below.
 
 ## Best Channels
 
@@ -17,9 +17,9 @@ This repository is maintained as a public field guide and curated resource list
 - reviewing loop pattern contracts for trigger, intake, verification, state, budget, escalation, and exit;
 - fixing broken links or replacing unstable sources.
 
-## What Is Out Of Scope
+## Where Else To Get Help
 
-- product support for third-party agent tools;
-- private consulting on proprietary loops;
-- generic prompt engineering questions with no loop, state, verification, or recurring system angle;
-- unrelated software event loops, growth loops, control theory loops, or generic automation.
+- Ask each tool's maintainer for product support.
+- Use an appropriate professional channel for private consulting on proprietary loops.
+- Take one-off prompt engineering questions to a prompt-focused community.
+- Use domain-specific communities for software event loops, growth loops, control theory, and generic automation.

TAXONOMY.md

@@ -1,6 +1,6 @@
 # Loop Engineering Taxonomy
 
-This taxonomy gives contributors a shared vocabulary for classifying AI-agent loops.
+Classify an AI-agent loop by its trigger, work intake, verification method, state model, agent topology, and operating domain.
 
 ## By Trigger
 

TRANSLATIONS.md

@@ -1,6 +1,6 @@
 # Translations
 
-The canonical list is [README.md](README.md). Translations should help more readers understand Loop Engineering without drifting from the repository's scope.
+Translate the core Loop Engineering concepts for new audiences while preserving the boundaries and facts in the canonical [English README](README.md).
 
 ## Available
 

assets/MEDIA_KIT.md

@@ -28,4 +28,4 @@ The original 1200x630 cover presents Loop Engineering as the operating layer abo
 - Preserve the semantic layer order: Prompt, Context, Harness, then Loop.
 - Follow the clear-space, color, and mark rules in the [brand system](BRAND.md).
 
-Unless otherwise noted, these assets use the repository's [CC0-1.0 license](../LICENSE).
+Unless otherwise noted, the original brand assets are released under [CC0-1.0](../LICENSE).

assets/README.md

@@ -1,6 +1,6 @@
 # Assets
 
-This directory contains visual assets used by the repository README, GitHub Pages landing page, and social previews.
+Find the logos, diagrams, editorial cover, social previews, and generated brand assets used across GitHub and the interactive website.
 
 - `awesome-loop-engineering-cover.png` - Original 1200x630 editorial cover, preserved for articles, talks, launch posts, and project features. It visualizes the Prompt, Context, Harness, and Loop stack.
 - `awesome-loop-engineering-logo-master.png` - High-resolution transparent ChatGPT Image source for the primary mark.

data/README.md

@@ -1,6 +1,6 @@
 # Dataset Exports
 
-This directory contains deterministic tabular exports of the canonical English resource list.
+Download deterministic tabular exports of all 545 source-audited resources.
 
 - `resources.csv` - Tabular export for spreadsheets and ad hoc analysis.
 - `resources.jsonl` - JSON Lines export; this file backs the Hugging Face Dataset Viewer (the dataset card's configs point at it).
@@ -14,7 +14,7 @@ The exports preserve each section and annotation while adding three discovery la
 - **Evidence facets**: `evidence_class`, `signal_strength`, `source_status`, canonical URL, source metadata, GitHub statistics, arXiv ID, and audit timestamp separate source provenance from popularity or editorial judgment.
 - **Publication facets**: `authors`, `publication_date`, `publication_year`, `publication_venue`, `publisher`, `doi`, `publication_note`, `primary_category`, and `metadata_source` provide a paper-like bibliographic row without inventing missing facts.
 
-`key_contribution`, `novelty`, and `impact` are resource-specific. `signal` states the evidence basis and limits; GitHub stars and forks provide point-in-time context, never proof of reliability. `signal_strength` is `high` for primary official documentation and benchmarks, `medium` for inspectable implementations, papers, patterns, and repository-native artifacts, `contextual` for practitioner analysis and curated lists, and `unverified` only when the latest audit cannot validate availability.
+`key_contribution`, `novelty`, and `impact` are resource-specific. `signal` states the evidence basis and limits; GitHub stars and forks provide point-in-time context, never proof of reliability. `signal_strength` is `high` for primary official documentation and benchmarks, `medium` for inspectable implementations, papers, patterns, and locally maintained artifacts, `contextual` for practitioner analysis and curated lists, and `unverified` only when the latest audit cannot validate availability.
 
 ## Load And Query
 
@@ -55,7 +55,7 @@ python3 scripts/check_project_consistency.py
 python3 scripts/build_hf_card.py --check
 ```
 
-The export is deterministic and includes one row for every README resource-table row that follows the repository's curated entry format: marker, resource type, title, link, original publishing platform or venue, annotation, section, and source line.
+Every README resource-table row becomes one deterministic export row with its marker, resource type, title, link, original publishing platform or venue, annotation, section, and source line.
 
 Run a network-backed source audit when refreshing the Hugging Face dataset:
 

docs/index.html

@@ -987,7 +987,7 @@
         "description": "A source-audited field guide and implementation kit for recurring, stateful, verified AI-agent systems.",
         "keywords": ["Loop Engineering", "AI agents", "Coding agents", "Agent workflows", "Agentic loops"],
         "datePublished": "2026-06-10",
-        "dateModified": "2026-07-17",
+        "dateModified": "2026-07-18",
         "sameAs": [
           "https://github.com/ChaoYue0307/awesome-loop-engineering",
           "https://huggingface.co/datasets/cy0307/awesome-loop-engineering"
@@ -1036,7 +1036,7 @@
         "@context": "https://schema.org",
         "@type": "Dataset",
         "name": "Awesome Loop Engineering resources",
-        "description": "Curated, annotated resources on recurring AI-agent and coding-agent loops, with contribution, novelty, impact, and signal fields.",
+        "description": "545 source-audited resources on recurring AI-agent and coding-agent loops, with contribution, novelty, impact, and evidence fields.",
         "url": "https://chaoyue0307.github.io/awesome-loop-engineering/",
         "sameAs": [
           "https://huggingface.co/datasets/cy0307/awesome-loop-engineering",
@@ -1048,7 +1048,7 @@
         "isAccessibleForFree": true,
         "creator": { "@type": "Person", "name": "Chaoyue He" },
         "datePublished": "2026-06-10",
-        "dateModified": "2026-07-17",
+        "dateModified": "2026-07-18",
         "keywords": ["Loop Engineering", "AI agents", "Coding agents", "Agent reliability", "Agent evaluation"],
         "includedInDataCatalog": {
           "@type": "DataCatalog",
@@ -1113,7 +1113,7 @@
             <p class="tagline">Design loops, not just prompts.</p>
             <p class="lead">545 source-audited resources, 20 operational patterns, 20 validated contracts, and 8 runtime starters for agent systems that must keep working.</p>
             <div class="actions">
-              <a class="button primary" href="https://github.com/ChaoYue0307/awesome-loop-engineering#readme"><i class="icon" data-lucide="book-open" aria-hidden="true"></i>Explore the list</a>
+              <a class="button primary" href="https://github.com/ChaoYue0307/awesome-loop-engineering#readme"><i class="icon" data-lucide="book-open" aria-hidden="true"></i>Explore resources</a>
               <a class="button" href="#choose"><i class="icon" data-lucide="compass" aria-hidden="true"></i>Choose a loop</a>
             </div>
           </div>
@@ -1167,7 +1167,7 @@
               <p class="mobile-stack-note">The first three layers improve <strong>one run</strong>. Loop Engineering governs <strong>work over time</strong>.</p>
             </div>
           </figure>
-          <div class="stats" role="list" aria-label="Repository at a glance">
+          <div class="stats" role="list" aria-label="Field guide at a glance">
             <div class="stat" role="listitem"><b><i class="icon" data-lucide="library" aria-hidden="true"></i>545</b><span>audited resources</span></div>
             <div class="stat" role="listitem"><b><i class="icon" data-lucide="blocks" aria-hidden="true"></i>20</b><span>operational patterns</span></div>
             <div class="stat" role="listitem"><b><i class="icon" data-lucide="file-check-2" aria-hidden="true"></i>20</b><span>loop contracts</span></div>
@@ -1421,7 +1421,7 @@
               <button class="resource-collection" type="button" data-collection="Apply" aria-pressed="false"><i class="icon" data-lucide="package-open" aria-hidden="true"></i>Apply</button>
             </div>
             <div class="resource-summary">
-              <p class="resource-count" id="resource-count" aria-live="polite">Resource index loads as you approach this section.</p>
+              <p class="resource-count" id="resource-count" aria-live="polite">Preparing 545 resources...</p>
               <label>
                 <span class="sr-only">Sort resources</span>
                 <select class="resource-select resource-sort" id="resource-sort">
@@ -1553,7 +1553,7 @@
       <div class="wrap">
         <div class="footer-grid">
           <div>
-            <h4><i class="icon" data-lucide="quote" aria-hidden="true"></i>Cite this repository</h4>
+            <h4><i class="icon" data-lucide="quote" aria-hidden="true"></i>Cite the field guide</h4>
             <div class="cite">
               <button class="copy" type="button" data-copy="cite-bib" aria-label="Copy BibTeX citation"><span aria-live="polite">Copy</span></button>
               <pre id="cite-bib">@misc{chaoyue2026awesome_loop_engineering,
@@ -1597,7 +1597,7 @@
             <a href="https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.pt-BR.md" lang="pt-BR">Português</a>
           </div>
         </div>
-        <p class="legal">This is a source-audited map of an emerging practice, not a final standard. Original repository materials are released under <a href="https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/LICENSE">CC0-1.0</a>. Linked resources retain their own licenses.</p>
+        <p class="legal">Treat each annotation as a map to primary evidence, not as a final standard or endorsement. Original field-guide materials use <a href="https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/LICENSE">CC0-1.0</a>; linked resources retain their own licenses.</p>
       </div>
     </footer>
 

examples/runnable/README.md

@@ -2,7 +2,7 @@
 
 Eight implementation starters connect a loop contract to a runtime. Three are dependency-light executables; five are copy/paste runtime templates with concrete prompts, schedules, permissions, state, and stop conditions.
 
-These are deliberately small. They demonstrate the control loop without hiding permissions, verification, state, or budgets inside a framework.
+Each starter keeps the control loop visible: permissions, verification, state, and budgets remain explicit instead of disappearing inside a framework.
 
 ## Choose A Starter
 

examples/runnable/codex-automation.md

@@ -54,4 +54,4 @@ Escalation:
 
 - Keep the patch scoped to the failure cause; scope creep is the most common way these loops go wrong.
 - Pair the event trigger with a periodic sweep so a missed event does not leave work undone.
-- This template mirrors the [CI repair pattern](../../patterns/ci-repair-loop.md); start from that contract for the full version.
+- Start from the full [CI repair pattern and contract](../../patterns/ci-repair-loop.md) before adapting this runtime variant.

examples/runnable/github-agentic-workflow.md

@@ -53,4 +53,4 @@ job: docs-drift-check
 - Default to read-only permissions and request only the minimum scopes the job needs; see [Securing Unattended Loops](../../README.md#securing-unattended-loops).
 - Make the verification gate a real status check, not a model summary.
 - Keep the escalation path a reviewable PR; never let the workflow merge its own changes to a protected branch.
-- This template mirrors the [docs drift pattern](../../patterns/docs-drift-collector.md); start from that contract for the full version.
+- Start from the full [docs drift pattern and contract](../../patterns/docs-drift-collector.md) before adapting this runtime variant.

gallery/README.md

@@ -1,8 +1,6 @@
 # Loop Gallery
 
-The gallery is for real-world or realistic loop examples contributed by the community.
-
-Use it to show how a loop works in practice, not just what resources describe it. A good gallery entry should be concrete enough that another builder can adapt the pattern without guessing the trigger, state, verification, or escalation rules.
+Share how a loop works in practice through a real, realistic, or safely anonymized case study. Include enough detail for another builder to understand the trigger, state, verification, budget, and escalation rules without guessing.
 
 ## Contribution Format
 

gallery/ci-repair-reference.md

@@ -2,7 +2,7 @@
 
 ## Summary
 
-This is a reference gallery entry for a loop that turns failing CI into a small verified patch or an escalation note. It is based on the repository's [`CI repair loop`](../patterns/ci-repair-loop.md) pattern and [`ci-repair-loop.json`](../examples/ci-repair-loop.json) contract, not a claimed production deployment.
+A reference design for turning failing CI into a small verified patch or a useful escalation note, built from the [`CI repair loop`](../patterns/ci-repair-loop.md) pattern and [`ci-repair-loop.json`](../examples/ci-repair-loop.json) contract. It illustrates the operating shape; it does not claim a production deployment.
 
 ## Runtime Or Tooling
 

gallery/docs-drift-reference.md

@@ -2,7 +2,7 @@
 
 ## Summary
 
-This is a reference gallery entry for a loop that finds and patches documentation drift. It is based on the repository's [`Docs drift collector`](../patterns/docs-drift-collector.md) pattern and [`docs-drift-loop.json`](../examples/docs-drift-loop.json) contract, not a claimed production deployment.
+A reference design for finding and patching documentation drift, built from the [`Docs drift collector`](../patterns/docs-drift-collector.md) pattern and [`docs-drift-loop.json`](../examples/docs-drift-loop.json) contract. It illustrates the operating shape; it does not claim a production deployment.
 
 ## Runtime Or Tooling
 

gallery/pr-babysitter-reference.md

@@ -2,7 +2,7 @@
 
 ## Summary
 
-This is a reference gallery entry for a loop that keeps one pull request moving toward merge readiness. It is based on the repository's [`PR babysitter`](../patterns/pr-babysitter.md) pattern and [`pr-babysitter-loop.json`](../examples/pr-babysitter-loop.json) contract, not a claimed production deployment.
+A reference design for keeping one pull request moving toward merge readiness, built from the [`PR babysitter`](../patterns/pr-babysitter.md) pattern and [`pr-babysitter-loop.json`](../examples/pr-babysitter-loop.json) contract. It illustrates the operating shape; it does not claim a production deployment.
 
 ## Runtime Or Tooling
 

meta/CURATION.md

@@ -1,6 +1,6 @@
 # Curation Standard
 
-This collection is intentionally selective. It covers recurring AI-agent and coding-agent systems with explicit triggers, verification, durable state, budgets, escalation, and exit; it is not a general agent link directory.
+Include only recurring AI-agent and coding-agent systems with explicit triggers, verification, durable state, budgets, escalation, and exit. General agent links belong elsewhere.
 
 ## Acceptance Test
 
@@ -49,7 +49,7 @@ The maintainer owns every released inclusion decision, category, annotation, and
 
 The public dataset keeps those layers separate:
 
-- `annotation`, `key_contribution`, `novelty`, and `impact` are original repository syntheses;
+- `annotation`, `key_contribution`, `novelty`, and `impact` are original editorial syntheses;
 - `source_title`, `source_description`, authorship, date, venue, DOI, and repository statistics come from the source audit and record their metadata provenance;
 - `signal_strength` describes the kind of evidence available, not whether the maintainer agrees with the work;
 - `source_status` records reachability at one point in time and is not a guarantee of future availability.
@@ -63,7 +63,7 @@ Accuracy corrections take priority over expansion. Authors and readers can submi
 When evidence conflicts:
 
 1. prefer the original publishing venue, official documentation, paper record, or repository over secondary coverage;
-1. distinguish a verified fact from the repository's interpretation;
+1. distinguish a verified fact from editorial interpretation;
 1. preserve material caveats, negative results, and stated evaluation limits;
 1. omit a date, venue, author, or metric rather than infer it from an unreliable source;
 1. record the correction in the next release notes when it changes the meaning of an entry.
@@ -74,7 +74,7 @@ When evidence conflicts:
 
 ## Annotation Rules
 
-Each annotation should answer: **why does this matter for Loop Engineering?**
+Each annotation should answer: **why does this matter for Loop Engineering?** Lead with the mechanism, result, use case, or limitation. Avoid self-referential openings such as "this paper explores" or "this page provides."
 
 Good:
 

meta/DISCUSSIONS.md

@@ -1,6 +1,6 @@
 # Discussion Guide
 
-GitHub Discussions are enabled for this repository. Use them to gather examples, improve definitions, and keep resource curation transparent.
+Use [GitHub Discussions](https://github.com/ChaoYue0307/awesome-loop-engineering/discussions) to share operating examples, challenge definitions, suggest evidence, and improve annotations in public.
 
 Seed thread: [Show your Loop Engineering patterns](https://github.com/ChaoYue0307/awesome-loop-engineering/discussions/2).
 
@@ -42,4 +42,4 @@ Examples: schedules, goals, hooks, worktrees, skills, plugins, subagents, workfl
 
 ## Moderation Standard
 
-Discussions should follow `CODE_OF_CONDUCT.md`, preserve the repository's narrow AI/coding-agent scope, and avoid unsupported claims. Promotional posts should include concrete technical details or they may be closed.
+Follow `CODE_OF_CONDUCT.md`, stay focused on recurring AI-agent and coding-agent systems, and support claims with inspectable evidence. Promotional posts need concrete technical detail or may be closed.

meta/MAINTENANCE.md

@@ -1,6 +1,6 @@
 # Maintenance Guide
 
-This guide keeps Awesome Loop Engineering useful, stable, and trustworthy over time. For the cadence of when to run each task, see the [maintenance calendar](MAINTENANCE_CALENDAR.md).
+Keep Awesome Loop Engineering accurate, stable, and trustworthy with the checks below. Use the [maintenance calendar](MAINTENANCE_CALENDAR.md) for the weekly, monthly, and release cadence.
 
 ## Weekly Checks
 

meta/README.md

@@ -1,6 +1,6 @@
 # Maintainer Docs
 
-Operational documents for running this repository. Visitors looking for Loop Engineering content should start at the [main README](../README.md).
+Maintain source quality, releases, discussions, and distribution with the operational guides below. Start with the [main field guide](../README.md) for Loop Engineering concepts and resources.
 
 - [Curation standard](CURATION.md) - How resources are accepted, rejected, and replaced.
 - [Maintenance guide](MAINTENANCE.md) - Weekly and monthly checks, link rot policy, and identity policy.

meta/RUNTIME_SELECTION.md

@@ -1,6 +1,6 @@
 # Runtime Selection Guide
 
-A loop is a design; a runtime is where it runs. The same [Loop Contract](../README.md#the-loop-contract) can run as a session-scoped command, a scheduled cloud job, a CI workflow, or a cron wrapper. This guide helps you pick deliberately.
+A loop is a design; a runtime is where it runs. Run the same [Loop Contract](../README.md#the-loop-contract) as a session-scoped command, scheduled cloud job, CI workflow, cron wrapper, or durable service. Choose by persistence, file access, isolation, permissions, verification, and escalation.
 
 Behavior of hosted products changes; treat the vendor-specific rows as a starting point and confirm current limits, triggers, and permissions in each product's official docs before relying on them unattended.
 

meta/hf_card_body.md

@@ -17,7 +17,7 @@
 
 ## Dataset Summary
 
-Each row represents one resource from the canonical English field guide. It pairs a concise editorial assessment with bibliographic metadata, lifecycle and audience facets, evidence classification, audit status, and point-in-time repository statistics when applicable.
+Each row pairs one source with a concise editorial assessment, bibliographic metadata, lifecycle and audience facets, evidence classification, audit status, and point-in-time repository statistics when applicable.
 
 Current release: **v{{VERSION}}**
 
@@ -71,7 +71,7 @@ Do not use `signal_strength`, GitHub stars, forks, or inclusion in this collecti
 
 ## Future Directions
 
-The project maintains a role-based agenda for moving Loop Engineering forward:
+Choose a role to find the highest-value open questions and build targets:
 
 | Audience | Priority categories |
 | --- | --- |
@@ -88,7 +88,7 @@ The primary configuration is `resources`, with one `train` split backed by `data
 | Field group | Fields | What it describes |
 | --- | --- | --- |
 | Identity | `row_id`, `title`, `url`, `canonical_url`, `resource_type`, `domain` | What the resource is and where it lives. |
-| Editorial assessment | `annotation`, `key_contribution`, `novelty`, `impact` | Why the resource matters in this collection. |
+| Editorial assessment | `annotation`, `key_contribution`, `novelty`, `impact` | What the resource contributes to recurring agent systems. |
 | Navigation | `section`, `collection`, `user_goal`, `lifecycle_stages`, `audience` | Where the resource fits and who it serves. |
 | Evidence | `evidence_class`, `signal`, `signal_strength`, `source_status`, `audited_at` | What kind of evidence or provenance is available. |
 | Publication | `authors`, `publication_date`, `publication_year`, `publication_venue`, `publisher`, `doi`, `arxiv_id`, `primary_category` | Bibliographic data exposed by the canonical source. |
@@ -100,7 +100,7 @@ The complete field-by-field schema is documented in [`data/README.md`](https://g
 
 ## Curation And Provenance
 
-The collection prioritizes primary papers, official documentation, project repositories, and implementation-heavy practitioner sources. Every released row must pass the public [curation standard](https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/meta/CURATION.md).
+Start with primary papers, official documentation, project repositories, and implementation-heavy practitioner sources. Every released row must pass the public [curation standard](https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/meta/CURATION.md).
 
 Annotations are original syntheses, not copied abstracts or claims of author endorsement. Automation may assist discovery, URL resolution, duplicate detection, bibliographic extraction, repository statistics, and draft normalization. The canonical source remains the evidence; the maintainer owns each released inclusion decision and annotation.
 

patterns/README.md

@@ -12,7 +12,7 @@ The layers have different jobs:
 
 | Layer | Question it answers | Artifact |
 | --- | --- | --- |
-| **Pattern** | How should this class of recurring work operate? | Human-readable playbook in this directory |
+| **Pattern** | How should this class of recurring work operate? | Human-readable operating playbook |
 | **Contract** | What exactly may this loop read, change, verify, spend, and escalate? | Schema-valid JSON in [`examples/`](../examples/README.md) |
 | **Runtime starter** | Where and how does the contract execute? | Copy/paste and executable starters in [`examples/runnable/`](../examples/runnable/README.md) |
 

posts/launch.md

@@ -1,10 +1,10 @@
 # Awesome Loop Engineering v0.7.0
 
-Awesome Loop Engineering v0.7.0 turns the implementation kit into a clear path from a recurring problem to a validated contract and a working runtime starter.
+Awesome Loop Engineering v0.7.0 connects a recurring problem to an operational pattern, a validated contract, and a working runtime starter.
 
-The collection now maps 545 sources across the operating layer that discovers work, delegates it, verifies results, persists state, and decides what happens next.
+Navigate 545 source-audited works across the operating layer that discovers work, delegates it, verifies results, persists state, and decides what happens next.
 
-## What Is Included
+## What You Can Use
 
 - 545 source-audited resources linked to canonical works
 - 20 operational patterns organized by build, operate, optimize, and govern use cases
@@ -27,7 +27,7 @@ The collection now maps 545 sources across the operating layer that discovers wo
 
 ## Why This Matters
 
-A resource list explains what exists. An implementation kit should also help a reader act.
+Evidence becomes useful when it leads to a reviewable implementation.
 
 The v0.7.0 path is explicit:
 
@@ -50,6 +50,6 @@ The goal remains bounded, reviewable, evidence-driven repetition, not unlimited
 
 Corrections are especially valuable. If a summary is inaccurate or a stronger canonical source exists, use the annotation-correction form or open a pull request.
 
-## Repository
+## Open The Field Guide
 
 [github.com/ChaoYue0307/awesome-loop-engineering](https://github.com/ChaoYue0307/awesome-loop-engineering)

posts/launch.zh-CN.md

@@ -2,9 +2,9 @@
 
 Awesome Loop Engineering v0.7.0 把 implementation kit 整理成一条清晰路径：从真实的重复性问题出发，选择 pattern，改造经校验的 contract，再接入可运行的 runtime starter。
 
-当前集合包含 545 条资源，覆盖 prompt、context 与 harness engineering 之上的运行层：负责发现工作、分派任务、验证结果、持久化状态并决定下一步的系统。
+可浏览 545 条经审核资源，理解 prompt、context 与 harness engineering 之上的运行层：如何发现工作、分派任务、验证结果、持久化状态并决定下一步。
 
-## 本次版本包含
+## 可直接使用
 
 - 545 条链接到 canonical source 的审阅资源
 - 20 个按 build、operate、optimize、govern 组织的 operational patterns
@@ -27,7 +27,7 @@ Awesome Loop Engineering v0.7.0 把 implementation kit 整理成一条清晰路
 
 ## 为什么值得关注
 
-资源列表说明“有什么”；implementation kit 还应回答“现在怎么做”。
+只有当证据能导向可审阅的实现时，它才真正有用。
 
 v0.7.0 的路径是：
 
@@ -48,8 +48,8 @@ v0.7.0 的路径是：
 - [使用 Hugging Face 数据集](https://huggingface.co/datasets/cy0307/awesome-loop-engineering)
 - [贡献资源或提交纠错](https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/CONTRIBUTING.md)
 
-如果仓库对某篇引用工作的概括不够准确，或存在更可靠的 canonical source，请使用 annotation-correction form 或提交 pull request。
+如果某条概括不够准确，或存在更可靠的 canonical source，请使用 annotation-correction form 或提交 pull request。
 
-## 仓库
+## 打开 Field Guide
 
 [github.com/ChaoYue0307/awesome-loop-engineering](https://github.com/ChaoYue0307/awesome-loop-engineering)

scripts/check_resource_labels.py

@@ -30,8 +30,8 @@ RESOURCE_SECTIONS = {
     "Examples And Schema",
     "Community Gallery",
     "Pattern Library",
-    "Discovery And Distribution",
-    "Roadmap And Discussion",
+    "Explore And Reuse",
+    "Shape What Comes Next",
     "Critiques, Risks, And Limitations",
     "Adjacent Awesome Lists",
 }

scripts/export_resource_dataset.py

@@ -163,8 +163,8 @@ COLLECTIONS = {
     "Examples And Schema": ("Apply", "Reuse, adapt, and contribute concrete loop artifacts."),
     "Community Gallery": ("Apply", "Reuse, adapt, and contribute concrete loop artifacts."),
     "Adjacent Awesome Lists": ("Apply", "Reuse, adapt, and contribute concrete loop artifacts."),
-    "Discovery And Distribution": ("Apply", "Reuse, adapt, and contribute concrete loop artifacts."),
-    "Roadmap And Discussion": ("Apply", "Reuse, adapt, and contribute concrete loop artifacts."),
+    "Explore And Reuse": ("Apply", "Reuse, adapt, and contribute concrete loop artifacts."),
+    "Shape What Comes Next": ("Apply", "Reuse, adapt, and contribute concrete loop artifacts."),
 }
 
 COLLECTION_IMPACT = {
@@ -228,7 +228,7 @@ OFFICIAL_DOC_DOMAINS = {
 }
 
 SECTION_IMPACT = {
-    "Concept Guides": "Clarifies the scope, vocabulary, and boundaries of Loop Engineering so the list does not drift into generic agent material.",
+    "Concept Guides": "Clarifies the scope, vocabulary, and boundaries of Loop Engineering for recurring, stateful, verified agent systems.",
     "Start Here": "Gives readers the origin story and first-principles framing for the new AI/coding-agent use of Loop Engineering.",
     "Core Loop Primitives": "Turns the concept into concrete loop mechanics: triggers, state, tools, worktrees, permissions, and recurring execution.",
     "Official Runtime Guides": "Anchors implementation choices in primary vendor and framework documentation instead of second-hand summaries.",
@@ -241,11 +241,11 @@ SECTION_IMPACT = {
     "Orchestration And Multi-Agent Delegation": "Maps the runtimes and coordination patterns used to split loop work across specialized agents and durable workflows.",
     "Benchmarks And Evaluation": "Provides measurement targets for long-horizon, tool-using, coding, web, and terminal agents.",
     "Operations Playbooks": "Collects practitioner workflows for running agents as delegated work systems rather than isolated prompts.",
-    "Templates And Patterns": "Provides reusable repository-native artifacts that contributors can adapt into loop specs, resources, and examples.",
+    "Templates And Patterns": "Provides reusable artifacts that builders can adapt into loop specs, resources, and examples.",
     "Examples And Schema": "Makes the loop contract executable and portable through validated JSON examples and runnable reference loops.",
     "Community Gallery": "Gives contributors a format for publishing real or anonymized loop cases with receipts and lessons learned.",
-    "Discovery And Distribution": "Documents how the project itself is packaged, indexed, mirrored, and made discoverable.",
-    "Roadmap And Discussion": "Keeps future work, community feedback, and pattern submissions visible.",
+    "Explore And Reuse": "Makes the evidence browsable, queryable, and reusable across interactive and machine-readable formats.",
+    "Shape What Comes Next": "Connects open questions, planned work, community feedback, and operating case studies.",
     "Pattern Library": "Translates the abstract loop contract into operational patterns with triggers, gates, budgets, and escalation paths.",
     "Critiques, Risks, And Limitations": "Preserves cautionary evidence so adoption stays proportional to task risk, signal quality, and economics.",
     "Adjacent Awesome Lists": "Connects readers to neighboring ecosystems while keeping Loop Engineering's scope distinct.",
@@ -263,11 +263,11 @@ SECTION_NOVELTY = {
     "Orchestration And Multi-Agent Delegation": "Shows how delegation, handoff, and workflow control turn one agent into a coordinated loop.",
     "Benchmarks And Evaluation": "Links loop design to measurable tasks where progress and failure can be compared.",
     "Operations Playbooks": "Translates agent-loop ideas into operator-facing workflows for repeated delegated work.",
-    "Templates And Patterns": "Provides reusable repository-native artifacts rather than leaving the concept as prose.",
+    "Templates And Patterns": "Turns the concept into reusable specifications, checklists, and operating patterns.",
     "Examples And Schema": "Makes loop contracts portable and validation-friendly through concrete examples.",
     "Community Gallery": "Turns loop adoption into shareable cases with enough structure to compare lessons learned.",
-    "Discovery And Distribution": "Makes the project discoverable as both documentation and machine-readable data.",
-    "Roadmap And Discussion": "Keeps community evolution and evidence gathering part of the project surface.",
+    "Explore And Reuse": "Turns the evidence into an interactive atlas and structured data.",
+    "Shape What Comes Next": "Turns open questions and operating lessons into visible next work.",
     "Pattern Library": "Turns common recurring-agent jobs into named patterns with gates, budgets, and escalation paths.",
     "Critiques, Risks, And Limitations": "Keeps adoption grounded in known failure modes, economics, and operational limits.",
     "Adjacent Awesome Lists": "Connects neighboring ecosystems while preserving Loop Engineering as a narrower operating concept.",
@@ -279,7 +279,7 @@ TYPE_SIGNAL = {
     "Tool": ("Working implementation, framework, runtime, or repository; signal comes from usable code and ecosystem adoption.", "high"),
     "Benchmark": ("Evaluation artifact or leaderboard; signal comes from measurable tasks and repeatable scoring.", "high"),
     "Pattern": ("Operational pattern or playbook; signal comes from reusable loop structure and practical transferability.", "medium"),
-    "Template": ("Repository-native template, schema, checklist, or guide; signal comes from reuse inside this project.", "medium"),
+    "Template": ("Reusable template, schema, checklist, or guide; signal comes from concrete adaptation and validation.", "medium"),
     "Blog": ("Practitioner essay or field note; signal comes from concrete experience, framing, examples, or adoption discussion.", "contextual"),
     "Critique": ("Risk or limitation analysis; signal comes from boundary conditions, failure modes, and adoption cautions.", "contextual"),
     "List": ("Adjacent curated collection; signal comes from ecosystem coverage rather than a single technical claim.", "contextual"),
@@ -287,10 +287,11 @@ TYPE_SIGNAL = {
 
 NOVELTY_RULES = [
     (r"\bofficial\b|\bprimary-source\b", "Primary-source operational guidance rather than commentary."),
+    (r"\bfuture directions?\b|\bresearch agenda\b", "Turns open gaps into measurable research, infrastructure, and product directions."),
     (r"\bdurable\b|\breplay\b", "Durable execution and replay are treated as first-class loop infrastructure."),
     (r"\bcheckpoint(?:ing|ed|s)?\b", "Checkpointed state makes long-running agent work recoverable across failures."),
     (r"\bworktree(?:s)?\b", "Workspace isolation is part of the loop design, not an afterthought."),
-    (r"\bdataset\b|\bresources\.csv\b|\bresources\.jsonl\b", "The list is made machine-readable as a tabular dataset rather than only a Markdown page."),
+    (r"\bdataset\b|\bresources\.csv\b|\bresources\.jsonl\b", "Packages the evidence as queryable CSV and JSONL rather than only a rendered page."),
     (r"\bdag(?:s)?\b|\bgraph(?:s)?\b", "Control flow is represented as an inspectable graph rather than an opaque prompt loop."),
     (r"\bschedule(?:d|s)?\b|\bscheduling\b|\bcadence\b", "The trigger or cadence is explicit, making the workflow recurring rather than one-off."),
     (r"\bself-verification\b|\bself-verifying\b", "The agent workflow includes explicit self-checking or gated completion."),
@@ -384,21 +385,21 @@ def key_contribution(annotation: str) -> str:
 
 def novelty(section: str, title: str, annotation: str) -> str:
     if section == "Concept Guides":
-        lens = "Repository-native artifact that makes an otherwise informal practice concrete and reusable."
-        return f"{lens} Resource-specific angle: {key_contribution(annotation)}"
+        lens = "Makes an otherwise informal practice concrete and reusable."
+        return f"{lens} {key_contribution(annotation)}"
 
     text = f"{title} {annotation}".lower()
     for pattern, phrase in NOVELTY_RULES:
         if re.search(pattern, text):
-            return f"{phrase} Resource-specific angle: {key_contribution(annotation)}"
+            return f"{phrase} {key_contribution(annotation)}"
 
     if section in {"Concept Guides", "Templates And Patterns", "Examples And Schema"}:
-        lens = "Repository-native artifact that makes an otherwise informal practice concrete and reusable."
+        lens = "Makes an otherwise informal practice concrete and reusable."
     elif section == "Research Foundations":
         lens = "Connects Loop Engineering to prior agent-loop and feedback-loop research."
     else:
         lens = SECTION_NOVELTY.get(section, "Contributes a distinct loop-engineering angle beyond a generic agent resource.")
-    return f"{lens} Resource-specific angle: {key_contribution(annotation)}"
+    return f"{lens} {key_contribution(annotation)}"
 
 
 def collection_for(section: str) -> tuple[str, str]:
@@ -455,7 +456,7 @@ def evidence_class(section: str, resource_type: str, domain: str, url_kind: str)
 
 def impact(collection: str, title: str) -> str:
     goal = COLLECTION_IMPACT.get(collection, "apply the source to a recurring agent system")
-    return f"Gives readers a concrete source in {title} when they need to {goal}."
+    return f"Use {title} to {goal}."
 
 
 def format_count(value: str) -> str:
@@ -476,7 +477,7 @@ def signal(
     if status in {"broken", "unreachable", "local_missing"}:
         return "The latest source audit could not verify this resource; treat its claims and availability as unverified.", "unverified"
     if url_kind != "external":
-        return "Repository-native artifact maintained and validated by this project's checks.", "medium"
+        return "Local artifact maintained with automated validation checks.", "medium"
     if evidence == "official-documentation":
         return f"Primary official documentation from {domain}; use it for current product or standard behavior.", "high"
     if evidence in {"research-preprint", "research-paper"}: