Tighten public copy and value proposition

CITATION.cff

@@ -9,7 +9,7 @@ authors:
     given-names: "Chaoyue"
 repository-code: "https://github.com/ChaoYue0307/awesome-loop-engineering"
 url: "https://chaoyue0307.github.io/awesome-loop-engineering/"
-abstract: "Curated resources and practical patterns for Loop Engineering with AI and coding agents."
+abstract: "Source-audited resources and reusable patterns for recurring, stateful, verified AI-agent systems."
 keywords:
   - loop-engineering
   - ai-agents

CONTRIBUTING.md

@@ -1,14 +1,14 @@
 # Contributing
 
-Thanks for helping improve Awesome Loop Engineering.
+Contribute a canonical source, an evidence-backed correction, or a loop you have run.
 
-This repository is intentionally narrow. It tracks the new AI and coding-agent meaning of **Loop Engineering**: the layer above prompt, context, and harness engineering where teams design systems that discover work, delegate to agents, verify outcomes, persist state, decide next actions, and run again on a cadence or until a goal is met.
+Scope is intentionally narrow: recurring AI-agent and coding-agent systems with explicit triggers, work intake, verification, durable state, budgets, escalation, and exit.
 
-Before submitting, read the [curation standard](meta/CURATION.md). Maintainers use it to decide whether a resource is specific, stable, and useful enough for the list.
+Before submitting, read the [curation standard](meta/CURATION.md). It defines scope, evidence tiers, annotation rules, and correction handling.
 
 ## Fastest Contribution (Under Five Minutes)
 
-If you are suggesting one resource and do not want to prepare a fork, use the [resource suggestion form](https://github.com/ChaoYue0307/awesome-loop-engineering/issues/new?template=resource-suggestion.yml). Include:
+Use the [resource suggestion form](https://github.com/ChaoYue0307/awesome-loop-engineering/issues/new?template=resource-suggestion.yml) to propose one source without preparing a fork. Include:
 
 - the canonical public URL;
 - the most specific README category;
@@ -16,9 +16,9 @@ If you are suggesting one resource and do not want to prepare a fork, use the [r
 - the source type and evidence tier;
 - any caveat, such as vendor bias, a paywall, early-stage status, or weak evidence.
 
-Maintainers will still apply the same scope, duplication, source-quality, and annotation checks used for pull requests. Corrections to an existing annotation are equally useful; link the current entry and state exactly what should change.
+Maintainers apply the same scope, duplication, source-quality, and annotation checks used for pull requests. Corrections are equally valuable: link the current entry and state the exact change.
 
-For an inaccurate summary, contribution, novelty, impact, author, date, venue, identifier, or canonical link, use the dedicated [annotation-correction form](https://github.com/ChaoYue0307/awesome-loop-engineering/issues/new?template=annotation-correction.yml). Include primary-source evidence and omit unavailable metadata rather than guessing.
+For an inaccurate summary, contribution, novelty, impact, authorship, date, venue, identifier, or canonical link, use the [annotation-correction form](https://github.com/ChaoYue0307/awesome-loop-engineering/issues/new?template=annotation-correction.yml). Include primary-source evidence; omit unavailable metadata rather than guessing.
 
 ## Quick PR Flow
 
@@ -85,7 +85,7 @@ Prefer higher tiers when two resources cover the same idea.
 | C | Curated survey or high-quality explainer | Taxonomy, comparison, tutorial |
 | D | Commentary or news coverage | Useful only when it documents origin, adoption, or debate |
 
-If two links say the same thing, prefer the more primary, practical, stable, and implementation-oriented one.
+If two links cover the same idea, prefer the more primary, stable, and implementation-specific source.
 
 ## Quality Labels
 

DEFINITION.md

@@ -1,6 +1,6 @@
 # Canonical Definition
 
-Use this page when you need a short, stable definition of Loop Engineering.
+Use this definition to scope a system, review a design, or cite the concept consistently.
 
 ## Short Definition
 
@@ -12,11 +12,11 @@ Prompt engineering improves what you ask the model, context engineering improves
 
 ## Longer Definition
 
-Loop Engineering sits above prompt, context, and harness engineering. It turns agent work from turn-by-turn human prompting into a reviewable operating contract: what starts the loop, where work comes from, which agents act, what context and tools they receive, how results are verified, what state survives, when retry is allowed, and when a human takes over.
+Loop Engineering connects prompt, context, and harness decisions across runs. It replaces turn-by-turn human steering with a reviewable operating contract: what starts the loop, where work comes from, which agents act, what they can access, how evidence gates results, what state survives, how retries are bounded, and when a human takes over.
 
 ## Minimal Loop Test
 
-A system is probably practicing Loop Engineering when it can answer:
+A system qualifies when it can answer:
 
 1. What triggers the loop?
 1. How does it discover or receive work?
@@ -30,4 +30,4 @@ A system is probably practicing Loop Engineering when it can answer:
 
 ## Citation Note
 
-If you cite this repository, use the BibTeX entry in [`README.md`](README.md#citation) or [`CITATION.bib`](CITATION.bib). If you quote the concept definition, prefer linking to this page so readers can see the scope boundary.
+Use the BibTeX entry in [`README.md`](README.md#citation) or [`CITATION.bib`](CITATION.bib). Link to this page when quoting the definition so readers can inspect the scope boundary.

MANIFESTO.md

@@ -1,24 +1,24 @@
 # Loop Engineering Manifesto
 
-Loop Engineering is the practice of designing recurring AI-agent systems that sit above prompt, context, and harness engineering.
+Loop Engineering is the practice of designing recurring AI-agent systems whose behavior remains reviewable across runs.
 
-Prompt engineering improves what you ask the model. Context engineering improves what the model can see. Harness engineering improves the tools, permissions, sandboxes, and checks around one agent run. Loop Engineering connects those layers over time: it defines when agents run, what work they discover, how work is delegated, how results are verified, what state survives, and when the system repeats, reports, or escalates.
+Prompt engineering shapes the instruction. Context engineering shapes what the model can see. Harness engineering shapes one run's tools, permissions, isolation, and checks. Loop Engineering connects those decisions across runs: when agents start, what work enters, who acts, what evidence gates results, what state survives, and when the system repeats, reports, escalates, or stops.
 
 ## Why It Exists
 
-The leverage point in agent work is moving from writing better one-off prompts to designing better recurring systems. A useful loop can notice work, load the right context, act in an isolated workspace, verify against explicit gates, record receipts, and continue later without relying on a human to remember every intermediate step.
+Recurring agent work shifts the design problem from the next prompt to the operating system around every run. A useful loop finds work, loads bounded context, acts in isolation, verifies against explicit gates, records receipts, and resumes without relying on human memory.
 
-This does not remove engineering judgment. It moves judgment into the loop contract: objective, trigger, intake, workspace, context, delegation, verification, state, budget, escalation, and exit.
+This does not remove engineering judgment. It makes judgment explicit in the loop contract: objective, trigger, intake, workspace, context, delegation, verification, state, budget, escalation, and exit.
 
 ## Core Commitments
 
-- **Design the system, not just the next prompt.** The human should specify the operating contract, not manually steer every turn.
+- **Design the system, not just the next prompt.** Specify the operating contract instead of steering every turn.
 - **Make state external.** Progress files, issue comments, traces, checkpoints, and dashboards should outlive the model context.
-- **Separate maker from checker.** The system that acts should not be the only system that decides whether the work is done.
+- **Separate maker from checker.** The actor must not be the sole judge of completion.
 - **Prefer deterministic gates.** Tests, typechecks, evals, dashboards, trace graders, and reviewer decisions are stronger than "looks good".
 - **Bound autonomy.** Loops need budgets, allowed actions, disallowed actions, stop conditions, and escalation paths.
 - **Keep receipts.** A loop should explain what it saw, what it changed, what it ran, what passed, what failed, and why it stopped.
-- **Stay responsible.** A loop can delegate work, but ownership of quality, safety, and product judgment remains human.
+- **Keep ownership human.** A loop can delegate work; quality, safety, and product judgment remain human responsibilities.
 
 ## What It Is Not
 
@@ -39,4 +39,4 @@ A Loop Engineering artifact is useful when another builder can answer:
 1. When does it escalate?
 1. When is it done?
 
-If those answers are visible, the loop can be reviewed, shared, improved, and trusted.
+Visible answers make the loop reviewable, portable, and improvable.

README.md

@@ -38,7 +38,7 @@ configs:
 <h1 align="center">Awesome Loop Engineering Dataset</h1>
 
 <p align="center">
-  A source-audited sheet of 540 papers, official docs, tools, benchmarks, patterns, critiques, and implementation guides for recurring AI-agent systems.
+  A source-audited dataset of 540 papers, official docs, tools, benchmarks, patterns, critiques, and implementation guides for recurring AI-agent systems.
 </p>
 
 <p align="center">
@@ -50,13 +50,13 @@ configs:
 
 ## Dataset Summary
 
-Each row represents one resource from the canonical English field guide. The export combines the maintainer's short assessment with source-level bibliographic metadata, lifecycle and audience facets, evidence classification, audit status, and current repository statistics when applicable.
+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.
 
 Current release: **v0.6.0**
 
 | Surface | Count |
 | --- | ---: |
-| Curated resources | 540 |
+| Source-audited resources | 540 |
 | Operational patterns | 15 |
 | Validated loop contracts | 15 |
 | Runnable templates | 6 |
@@ -94,11 +94,11 @@ Use `url` as the durable join key. `row_id` and `source_line` are positional and
 
 ## Intended Uses
 
-- discover primary sources and implementation references for recurring agent systems;
-- compare resources by lifecycle stage, audience, evidence class, source type, and publication metadata;
-- build literature maps, reading lists, dashboards, or retrieval indexes;
-- audit how a public field guide characterizes contribution, novelty, impact, and evidence;
-- find reusable patterns, contracts, schemas, and runnable examples.
+- Find primary sources and implementation references for recurring agent systems.
+- Compare works by lifecycle, audience, evidence class, source type, and publication metadata.
+- Build literature maps, reading lists, dashboards, or retrieval indexes.
+- Audit contribution, novelty, impact, provenance, and evidence claims.
+- Find reusable patterns, contracts, schemas, and runnable examples.
 
 Do not use `signal_strength`, GitHub stars, forks, or inclusion in this collection as a quality label, endorsement, or automated ranking of scientific validity.
 
@@ -123,7 +123,7 @@ The complete field-by-field schema is documented in [`data/README.md`](https://g
 
 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).
 
-Annotations are original repository syntheses, not copied abstracts and not 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, and the maintainer is accountable for the released inclusion decision and text.
+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.
 
 The full point-in-time audit is available as [`data/resource_source_audit.csv`](https://huggingface.co/datasets/cy0307/awesome-loop-engineering/blob/main/data/resource_source_audit.csv).
 
@@ -139,7 +139,7 @@ The full point-in-time audit is available as [`data/resource_source_audit.csv`](
 
 ## Versioning And Corrections
 
-GitHub Releases define versioned snapshots. Cite a release or commit when reproducibility matters. Accuracy corrections to summaries, contribution, novelty, impact, author, date, venue, identifier, or canonical URL are accepted through the [correction form](https://github.com/ChaoYue0307/awesome-loop-engineering/issues/new?template=annotation-correction.yml).
+GitHub Releases define versioned snapshots; cite a release or commit for reproducibility. Submit corrections to summaries, contribution, novelty, impact, authorship, dates, venues, identifiers, or canonical URLs through the [correction form](https://github.com/ChaoYue0307/awesome-loop-engineering/issues/new?template=annotation-correction.yml).
 
 ## Citation
 

ROADMAP.md

@@ -1,6 +1,6 @@
 # Roadmap
 
-This roadmap keeps Awesome Loop Engineering focused on useful, verifiable work for an emerging practice.
+The roadmap prioritizes stronger evidence, runnable implementations, and real operating results.
 
 ## Near Term
 
@@ -8,8 +8,8 @@ This roadmap keeps Awesome Loop Engineering focused on useful, verifiable work f
 - Add real or anonymized gallery entries from practitioners running recurring agent loops.
 - Grow the runnable loop directory beyond the test-repair reference loop, including scheduled-trigger variants per runtime.
 - Add more translations for the introduction, mental model, Loop Contract, and contribution guide.
-- Audit contextual sources in small batches and replace weak summaries or secondary links with stronger canonical evidence.
-- Continue replacing weak or unstable links with primary sources, official docs, papers, and implementation-heavy write-ups.
+- Audit contextual sources in small batches; replace weak summaries and secondary links with canonical evidence.
+- Replace unstable links with primary sources, official docs, papers, or implementation-heavy write-ups.
 
 ## Pattern Library
 

data/README.md

@@ -1,6 +1,6 @@
 # Dataset Exports
 
-This directory contains generated tabular exports of the canonical English `README.md` resource list.
+This directory contains deterministic tabular exports of the canonical English resource list.
 
 - `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).
@@ -8,13 +8,13 @@ This directory contains generated tabular exports of the canonical English `READ
 - `first_seen.json` - Forward-only sidecar mapping resource URL to the date it was first added; the export left-joins it into the `date_added` column. Empty `date_added` means the entry predates per-entry tracking (started 2026-07-15).
 - `resource_source_audit.csv` - Retrieval-time audit of every row, including URL status, source title metadata, arXiv IDs, and GitHub repository stats where available.
 
-The main exports preserve the original section and annotation while adding three complementary discovery layers:
+The exports preserve each section and annotation while adding three discovery layers:
 
 - **Task facets**: `collection`, `user_goal`, `lifecycle_stages`, and `audience` answer why a reader needs the source and where it fits in the Loop Contract.
 - **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 its limits; GitHub stars and forks are reported as current context, never as proof of reliability. Signal strength is calibrated as `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 source 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 repository-native artifacts, `contextual` for practitioner analysis and curated lists, and `unverified` only when the latest audit cannot validate availability.
 
 ## Load And Query
 

docs/index.html

@@ -4,7 +4,7 @@
     <meta charset="utf-8">
     <meta name="viewport" content="width=device-width, initial-scale=1">
     <title>Awesome Loop Engineering</title>
-    <meta name="description" content="540 audited resources, 15 operational patterns, loop contracts, and runnable examples for recurring, verified AI-agent systems.">
+    <meta name="description" content="540 source-audited resources, 15 operational patterns, validated contracts, and runnable templates for recurring AI-agent systems.">
     <meta name="robots" content="index,follow">
     <meta name="author" content="Chaoyue He">
     <link rel="canonical" href="https://chaoyue0307.github.io/awesome-loop-engineering/">
@@ -15,7 +15,7 @@
     <link href="https://fonts.googleapis.com/css2?family=IBM+Plex+Mono:wght@500;600&amp;family=Inter:wght@400;500;600;700;800&amp;display=swap" rel="stylesheet">
     <meta property="og:type" content="website">
     <meta property="og:title" content="Awesome Loop Engineering">
-    <meta property="og:description" content="Explore 540 audited resources, 15 operational patterns, reusable loop contracts, and runnable examples for recurring AI-agent systems.">
+    <meta property="og:description" content="Explore 540 source-audited resources, 15 operational patterns, validated contracts, and runnable templates for recurring AI-agent systems.">
     <meta property="og:url" content="https://chaoyue0307.github.io/awesome-loop-engineering/">
     <meta property="og:image" content="https://chaoyue0307.github.io/awesome-loop-engineering/assets/social-preview.png?v=4">
     <meta property="og:image:width" content="1280">
@@ -23,7 +23,7 @@
     <meta property="og:image:alt" content="Awesome Loop Engineering: design loops, not just prompts.">
     <meta name="twitter:card" content="summary_large_image">
     <meta name="twitter:title" content="Awesome Loop Engineering">
-    <meta name="twitter:description" content="540 audited resources and reusable patterns for designing recurring AI-agent systems above prompt, context, and harness engineering.">
+    <meta name="twitter:description" content="540 source-audited resources and reusable operating patterns for recurring, stateful, verified AI-agent systems.">
     <meta name="twitter:image" content="https://chaoyue0307.github.io/awesome-loop-engineering/assets/social-preview.png?v=4">
     <link rel="alternate" type="application/atom+xml" title="Awesome Loop Engineering releases" href="https://github.com/ChaoYue0307/awesome-loop-engineering/releases.atom">
     <link rel="alternate" type="application/atom+xml" title="Awesome Loop Engineering commits" href="https://github.com/ChaoYue0307/awesome-loop-engineering/commits/main.atom">
@@ -778,7 +778,7 @@
         "@type": "CollectionPage",
         "name": "Awesome Loop Engineering",
         "url": "https://chaoyue0307.github.io/awesome-loop-engineering/",
-        "description": "A curated field guide to Loop Engineering: designing recurring AI-agent systems above prompt, context, and harness engineering.",
+        "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",
@@ -800,7 +800,7 @@
           {
             "@type": "Question",
             "name": "What is loop engineering?",
-            "acceptedAnswer": { "@type": "Answer", "text": "Loop Engineering is the practice of designing recurring AI agent and coding-agent systems that discover work, delegate it, verify results, persist state, decide next actions, and run again." }
+            "acceptedAnswer": { "@type": "Answer", "text": "Loop Engineering is the practice of designing recurring AI-agent and coding-agent systems that discover work, delegate it, verify results, persist state, decide next actions, and run again." }
           },
           {
             "@type": "Question",
@@ -904,7 +904,7 @@
             <p class="section-label hero-kicker"><span class="emoji" aria-hidden="true">🔁</span><span>The field guide for recurring AI-agent systems</span></p>
             <h1>Awesome Loop Engineering</h1>
             <p class="tagline">Design loops, not just prompts.</p>
-            <p class="lead">Build the recurring system around the agent: discover work, delegate it, verify the result, persist state, and decide what happens next.</p>
+            <p class="lead">540 source-audited resources, 15 operational patterns, and reusable contracts for agent systems that must keep working.</p>
             <div class="actions">
               <a class="button primary" href="https://github.com/ChaoYue0307/awesome-loop-engineering#readme"><span class="emoji" aria-hidden="true">📚</span>Explore the list</a>
               <a class="button" href="#choose"><span class="emoji" aria-hidden="true">🧭</span>Choose a loop</a>
@@ -954,7 +954,7 @@
             </picture>
           </figure>
           <div class="stats" role="list" aria-label="Repository at a glance">
-            <div class="stat" role="listitem"><b><span class="emoji" aria-hidden="true">📚</span>540</b><span>curated entries</span></div>
+            <div class="stat" role="listitem"><b><span class="emoji" aria-hidden="true">📚</span>540</b><span>audited resources</span></div>
             <div class="stat" role="listitem"><b><span class="emoji" aria-hidden="true">🧩</span>15</b><span>operational patterns</span></div>
             <div class="stat" role="listitem"><b><span class="emoji" aria-hidden="true">🧾</span>15</b><span>loop contracts</span></div>
             <div class="stat" role="listitem"><b><span class="emoji" aria-hidden="true">▶️</span>6</b><span>runnable templates</span></div>
@@ -970,7 +970,7 @@
               <p class="section-label"><span class="emoji" aria-hidden="true">🧾</span><span>01 / The loop contract</span></p>
               <h2>Every reliable loop names the same parts.</h2>
             </div>
-            <p class="section-intro">The contract turns a recurring prompt habit into a reviewable operating system. Use the <a href="https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/schemas/loop-contract.schema.json">JSON schema</a> and <a href="https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/examples/README.md">15 validated examples</a> to make each decision explicit.</p>
+            <p class="section-intro">A loop contract makes triggers, permissions, verification, state, budgets, and human handoff reviewable. Start with the <a href="https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/schemas/loop-contract.schema.json">JSON schema</a> or one of <a href="https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/examples/README.md">15 validated examples</a>.</p>
           </div>
           <figure class="diagram desktop-diagram">
             <div class="diagram-scroll"><img src="assets/loop-contract-cards.svg" width="1200" height="720" loading="lazy" alt="Loop Contract: objective, trigger, intake, workspace, context, delegation, verification, state, budget, escalation, exit, and next action."></div>
@@ -1020,7 +1020,7 @@
               <p class="section-label"><span class="emoji" aria-hidden="true">🔁</span><span>02 / The lifecycle</span></p>
               <h2>Evidence moves the loop forward.</h2>
             </div>
-            <p class="section-intro">A deterministic gate checks the work. The result and its receipts persist outside the model. The next action is retry, escalate, or exit.</p>
+            <p class="section-intro">Tests, evals, traces, or reviewers gate the work. Evidence and state persist outside the model, then drive retry, escalation, or exit.</p>
           </div>
           <figure class="diagram desktop-diagram">
             <div class="diagram-scroll"><img src="assets/loop-lifecycle.svg" width="1240" height="560" loading="lazy" alt="Loop Engineering lifecycle: Intake, Delegate, Act, Verify, Persist, Decide; then retry with evidence, escalate to a human, or exit when the goal is met."></div>
@@ -1130,17 +1130,17 @@
         <div class="wrap">
           <div class="section-head">
             <div>
-              <p class="section-label"><span class="emoji" aria-hidden="true">📚</span><span>06 / Curated resources</span></p>
-              <h2>Find the evidence for your next decision.</h2>
+              <p class="section-label"><span class="emoji" aria-hidden="true">📚</span><span>06 / Resource atlas</span></p>
+              <h2>Find evidence, not just links.</h2>
             </div>
-            <p class="section-intro">Search 540 audited works by goal, lifecycle stage, artifact type, and evidence class. Each row combines the curated assessment with verified authorship, year, venue or source platform, and identifiers when the primary source exposes them.</p>
+            <p class="section-intro">Filter 540 source-audited works by goal, lifecycle, artifact type, and evidence class. Open any result for contribution, novelty, impact, provenance, and source metadata.</p>
           </div>
           <p class="audit-note"><strong>Audit snapshot, 2026-07-17:</strong> all 540 rows checked; 493 public sources reachable, 4 access-restricted, 43 repository-native, and 0 broken or unreachable. <a href="https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/data/resource_source_audit.csv">Inspect the audit</a> or <a href="https://github.com/ChaoYue0307/awesome-loop-engineering/issues/new?template=annotation-correction.yml">report a correction</a>.</p>
           <div class="resource-explorer" id="resource-explorer">
-            <div class="resource-toolbar" role="search" aria-label="Search curated resources">
+            <div class="resource-toolbar" role="search" aria-label="Search resources">
               <label>
                 <span class="sr-only">Search resources</span>
-                <input class="resource-input" id="resource-search" type="search" placeholder="Search 540 audited resources" autocomplete="off">
+                <input class="resource-input" id="resource-search" type="search" placeholder="Search title, author, or topic" autocomplete="off">
               </label>
               <label>
                 <span class="sr-only">Resource type</span>
@@ -1236,9 +1236,9 @@
           <div class="section-head">
             <div>
               <p class="section-label"><span class="emoji" aria-hidden="true">🤝</span><span>07 / Contribute</span></p>
-              <h2>Bring a source or a real loop.</h2>
+              <h2>Improve the evidence or share a loop.</h2>
             </div>
-            <p class="section-intro">Add a vetted source with a specific annotation, or document a loop you actually run. Both paths have a checklist.</p>
+            <p class="section-intro">Submit one canonical source, correct an existing record with primary evidence, or document a loop you have run.</p>
           </div>
           <div class="link-grid contribute-grid">
             <a class="card" href="https://github.com/ChaoYue0307/awesome-loop-engineering/issues/new?template=resource-suggestion.yml"><h3><span class="emoji" aria-hidden="true">➕</span>Suggest a resource</h3><p>Submit one canonical source and a precise annotation in under five minutes.</p></a>
@@ -1254,13 +1254,13 @@
           <div class="section-head">
             <div>
               <p class="section-label"><span class="emoji" aria-hidden="true">💬</span><span>08 / FAQ</span></p>
-              <h2>Loop Engineering, answered.</h2>
+              <h2>Questions that define the boundary.</h2>
             </div>
-            <p class="section-intro">Short answers to the questions people ask first.</p>
+            <p class="section-intro">Use these tests to decide whether a recurring agent system needs a loop.</p>
           </div>
           <details open>
             <summary>What is loop engineering?</summary>
-            <p>Loop Engineering is the practice of designing recurring AI agent and coding-agent systems that discover work, delegate it, verify the result, persist state outside the model, decide what happens next, and run again.</p>
+            <p>Loop Engineering is the practice of designing recurring AI-agent and coding-agent systems that discover work, delegate it, verify results, persist state outside the model, decide what happens next, and run again.</p>
           </details>
           <details>
             <summary>How is it different from prompt, context, and harness engineering?</summary>
@@ -1328,7 +1328,7 @@
             <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 an early curated 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 keep their own licenses.</p>
+        <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>
       </div>
     </footer>
 

examples/README.md

@@ -1,6 +1,6 @@
 # Loop Examples
 
-These examples show how to turn the abstract loop contract into concrete operating specs.
+These examples turn the Loop Contract into validated operating specifications.
 
 ## Contract Library
 
@@ -22,11 +22,11 @@ Every loop in the [pattern library](../patterns/README.md) has a matching contra
 - [`release-note-loop.json`](release-note-loop.json)
 - [`model-routing-loop.json`](model-routing-loop.json)
 
-Use them as starting points. Adapt triggers, tools, checks, state artifacts, and escalation rules to your repository and runtime.
+Adapt each contract's triggers, tools, checks, state artifacts, and escalation rules to your repository and runtime.
 
 ## Runnable Loops
 
-The [runnable directory](runnable/README.md) contains working loop scripts, starting with a dependency-light test-repair loop you can drive with Claude Code, Codex CLI, or any agent CLI.
+The [runnable directory](runnable/README.md) contains working scripts, including a dependency-light test-repair loop for Claude Code, Codex CLI, or another agent CLI.
 
 ## PR Babysitter
 

meta/CURATION.md

@@ -1,14 +1,14 @@
 # Curation Standard
 
-This repository is intentionally selective. It should help builders understand and practice Loop Engineering for AI and coding agents as the layer above prompt, context, and harness engineering, not become a general AI-agent link dump.
+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.
 
 ## Acceptance Test
 
 A resource belongs when it passes all three checks:
 
-1. **Scope fit**: It is about AI/coding-agent loops, or a direct foundation for designing repeated agent runs that discover work, delegate to agents, coordinate context and harnesses, verify results, persist state, decide next actions, and escalate.
+1. **Scope fit**: It covers AI/coding-agent loops or a direct foundation for repeated runs that discover work, delegate, verify, persist state, decide next actions, and escalate.
 1. **Builder value**: It helps someone design, run, verify, evaluate, operate, or critique recurring agent systems.
-1. **Stable evidence**: It is public, specific, and stable enough that readers can inspect it later.
+1. **Inspectable evidence**: It is public, specific, and stable enough for readers to verify.
 
 ## Strong Signals
 
@@ -18,7 +18,7 @@ Prefer resources with one or more of these properties:
 - Official docs for an agent runtime, SDK, workflow system, benchmark, or eval framework.
 - Concrete implementation detail: commands, architecture, traces, code, loop instructions, automation configs, hooks, schedules, state files, worktrees, checks, or failure modes.
 - Durable research foundation: ReAct, reflection, self-correction, planning, memory, tool use, evaluation, or state.
-- Practical operational value: CI repair, PR babysitting, deploy verification, docs drift, feedback clustering, cost control, or escalation.
+- Operational value: CI repair, PR babysitting, deploy verification, docs drift, feedback clustering, cost control, or escalation.
 
 ## Weak Signals
 
@@ -45,7 +45,7 @@ Prefer the highest-tier source that explains the same idea clearly.
 
 ## Editorial Responsibility And Assistance
 
-The maintainer is accountable for every released inclusion decision, category, annotation, and project-level claim. Repository automation may assist with discovery, URL resolution, duplicate detection, bibliographic extraction, repository statistics, and draft normalization. Automated output is never treated as evidence on its own: the canonical source record, primary metadata, and stated limitations control the final entry.
+The maintainer owns every released inclusion decision, category, annotation, and project-level claim. Automation may assist discovery, URL resolution, duplicate detection, bibliographic extraction, repository statistics, and draft normalization. Automated output is not evidence; canonical sources, primary metadata, and stated limitations control the final entry.
 
 The public dataset keeps those layers separate:
 

meta/DISTRIBUTION.md

@@ -1,6 +1,6 @@
 # Distribution And Discovery Checklist
 
-Use this checklist after major updates or releases. The goal is accurate discovery, not hype.
+Use this checklist after major updates or releases. Optimize for qualified discovery, accurate claims, and useful follow-through.
 
 ## Current Launch Status
 
@@ -29,7 +29,7 @@ Search engines may still take days or longer to index a new repository. Backlink
 
 ## Hugging Face Dataset Mirror
 
-The repository is mirrored to the Hugging Face Hub dataset repo [`datasets/cy0307/awesome-loop-engineering`](https://huggingface.co/datasets/cy0307/awesome-loop-engineering) for discovery within the AI/ML community. The daily maintenance routine syncs it after each GitHub push.
+The Hugging Face dataset [`datasets/cy0307/awesome-loop-engineering`](https://huggingface.co/datasets/cy0307/awesome-loop-engineering) exposes the collection to AI/ML users and tabular workflows. Sync it after each GitHub release or data change.
 
 - The mirror tracks the full GitHub tree (docs, patterns, examples, schema, scripts).
 - The dataset mirror includes generated resource sheets at `data/resources.csv` and `data/resources.jsonl`, refreshed from the canonical English `README.md` by `scripts/export_resource_dataset.py`.
@@ -39,14 +39,14 @@ The repository is mirrored to the Hugging Face Hub dataset repo [`datasets/cy030
 
 ## GitHub-Native Promotion
 
-Discoverability levers that live on GitHub itself, in priority order.
+Use GitHub-native discovery in this order.
 
 - **Topic page (`github/explore`).** The [`loop-engineering` topic](https://github.com/topics/loop-engineering) now has a curated page through merged PR [`github/explore#5209`](https://github.com/github/explore/pull/5209). Keep the topic description ecosystem-wide rather than repository-specific.
 - **sindresorhus/awesome submission.** The open PR at [`sindresorhus/awesome#4339`](https://github.com/sindresorhus/awesome/pull/4339) has a passing lint check and an explicit owner policy objection to AI-generated lists. Keep the curation process transparent and wait for maintainer clarification; do not present the PR as ordinary pending review. See [`AWESOME_SUBMISSION.md`](AWESOME_SUBMISSION.md).
 - **Editorial submissions to adjacent lists** this repo already cites (harness, context, and agent-paper lists). Submit only where the maintainers explicitly accept related resources. Ready-to-paste entry:
 
   ```md
-  - [Awesome Loop Engineering](https://github.com/ChaoYue0307/awesome-loop-engineering#readme) - Recurring AI/coding-agent systems above prompt, context, and harness engineering: patterns, contracts, runnable loops, and curated resources.
+  - [Awesome Loop Engineering](https://github.com/ChaoYue0307/awesome-loop-engineering#readme) - Source-audited resources, reusable patterns, validated contracts, and runnable templates for recurring AI-agent systems.
   ```
 
 - **Pin the repository** on your profile, and add it to your profile README (profile-level promotion).
@@ -103,11 +103,11 @@ https://chaoyue0307.github.io/awesome-loop-engineering/
 ```text
 Awesome Loop Engineering v0.6.0 is live.
 
-Loop Engineering is the operating layer above prompt, context, and harness engineering: the recurring system that discovers work, delegates it, verifies results, persists state, and decides what happens next.
+Loop Engineering governs what happens across agent runs: how work enters, agents act, evidence decides, state survives, and the system continues.
 
 The release includes:
 
-- 540 audited resources
+- 540 source-audited resources
 - an interactive Resource Atlas
 - 15 operational patterns and 15 validated loop contracts
 - 6 runnable templates
@@ -125,9 +125,9 @@ Use a regular submission, not Show HN.
 ```text
 Title: Awesome Loop Engineering: 540 resources for recurring AI-agent systems
 
-I have been mapping the layer above prompt, context, and harness engineering: recurring systems that discover work, delegate it, verify results, persist state, and decide whether to retry, escalate, or exit.
+I have been mapping recurring AI-agent systems: how work enters, agents act, evidence gates results, state survives, and the system retries, escalates, or exits.
 
-The repository now includes 540 audited sources, 15 operational patterns, validated loop contracts, runnable templates, an interactive Resource Atlas, and a structured Hugging Face dataset. The curation and source-audit method is public, and corrections to the taxonomy and source annotations are especially welcome.
+The repository includes 540 source-audited works, 15 operational patterns, validated loop contracts, runnable templates, an interactive Resource Atlas, and a structured Hugging Face dataset. The curation and audit methods are public; corrections to the taxonomy and annotations are especially welcome.
 
 https://github.com/ChaoYue0307/awesome-loop-engineering
 ```

meta/OUTREACH.md

@@ -1,6 +1,6 @@
 # Outreach Kit
 
-Use this when inviting authors, practitioners, or tool builders to improve the repository.
+Use these messages to request sources, operating examples, and accuracy corrections without asking for promotion.
 
 ## Who To Invite
 
@@ -12,9 +12,9 @@ Use this when inviting authors, practitioners, or tool builders to improve the r
 ## Short Message
 
 ```text
-Hi <name>, this repository is collecting high-signal resources and practical patterns for Loop Engineering: designing recurring AI-agent systems above prompt, context, and harness engineering.
+Hi <name>, Awesome Loop Engineering collects source-audited resources and reusable patterns for recurring AI-agent systems.
 
-If you have a source, correction, real-world loop pattern, or cautionary example that should be included, contributions are welcome:
+If you have a canonical source, correction, real-world loop, or cautionary example, you can contribute here:
 https://github.com/ChaoYue0307/awesome-loop-engineering
 ```
 
@@ -23,7 +23,7 @@ https://github.com/ChaoYue0307/awesome-loop-engineering
 ```text
 Hi <name>, do you have a public example of an agent loop you can share?
 
-The repo is looking for examples with a clear trigger, work intake, agent roles, verification gates, durable state, retry budget, escalation path, and receipts. An anonymized version is fine if the implementation cannot be public.
+Useful examples name the trigger, intake, agent roles, verification gates, durable state, retry budget, escalation path, and receipts. An anonymized version is welcome when the implementation cannot be public.
 ```
 
 ## Accuracy Request
@@ -31,14 +31,14 @@ The repo is looking for examples with a clear trigger, work intake, agent roles,
 ```text
 Hi <name>, I included <work title> in Awesome Loop Engineering and summarized its contribution as: "<current annotation>."
 
-Could you check whether that characterization is accurate? If it misses the main contribution, novelty, impact, or best canonical link, a correction is very welcome:
+Could you check whether that characterization is accurate? If it misses the main contribution, novelty, impact, or canonical link, please submit a correction:
 https://github.com/ChaoYue0307/awesome-loop-engineering/issues/new?template=annotation-correction.yml
 ```
 
 ## Maintainer Request
 
 ```text
-Hi <name>, Awesome Loop Engineering is a curated map of recurring AI-agent systems above prompt, context, and harness engineering. It includes audited resources, operational patterns, loop contracts, runnable templates, and a structured dataset.
+Hi <name>, Awesome Loop Engineering is a source-audited field guide and implementation kit for recurring AI-agent systems. It includes 540 resources, 15 operational patterns, validated contracts, runnable templates, and a structured dataset.
 
 Would it fit the related-resources policy for <project or newsletter>? I can provide a shorter description or submit through the normal contribution process:
 https://github.com/ChaoYue0307/awesome-loop-engineering

meta/hf_card_body.md

@@ -5,7 +5,7 @@
 <h1 align="center">Awesome Loop Engineering Dataset</h1>
 
 <p align="center">
-  A source-audited sheet of {{RESOURCE_COUNT}} papers, official docs, tools, benchmarks, patterns, critiques, and implementation guides for recurring AI-agent systems.
+  A source-audited dataset of {{RESOURCE_COUNT}} papers, official docs, tools, benchmarks, patterns, critiques, and implementation guides for recurring AI-agent systems.
 </p>
 
 <p align="center">
@@ -17,13 +17,13 @@
 
 ## Dataset Summary
 
-Each row represents one resource from the canonical English field guide. The export combines the maintainer's short assessment with source-level bibliographic metadata, lifecycle and audience facets, evidence classification, audit status, and current repository statistics when applicable.
+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.
 
 Current release: **v{{VERSION}}**
 
 | Surface | Count |
 | --- | ---: |
-| Curated resources | {{RESOURCE_COUNT}} |
+| Source-audited resources | {{RESOURCE_COUNT}} |
 | Operational patterns | {{PATTERN_COUNT}} |
 | Validated loop contracts | {{CONTRACT_COUNT}} |
 | Runnable templates | {{RUNNABLE_COUNT}} |
@@ -61,11 +61,11 @@ Use `url` as the durable join key. `row_id` and `source_line` are positional and
 
 ## Intended Uses
 
-- discover primary sources and implementation references for recurring agent systems;
-- compare resources by lifecycle stage, audience, evidence class, source type, and publication metadata;
-- build literature maps, reading lists, dashboards, or retrieval indexes;
-- audit how a public field guide characterizes contribution, novelty, impact, and evidence;
-- find reusable patterns, contracts, schemas, and runnable examples.
+- Find primary sources and implementation references for recurring agent systems.
+- Compare works by lifecycle, audience, evidence class, source type, and publication metadata.
+- Build literature maps, reading lists, dashboards, or retrieval indexes.
+- Audit contribution, novelty, impact, provenance, and evidence claims.
+- Find reusable patterns, contracts, schemas, and runnable examples.
 
 Do not use `signal_strength`, GitHub stars, forks, or inclusion in this collection as a quality label, endorsement, or automated ranking of scientific validity.
 
@@ -90,7 +90,7 @@ The complete field-by-field schema is documented in [`data/README.md`](https://g
 
 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).
 
-Annotations are original repository syntheses, not copied abstracts and not 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, and the maintainer is accountable for the released inclusion decision and text.
+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.
 
 The full point-in-time audit is available as [`data/resource_source_audit.csv`](https://huggingface.co/datasets/cy0307/awesome-loop-engineering/blob/main/data/resource_source_audit.csv).
 
@@ -106,7 +106,7 @@ The full point-in-time audit is available as [`data/resource_source_audit.csv`](
 
 ## Versioning And Corrections
 
-GitHub Releases define versioned snapshots. Cite a release or commit when reproducibility matters. Accuracy corrections to summaries, contribution, novelty, impact, author, date, venue, identifier, or canonical URL are accepted through the [correction form](https://github.com/ChaoYue0307/awesome-loop-engineering/issues/new?template=annotation-correction.yml).
+GitHub Releases define versioned snapshots; cite a release or commit for reproducibility. Submit corrections to summaries, contribution, novelty, impact, authorship, dates, venues, identifiers, or canonical URLs through the [correction form](https://github.com/ChaoYue0307/awesome-loop-engineering/issues/new?template=annotation-correction.yml).
 
 ## Citation
 

meta/social-preview.html

@@ -63,7 +63,7 @@
   <body>
     <main class="preview">
       <div class="identity">
-        <p class="kicker">The field guide for recurring AI-agent systems</p>
+        <p class="kicker">Source-audited field guide for recurring AI-agent systems</p>
         <h1>Awesome Loop Engineering</h1>
         <p class="tagline">Design loops, not just prompts.</p>
         <p class="support">Discover. Delegate. Verify. Persist. Decide.</p>
@@ -72,7 +72,7 @@
         <img src="../assets/awesome-loop-engineering-logo.png" alt="">
       </div>
       <div class="stats">
-        <div class="stat"><b>540</b><span>Curated entries</span></div>
+        <div class="stat"><b>540</b><span>Audited resources</span></div>
         <div class="stat"><b>15</b><span>Operational patterns</span></div>
         <div class="stat"><b>15</b><span>Validated contracts</span></div>
       </div>

patterns/README.md

@@ -1,6 +1,6 @@
 # Loop Pattern Library
 
-This directory turns Loop Engineering principles into practical operating patterns. Each pattern sits above prompt, context, and harness choices: it decides when to trigger agents, what context and tools they get, how work is verified, what state survives, and when the loop repeats or escalates. Each pattern is intentionally small enough to adapt to Codex, Claude Code, GitHub Agentic Workflows, shell scripts, or custom agent runtimes.
+This library turns Loop Engineering principles into portable operating patterns. Each pattern specifies the trigger, context and tools, verification gate, durable state, budget, and escalation path. Adapt them to Codex, Claude Code, GitHub Agentic Workflows, shell scripts, or custom runtimes.
 
 Use a pattern when you can name:
 
@@ -38,6 +38,6 @@ Compare every pattern side by side in the [pattern matrix](MATRIX.md), or start
 
 ## Pattern Quality Bar
 
-A good loop pattern should be boring in the right places. The goal, allowed actions, deterministic verification gate, retry budget, and stop condition should be explicit before the agent starts doing work.
+A strong pattern makes the objective, allowed actions, verification gate, retry budget, and stop condition explicit before the agent acts.
 
-Avoid patterns that depend only on the model saying "looks good". Prefer checks with exit codes, changed files, issue links, trace IDs, dashboards, screenshots, or reviewer decisions.
+Reject patterns that depend on the model saying "looks good". Prefer exit codes, changed files, issue links, trace IDs, dashboards, screenshots, or reviewer decisions.

posts/launch.md

@@ -1,12 +1,12 @@
 # Awesome Loop Engineering v0.6.0
 
-Awesome Loop Engineering v0.6.0 strengthens the project as both a field guide and a verifiable data product for recurring AI-agent systems.
+Awesome Loop Engineering v0.6.0 turns the project into a source-audited field guide, implementation kit, and structured dataset for recurring AI-agent systems.
 
-The collection now maps 540 sources across the operating layer above prompt, context, and harness engineering: the system that discovers work, delegates it, verifies results, persists state, and decides what happens next.
+The collection maps 540 sources across the operating layer that discovers work, delegates it, verifies results, persists state, and decides what happens next.
 
 ## What Is Included
 
-- 540 audited resources linked to canonical sources
+- 540 source-audited resources linked to canonical works
 - 15 operational patterns and a symptom-first comparison matrix
 - 15 schema-validated loop contracts
 - 6 runnable templates for local, scheduled, CI, and hosted execution
@@ -27,11 +27,11 @@ The collection now maps 540 sources across the operating layer above prompt, con
 
 ## Why This Matters
 
-Prompt engineering improves what you ask the model. Context engineering improves what the model can see. Harness engineering improves the tools, permissions, sandboxes, and checks around one run.
+Prompt engineering shapes the instruction. Context engineering shapes what the model can see. Harness engineering shapes one run's tools, permissions, isolation, and checks.
 
-Loop Engineering asks the next operating question: what recurring system should wake up, find work, load durable context, act safely, verify with explicit gates, record receipts, and then repeat, report, escalate, or stop?
+Loop Engineering asks the operating question across runs: what starts the system, where work enters, who acts, what evidence gates results, what state survives, and when does the system repeat, report, escalate, or stop?
 
-The goal is not unlimited autonomy. The goal is bounded, reviewable, evidence-driven repetition.
+The goal is bounded, reviewable, evidence-driven repetition, not unlimited autonomy.
 
 ## Explore And Reuse
 
@@ -42,7 +42,7 @@ The goal is not unlimited autonomy. The goal is bounded, reviewable, evidence-dr
 - [Review the curation standard](https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/meta/CURATION.md)
 - [Contribute a source or correction](https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/CONTRIBUTING.md)
 
-Corrections are especially valuable. If a cited work is summarized inaccurately, or a stronger canonical source exists, use the annotation-correction form or open a pull request.
+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
 

scripts/check_project_consistency.py

@@ -31,7 +31,7 @@ def main() -> int:
         ROOT / "README.md",
         [
             f"resources-{count}-",
-            f"**{count} curated resources**",
+            f"**{count} source-audited resources**",
             f"covers all {count} rows",
         ],
         failures,
@@ -39,18 +39,17 @@ def main() -> int:
     require(
         ROOT / "docs" / "index.html",
         [
-            f'content="{count} audited resources',
-            f">{count}</b><span>curated entries</span>",
-            f"Search {count} audited works",
-            f"Search {count} audited resources",
+            f'content="{count} source-audited resources',
+            f">{count}</b><span>audited resources</span>",
+            f"Filter {count} source-audited works",
             f'"version": "{version}"',
         ],
         failures,
     )
     require(ROOT / "meta" / "social-preview.html", [f">{count}</b>"], failures)
-    require(ROOT / "posts" / "launch.md", [f"# Awesome Loop Engineering v{version}", f"{count} audited resources"], failures)
+    require(ROOT / "posts" / "launch.md", [f"# Awesome Loop Engineering v{version}", f"{count} source-audited resources"], failures)
     require(ROOT / "posts" / "launch.zh-CN.md", [f"# Awesome Loop Engineering v{version}", f"{count} 条"], failures)
-    require(ROOT / "meta" / "DISTRIBUTION.md", [f"v{version}", f"{count} audited resources"], failures)
+    require(ROOT / "meta" / "DISTRIBUTION.md", [f"v{version}", f"{count} source-audited resources"], failures)
 
     for translation in sorted(ROOT.glob("README.*.md")):
         require(translation, [count], failures)