diff --git "a/data/resources.jsonl" "b/data/resources.jsonl"
--- "a/data/resources.jsonl"
+++ "b/data/resources.jsonl"
@@ -521,25 +521,25 @@
 {"row_id": "ale-0521", "section": "Critiques, Risks, And Limitations", "section_slug": "critiques-risks-and-limitations", "resource_type": "Paper", "marker": "📄", "title": "Compaction as Epistemic Failure: How Agentic LLM Tools Fabricate Confirmed Results from Killed Processes", "url": "https://arxiv.org/abs/2607.13071", "url_kind": "external", "domain": "arxiv.org", "annotation": "Documents a Claude Code failure in which partial output from a process killed with exit 143 becomes a confirmed claim after context compaction, without re-verification, showing why receipts and process status must survive summarization.", "description": "Documents a Claude Code failure in which partial output from a process killed with exit 143 becomes a confirmed claim after context compaction, without re-verification, showing why receipts and process status must survive summarization.", "key_contribution": "Documents a Claude Code failure in which partial output from a process killed with exit 143 becomes a confirmed claim after context compaction, without re-verification, showing why receipts and process status must survive summarization.", "novelty": "Verification is promoted from a final check to a loop-control signal. Documents a Claude Code failure in which partial output from a process killed with exit 143 becomes a confirmed claim after context compaction, without re-verification, showing why receipts and process status must survive summarization.", "impact": "Use Compaction as Epistemic Failure: How Agentic LLM Tools Fabricate Confirmed Results from Killed Processes to bound risk before recurring or unattended execution.", "signal": "Research source arXiv:2607.13071; inspect its method and evaluation before treating results as production evidence.", "signal_strength": "medium", "source_readme": "README.md", "source_line": 1197, "source_url": "https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1197", "date_added": "2026-07-17", "collection": "Govern", "collection_slug": "govern", "user_goal": "Bound permissions, cost, failure, and escalation.", "lifecycle_stages": "workspace;context;verification;state;exit", "audience": "researcher;evaluator;operator;security", "evidence_class": "research-preprint", "source_status": "ok", "canonical_url": "https://arxiv.org/abs/2607.13071", "source_title": "[2607.13071] Compaction as Epistemic Failure: How Agentic LLM Tools Fabricate Confirmed Results from Killed Processes", "source_description": "Agentic LLM coding tools compress long session histories into compaction summaries that subsequent sessions inherit as ground truth. This paper documents a failure mode in Claude Code where partial standard output from timed-out commands (exit code 143) is recorded in compaction summaries as confirmed results, propagating false positives across sessions and model versions without re-verification. The underlying mechanism is a conflation of observation and persistence, where information that appeared in the terminal is treated as equivalent to information written to durable storage. This finding extends the analysis of LLM self-evaluation failures reported in prior work on non-determinism in LLM-as-judge grading by showing that agentic tools exhibit analogous reliability deficits when reporting on their own operational outcomes. The failure has direct implications for any workflow that relies on agentic session continuity for data processing, scientific computation, or multi-step automation.", "authors": "Hiroki Tamba", "publication_date": "2026-07-11", "publication_year": "2026", "publication_venue": "arXiv", "publisher": "arXiv", "doi": "", "publication_note": "8 pages, companion to arXiv:2606.26185", "primary_category": "cs.SE", "metadata_source": "arxiv-api", "github_repo": "", "github_stars": "", "github_forks": "", "github_license": "", "github_created_at": "", "github_updated_at": "", "arxiv_id": "2607.13071", "audited_at": "2026-07-17T17:58:18+00:00"}
 {"row_id": "ale-0522", "section": "Critiques, Risks, And Limitations", "section_slug": "critiques-risks-and-limitations", "resource_type": "Paper", "marker": "📄", "title": "Do Agent Optimizers Compound? A Continual-Learning Evaluation on Terminal-Bench 2.0", "url": "https://arxiv.org/abs/2607.14004", "url_kind": "external", "domain": "arxiv.org", "annotation": "Compares GEPA, Meta Harness, and RELAI-VCL under matched continual-learning budgets; only the regression-controlled method keeps improving, reaching 76.4% lifelong performance versus 66.0%, 64.6%, and 58.7% for the reported alternatives.", "description": "Compares GEPA, Meta Harness, and RELAI-VCL under matched continual-learning budgets; only the regression-controlled method keeps improving, reaching 76.4% lifelong performance versus 66.0%, 64.6%, and 58.7% for the reported alternatives.", "key_contribution": "Compares GEPA, Meta Harness, and RELAI-VCL under matched continual-learning budgets; only the regression-controlled method keeps improving, reaching 76.4% lifelong performance versus 66.0%, 64.6%, and 58.7% for the reported alternatives.", "novelty": "Evaluation data is used as the feedback signal for improving loop behavior. Compares GEPA, Meta Harness, and RELAI-VCL under matched continual-learning budgets; only the regression-controlled method keeps improving, reaching 76.4% lifelong performance versus 66.0%, 64.6%, and 58.7% for the reported alternatives.", "impact": "Use Do Agent Optimizers Compound? A Continual-Learning Evaluation on Terminal-Bench 2.0 to bound risk before recurring or unattended execution.", "signal": "Research source arXiv:2607.14004; inspect its method and evaluation before treating results as production evidence.", "signal_strength": "medium", "source_readme": "README.md", "source_line": 1198, "source_url": "https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1198", "date_added": "2026-07-17", "collection": "Govern", "collection_slug": "govern", "user_goal": "Bound permissions, cost, failure, and escalation.", "lifecycle_stages": "verification;budget", "audience": "researcher;evaluator;operator;security", "evidence_class": "research-preprint", "source_status": "ok", "canonical_url": "https://arxiv.org/abs/2607.14004", "source_title": "[2607.14004] Do Agent Optimizers Compound? A Continual-Learning Evaluation on Terminal-Bench 2.0", "source_description": "Most reported gains from agent-optimization methods are one-shot: an agent is optimized against a fixed benchmark and the resulting improvement is reported as if it were a stable property of the method. This does not test the setting that matters for deployed agents, where optimization is applied recursively as new failures and new tasks appear over time. The central question this raises is whether optimizer-driven gains compound: after an agent has been optimized once, can it be optimized again on newly arrived tasks without eroding the gains the first round produced? We study this question with a two-phase continual-learning evaluation built from hard tasks in Terminal-Bench 2.0, comparing three approaches to agent-harness optimization (GEPA, Meta Harness, and RELAI's Verifiable Continual Learning, RELAI-VCL) under identical optimization budgets. All three methods improve over the baseline agent in the conventional, static, single-phase setting. However, once new tasks are introduced, the methods diverge sharply: GEPA's optimized agent transfers below the unoptimized baseline, Meta Harness transfers well but fails to improve further once given a second optimization budget, and RELAI-VCL is the only method that both transfers positively to unseen tasks and continues improving after those tasks are folded into the optimization objective, reaching the highest pass rate at every evaluated stage and the highest lifelong average pass rate overall (76.4% vs. 66.0% for GEPA, 64.6% for Meta Harness, and 58.7% for the baseline). Our key observation was that optimization gains compounded only when regression control was built into the optimization loop, providing an inductive bias against shortcut solutions that fail to generalize.", "authors": "Wenxiao Wang; Priyatham Kattakinda; Soheil Feizi", "publication_date": "2026-07-15", "publication_year": "2026", "publication_venue": "arXiv", "publisher": "arXiv", "doi": "", "publication_note": "Technical Report by RELAI (relai.ai)", "primary_category": "cs.AI", "metadata_source": "arxiv-api", "github_repo": "", "github_stars": "", "github_forks": "", "github_license": "", "github_created_at": "", "github_updated_at": "", "arxiv_id": "2607.14004", "audited_at": "2026-07-17T17:58:18+00:00"}
 {"row_id": "ale-0523", "section": "Critiques, Risks, And Limitations", "section_slug": "critiques-risks-and-limitations", "resource_type": "Paper", "marker": "📄", "title": "Does Multi-Agent Debate Improve AI Feedback on Research Papers?", "url": "https://arxiv.org/abs/2607.14713", "url_kind": "external", "domain": "arxiv.org", "annotation": "In a preregistered masked study with authors of 44 meta-analyses, participants prefer single-pass feedback to two multi-agent debate systems, one using about 30x more tokens; AI judges reverse the human preference, warning against self-evaluation alone.", "description": "In a preregistered masked study with authors of 44 meta-analyses, participants prefer single-pass feedback to two multi-agent debate systems, one using about 30x more tokens; AI judges reverse the human preference, warning against self-evaluation alone.", "key_contribution": "In a preregistered masked study with authors of 44 meta-analyses, participants prefer single-pass feedback to two multi-agent debate systems, one using about 30x more tokens; AI judges reverse the human preference, warning against self-evaluation alone.", "novelty": "Evaluation data is used as the feedback signal for improving loop behavior. In a preregistered masked study with authors of 44 meta-analyses, participants prefer single-pass feedback to two multi-agent debate systems, one using about 30x more tokens; AI judges reverse the human preference, warning against self-evaluation alone.", "impact": "Use Does Multi-Agent Debate Improve AI Feedback on Research Papers? to bound risk before recurring or unattended execution.", "signal": "Research source arXiv:2607.14713; inspect its method and evaluation before treating results as production evidence.", "signal_strength": "medium", "source_readme": "README.md", "source_line": 1199, "source_url": "https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1199", "date_added": "2026-07-17", "collection": "Govern", "collection_slug": "govern", "user_goal": "Bound permissions, cost, failure, and escalation.", "lifecycle_stages": "delegation;verification;budget;escalation", "audience": "researcher;evaluator;operator;security", "evidence_class": "research-preprint", "source_status": "ok", "canonical_url": "https://arxiv.org/abs/2607.14713", "source_title": "[2607.14713] Does Multi-Agent Debate Improve AI Feedback on Research Papers?", "source_description": "Probably not, at least for meta-analyses in economics. In a pre-registered, identity-masked, within-paper experiment, the authors of 44 meta-analyses ranked three AI reports on their own paper by usefulness for improving it: a single pass by a frontier model against two multi-agent debate tools we built and expected to win. All reports were held to a common length and template. The authors preferred the single pass, by 0.66 rank points over mad-research (95% CI 0.32 to 1.00) and 0.57 over paper-workshop (0.16 to 0.95), though paper-workshop spent roughly thirty times the tokens. Authors who recalled their journal referee report usually placed it first and never last; in a separate exercise, three AI judges almost always placed the real journal referee report last. Among the three AI reports, Gemini (the judge whose model family wrote none of the reports) would have ranked paper-workshop first in the authors' place, reversing the single-pass preference. The reversal warns against substituting an AI judge for the author. We measure perceived usefulness for finished papers; whether AI should referee papers is a separate question.", "authors": "Tomas Havranek; Zuzana Irsova", "publication_date": "2026-07-16", "publication_year": "2026", "publication_venue": "arXiv", "publisher": "arXiv", "doi": "", "publication_note": "29 pages, 1 figure, 6 tables. Pre-registered on OSF; data, code, judge prompts, and blinded reports in the replication package on Zenodo. Project page: https://meta-analysis.cz/debate", "primary_category": "econ.GN", "metadata_source": "arxiv-api", "github_repo": "", "github_stars": "", "github_forks": "", "github_license": "", "github_created_at": "", "github_updated_at": "", "arxiv_id": "2607.14713", "audited_at": "2026-07-17T17:58:18+00:00"}
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+{"row_id": "ale-0530", "section": "Adjacent Awesome Lists", "section_slug": "adjacent-awesome-lists", "resource_type": "List", "marker": "🧭", "title": "Awesome AI Agents", "url": "https://github.com/e2b-dev/awesome-ai-agents", "url_kind": "external", "domain": "github.com", "annotation": "Broad AI agent ecosystem map.", "description": "Broad AI agent ecosystem map.", "key_contribution": "Broad AI agent ecosystem map.", "novelty": "Connects neighboring ecosystems while preserving Loop Engineering as a narrower operating concept. Broad AI agent ecosystem map.", "impact": "Use Awesome AI Agents to reuse a concrete artifact or connect it to the wider ecosystem.", "signal": "Inspectable GitHub source (28,848 stars; 3,178 forks; NOASSERTION license; updated 2026-07-17); popularity is context, not proof of reliability.", "signal_strength": "medium", "source_readme": "README.md", "source_line": 1225, "source_url": "https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1225", "date_added": "", "collection": "Apply", "collection_slug": "apply", "user_goal": "Reuse, adapt, and contribute concrete loop artifacts.", "lifecycle_stages": "whole-loop", "audience": "builder", "evidence_class": "curated-index", "source_status": "ok", "canonical_url": "https://github.com/e2b-dev/awesome-ai-agents", "source_title": "GitHub - e2b-dev/awesome-ai-agents: A list of AI autonomous agents · GitHub", "source_description": "A list of AI autonomous agents. Contribute to e2b-dev/awesome-ai-agents development by creating an account on GitHub.", "authors": "", "publication_date": "2023-06-19", "publication_year": "2023", "publication_venue": "e2b-dev/awesome-ai-agents", "publisher": "GitHub", "doi": "", "publication_note": "", "primary_category": "", "metadata_source": "github-api", "github_repo": "e2b-dev/awesome-ai-agents", "github_stars": "28848", "github_forks": "3178", "github_license": "NOASSERTION", "github_created_at": "2023-06-19T00:20:06Z", "github_updated_at": "2026-07-17T16:42:36Z", "arxiv_id": "", "audited_at": "2026-07-17T17:58:18+00:00"}
+{"row_id": "ale-0531", "section": "Adjacent Awesome Lists", "section_slug": "adjacent-awesome-lists", "resource_type": "List", "marker": "🧭", "title": "Awesome CLI Coding Agents", "url": "https://github.com/bradAGI/awesome-cli-coding-agents", "url_kind": "external", "domain": "github.com", "annotation": "Directory of terminal-native coding agents, parallel runners, autonomous loops, and the harnesses that orchestrate them.", "description": "Directory of terminal-native coding agents, parallel runners, autonomous loops, and the harnesses that orchestrate them.", "key_contribution": "Directory of terminal-native coding agents, parallel runners, autonomous loops, and the harnesses that orchestrate them.", "novelty": "Orchestration and control flow are made explicit and inspectable. Directory of terminal-native coding agents, parallel runners, autonomous loops, and the harnesses that orchestrate them.", "impact": "Use Awesome CLI Coding Agents to reuse a concrete artifact or connect it to the wider ecosystem.", "signal": "Inspectable GitHub source (820 stars; 219 forks; updated 2026-07-17); popularity is context, not proof of reliability.", "signal_strength": "medium", "source_readme": "README.md", "source_line": 1226, "source_url": "https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1226", "date_added": "", "collection": "Apply", "collection_slug": "apply", "user_goal": "Reuse, adapt, and contribute concrete loop artifacts.", "lifecycle_stages": "delegation", "audience": "builder", "evidence_class": "curated-index", "source_status": "ok", "canonical_url": "https://github.com/bradAGI/awesome-cli-coding-agents", "source_title": "GitHub - bradAGI/awesome-cli-coding-agents: Curated directory of terminal-native AI coding agents and the harnesses that orchestrate them. Covers open-source tools (Pi, OpenCode, Aider, Goose), platform agents (Claude Code, Codex, Gemini CLI), parallel runners, autonomous loops, and agent infrastructure. · GitHub", "source_description": "Curated directory of terminal-native AI coding agents and the harnesses that orchestrate them. Covers open-source tools (Pi, OpenCode, Aider, Goose), platform agents (Claude Code, Codex, Gemini CLI), parallel runners, autonomous loops, and agent infrastructure. - GitHub - bradAGI/awesome-cli-coding-agents: Curated directory of terminal-native AI coding agents and the harnesses that orchestrate them. Covers open-source tools (Pi, OpenCode, Aider, Goose), platform agents (Claude Code, Codex, Gemini CLI), parallel runners, autonomous loops, and agent infrastructure.", "authors": "", "publication_date": "2026-02-07", "publication_year": "2026", "publication_venue": "bradAGI/awesome-cli-coding-agents", "publisher": "GitHub", "doi": "", "publication_note": "", "primary_category": "", "metadata_source": "github-api", "github_repo": "bradAGI/awesome-cli-coding-agents", "github_stars": "820", "github_forks": "219", "github_license": "", "github_created_at": "2026-02-07T00:53:24Z", "github_updated_at": "2026-07-17T15:10:10Z", "arxiv_id": "", "audited_at": "2026-07-17T17:58:18+00:00"}
+{"row_id": "ale-0532", "section": "Adjacent Awesome Lists", "section_slug": "adjacent-awesome-lists", "resource_type": "List", "marker": "🧭", "title": "Awesome Self-Evolving Agents", "url": "https://github.com/XMUDeepLIT/Awesome-Self-Evolving-Agents", "url_kind": "external", "domain": "github.com", "annotation": "Survey-style list of agents that improve themselves over repeated runs, an adjacent angle on long-running loops with memory and verification.", "description": "Survey-style list of agents that improve themselves over repeated runs, an adjacent angle on long-running loops with memory and verification.", "key_contribution": "Survey-style list of agents that improve themselves over repeated runs, an adjacent angle on long-running loops with memory and verification.", "novelty": "Verification is promoted from a final check to a loop-control signal. Survey-style list of agents that improve themselves over repeated runs, an adjacent angle on long-running loops with memory and verification.", "impact": "Use Awesome Self-Evolving Agents to reuse a concrete artifact or connect it to the wider ecosystem.", "signal": "Inspectable GitHub source (329 stars; 19 forks; updated 2026-07-17); popularity is context, not proof of reliability.", "signal_strength": "medium", "source_readme": "README.md", "source_line": 1227, "source_url": "https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1227", "date_added": "", "collection": "Apply", "collection_slug": "apply", "user_goal": "Reuse, adapt, and contribute concrete loop artifacts.", "lifecycle_stages": "context;verification", "audience": "builder", "evidence_class": "curated-index", "source_status": "ok", "canonical_url": "https://github.com/XMUDeepLIT/Awesome-Self-Evolving-Agents", "source_title": "GitHub - XMUDeepLIT/Awesome-Self-Evolving-Agents: A Survey of Self-Evolving Agents | A curated list of resources (surveys, papers, benchmarks, and opensource projects) on Self-Evolving Agents. · GitHub", "source_description": "A Survey of Self-Evolving Agents | A curated list of resources (surveys, papers, benchmarks, and opensource projects) on Self-Evolving Agents. - XMUDeepLIT/Awesome-Self-Evolving-Agents", "authors": "", "publication_date": "2026-02-09", "publication_year": "2026", "publication_venue": "XMUDeepLIT/Awesome-Self-Evolving-Agents", "publisher": "GitHub", "doi": "", "publication_note": "", "primary_category": "", "metadata_source": "github-api", "github_repo": "XMUDeepLIT/Awesome-Self-Evolving-Agents", "github_stars": "329", "github_forks": "19", "github_license": "", "github_created_at": "2026-02-09T10:57:30Z", "github_updated_at": "2026-07-17T10:00:18Z", "arxiv_id": "", "audited_at": "2026-07-17T17:58:18+00:00"}
+{"row_id": "ale-0533", "section": "Adjacent Awesome Lists", "section_slug": "adjacent-awesome-lists", "resource_type": "List", "marker": "🧭", "title": "Awesome AI Agent Papers", "url": "https://github.com/VoltAgent/awesome-ai-agent-papers", "url_kind": "external", "domain": "github.com", "annotation": "Curated 2026 research collection across agent engineering, memory, evaluation, workflows, and autonomous systems, a paper-level feeder for loop-design foundations.", "description": "Curated 2026 research collection across agent engineering, memory, evaluation, workflows, and autonomous systems, a paper-level feeder for loop-design foundations.", "key_contribution": "Curated 2026 research collection across agent engineering, memory, evaluation, workflows, and autonomous systems, a paper-level feeder for loop-design foundations.", "novelty": "Evaluation data is used as the feedback signal for improving loop behavior. Curated 2026 research collection across agent engineering, memory, evaluation, workflows, and autonomous systems, a paper-level feeder for loop-design foundations.", "impact": "Use Awesome AI Agent Papers to reuse a concrete artifact or connect it to the wider ecosystem.", "signal": "Inspectable GitHub source (1,591 stars; 165 forks; MIT license; updated 2026-07-17); popularity is context, not proof of reliability.", "signal_strength": "medium", "source_readme": "README.md", "source_line": 1228, "source_url": "https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1228", "date_added": "", "collection": "Apply", "collection_slug": "apply", "user_goal": "Reuse, adapt, and contribute concrete loop artifacts.", "lifecycle_stages": "context;verification", "audience": "builder", "evidence_class": "curated-index", "source_status": "ok", "canonical_url": "https://github.com/VoltAgent/awesome-ai-agent-papers", "source_title": "GitHub - VoltAgent/awesome-ai-agent-papers: A curated collection of AI agent research papers released in 2026, covering agent engineering, memory, evaluation, workflows, and autonomous systems. · GitHub", "source_description": "A curated collection of AI agent research papers released in 2026, covering agent engineering, memory, evaluation, workflows, and autonomous systems. - VoltAgent/awesome-ai-agent-papers", "authors": "", "publication_date": "2026-02-10", "publication_year": "2026", "publication_venue": "VoltAgent/awesome-ai-agent-papers", "publisher": "GitHub", "doi": "", "publication_note": "", "primary_category": "", "metadata_source": "github-api", "github_repo": "VoltAgent/awesome-ai-agent-papers", "github_stars": "1591", "github_forks": "165", "github_license": "MIT", "github_created_at": "2026-02-10T10:58:31Z", "github_updated_at": "2026-07-17T17:40:19Z", "arxiv_id": "", "audited_at": "2026-07-17T17:58:18+00:00"}
+{"row_id": "ale-0534", "section": "Adjacent Awesome Lists", "section_slug": "adjacent-awesome-lists", "resource_type": "List", "marker": "🧭", "title": "awesome-ralph", "url": "https://github.com/snwfdhmp/awesome-ralph", "url_kind": "external", "domain": "github.com", "annotation": "Curated directory for the Ralph technique, collecting official resources, implementations, playbooks, tutorials, and community channels for running coding agents in automated loops until specifications are fulfilled.", "description": "Curated directory for the Ralph technique, collecting official resources, implementations, playbooks, tutorials, and community channels for running coding agents in automated loops until specifications are fulfilled.", "key_contribution": "Curated directory for the Ralph technique, collecting official resources, implementations, playbooks, tutorials, and community channels for running coding agents in automated loops until specifications are fulfilled.", "novelty": "Primary-source operational guidance rather than commentary. Curated directory for the Ralph technique, collecting official resources, implementations, playbooks, tutorials, and community channels for running coding agents in automated loops until specifications are fulfilled.", "impact": "Use awesome-ralph to reuse a concrete artifact or connect it to the wider ecosystem.", "signal": "Inspectable GitHub source (913 stars; 72 forks; updated 2026-07-13); popularity is context, not proof of reliability.", "signal_strength": "medium", "source_readme": "README.md", "source_line": 1229, "source_url": "https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1229", "date_added": "", "collection": "Apply", "collection_slug": "apply", "user_goal": "Reuse, adapt, and contribute concrete loop artifacts.", "lifecycle_stages": "whole-loop", "audience": "builder", "evidence_class": "curated-index", "source_status": "ok", "canonical_url": "https://github.com/snwfdhmp/awesome-ralph", "source_title": "GitHub - snwfdhmp/awesome-ralph: A curated list of resources about Ralph, the AI coding technique that runs AI coding agents in automated loops until specifications are fulfilled. · GitHub", "source_description": "A curated list of resources about Ralph, the AI coding technique that runs AI coding agents in automated loops until specifications are fulfilled. - snwfdhmp/awesome-ralph", "authors": "", "publication_date": "2026-01-19", "publication_year": "2026", "publication_venue": "snwfdhmp/awesome-ralph", "publisher": "GitHub", "doi": "", "publication_note": "", "primary_category": "", "metadata_source": "github-api", "github_repo": "snwfdhmp/awesome-ralph", "github_stars": "913", "github_forks": "72", "github_license": "", "github_created_at": "2026-01-19T08:42:54Z", "github_updated_at": "2026-07-13T12:09:52Z", "arxiv_id": "", "audited_at": "2026-07-17T17:58:18+00:00"}
+{"row_id": "ale-0535", "section": "Adjacent Awesome Lists", "section_slug": "adjacent-awesome-lists", "resource_type": "List", "marker": "🧭", "title": "Awesome Agent Loops", "url": "https://github.com/serenakeyitan/awesome-agent-loops", "url_kind": "external", "domain": "github.com", "annotation": "Curated collection of /loop, /goal, and /schedule commands for Claude Code and Codex sourced from practitioner posts, organized around trigger, condition, and skill structure.", "description": "Curated collection of /loop, /goal, and /schedule commands for Claude Code and Codex sourced from practitioner posts, organized around trigger, condition, and skill structure.", "key_contribution": "Curated collection of /loop, /goal, and /schedule commands for Claude Code and Codex sourced from practitioner posts, organized around trigger, condition, and skill structure.", "novelty": "The trigger or cadence is explicit, making the workflow recurring rather than one-off. Curated collection of /loop, /goal, and /schedule commands for Claude Code and Codex sourced from practitioner posts, organized around trigger, condition, and skill structure.", "impact": "Use Awesome Agent Loops to reuse a concrete artifact or connect it to the wider ecosystem.", "signal": "Inspectable GitHub source (194 stars; 16 forks; CC-BY-4.0 license; updated 2026-07-15); popularity is context, not proof of reliability.", "signal_strength": "medium", "source_readme": "README.md", "source_line": 1230, "source_url": "https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1230", "date_added": "", "collection": "Apply", "collection_slug": "apply", "user_goal": "Reuse, adapt, and contribute concrete loop artifacts.", "lifecycle_stages": "objective;trigger", "audience": "builder", "evidence_class": "curated-index", "source_status": "ok", "canonical_url": "https://github.com/serenakeyitan/awesome-agent-loops", "source_title": "GitHub - serenakeyitan/awesome-agent-loops: A curated collection of the best /loop, /goal, and /schedule uses for Claude Code & Codex — real commands sourced from Twitter/X. The awesome-list of agent loops. · GitHub", "source_description": "A curated collection of the best /loop, /goal, and /schedule uses for Claude Code & Codex — real commands sourced from Twitter/X. The awesome-list of agent loops. - serenakeyitan/awesome-agent-loops", "authors": "", "publication_date": "2026-06-09", "publication_year": "2026", "publication_venue": "serenakeyitan/awesome-agent-loops", "publisher": "GitHub", "doi": "", "publication_note": "", "primary_category": "", "metadata_source": "github-api", "github_repo": "serenakeyitan/awesome-agent-loops", "github_stars": "194", "github_forks": "16", "github_license": "CC-BY-4.0", "github_created_at": "2026-06-09T01:26:51Z", "github_updated_at": "2026-07-15T11:44:20Z", "arxiv_id": "", "audited_at": "2026-07-17T17:58:18+00:00"}
+{"row_id": "ale-0536", "section": "Explore And Reuse", "section_slug": "explore-and-reuse", "resource_type": "Template", "marker": "🧾", "title": "Resource Atlas", "url": "https://chaoyue0307.github.io/awesome-loop-engineering/", "url_kind": "external", "domain": "chaoyue0307.github.io", "annotation": "Filter 545 resources by goal, lifecycle stage, artifact type, evidence class, and search query.", "description": "Filter 545 resources by goal, lifecycle stage, artifact type, evidence class, and search query.", "key_contribution": "Filter 545 resources by goal, lifecycle stage, artifact type, evidence class, and search query.", "novelty": "Turns the evidence into an interactive atlas and structured data. Filter 545 resources by goal, lifecycle stage, artifact type, evidence class, and search query.", "impact": "Use Resource Atlas to reuse a concrete artifact or connect it to the wider ecosystem.", "signal": "Reusable template, schema, checklist, or guide; signal comes from concrete adaptation and validation.", "signal_strength": "medium", "source_readme": "README.md", "source_line": 1238, "source_url": "https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1238", "date_added": "", "collection": "Apply", "collection_slug": "apply", "user_goal": "Reuse, adapt, and contribute concrete loop artifacts.", "lifecycle_stages": "objective", "audience": "builder", "evidence_class": "reusable-artifact", "source_status": "ok", "canonical_url": "https://chaoyue0307.github.io/awesome-loop-engineering/", "source_title": "Awesome Loop Engineering", "source_description": "545 source-audited resources, 20 patterns, 20 validated contracts, and 8 runtime starters for recurring, stateful, verified AI-agent systems.", "authors": "Chaoyue He", "publication_date": "", "publication_year": "", "publication_venue": "", "publisher": "chaoyue0307.github.io", "doi": "", "publication_note": "", "primary_category": "", "metadata_source": "html-meta", "github_repo": "", "github_stars": "", "github_forks": "", "github_license": "", "github_created_at": "", "github_updated_at": "", "arxiv_id": "", "audited_at": "2026-07-17T17:58:18+00:00"}
+{"row_id": "ale-0537", "section": "Explore And Reuse", "section_slug": "explore-and-reuse", "resource_type": "List", "marker": "🧭", "title": "Hugging Face dataset", "url": "https://huggingface.co/datasets/cy0307/awesome-loop-engineering", "url_kind": "external", "domain": "huggingface.co", "annotation": "Query the full collection as generated CSV and JSONL tables with publication, evidence, and lifecycle fields.", "description": "Query the full collection as generated CSV and JSONL tables with publication, evidence, and lifecycle fields.", "key_contribution": "Query the full collection as generated CSV and JSONL tables with publication, evidence, and lifecycle fields.", "novelty": "Packages the evidence as queryable CSV and JSONL rather than only a rendered page. Query the full collection as generated CSV and JSONL tables with publication, evidence, and lifecycle fields.", "impact": "Use Hugging Face dataset to reuse a concrete artifact or connect it to the wider ecosystem.", "signal": "Contextual source from huggingface.co; useful for practice signals or boundary conditions, not independent validation.", "signal_strength": "contextual", "source_readme": "README.md", "source_line": 1239, "source_url": "https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1239", "date_added": "", "collection": "Apply", "collection_slug": "apply", "user_goal": "Reuse, adapt, and contribute concrete loop artifacts.", "lifecycle_stages": "whole-loop", "audience": "builder", "evidence_class": "curated-index", "source_status": "ok", "canonical_url": "https://huggingface.co/datasets/cy0307/awesome-loop-engineering", "source_title": "cy0307/awesome-loop-engineering · Datasets at Hugging Face", "source_description": "We’re on a journey to advance and democratize artificial intelligence through open source and open science.", "authors": "", "publication_date": "", "publication_year": "", "publication_venue": "", "publisher": "Hugging Face", "doi": "", "publication_note": "", "primary_category": "", "metadata_source": "domain-fallback", "github_repo": "", "github_stars": "", "github_forks": "", "github_license": "", "github_created_at": "", "github_updated_at": "", "arxiv_id": "", "audited_at": "2026-07-17T17:58:18+00:00"}
+{"row_id": "ale-0538", "section": "Explore And Reuse", "section_slug": "explore-and-reuse", "resource_type": "Template", "marker": "🧾", "title": "Dataset export guide", "url": "data/README.md", "url_kind": "local_path", "domain": "", "annotation": "Load, query, regenerate, and audit the CSV, JSONL, and Resource Atlas data.", "description": "Load, query, regenerate, and audit the CSV, JSONL, and Resource Atlas data.", "key_contribution": "Load, query, regenerate, and audit the CSV, JSONL, and Resource Atlas data.", "novelty": "Packages the evidence as queryable CSV and JSONL rather than only a rendered page. Load, query, regenerate, and audit the CSV, JSONL, and Resource Atlas data.", "impact": "Use Dataset export guide to reuse a concrete artifact or connect it to the wider ecosystem.", "signal": "Local artifact maintained with automated validation checks.", "signal_strength": "medium", "source_readme": "README.md", "source_line": 1240, "source_url": "https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1240", "date_added": "", "collection": "Apply", "collection_slug": "apply", "user_goal": "Reuse, adapt, and contribute concrete loop artifacts.", "lifecycle_stages": "whole-loop", "audience": "builder", "evidence_class": "repository-native", "source_status": "local_ok", "canonical_url": "https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/data/README.md", "source_title": "Dataset export guide", "source_description": "", "authors": "", "publication_date": "", "publication_year": "2026", "publication_venue": "GitHub", "publisher": "GitHub", "doi": "", "publication_note": "", "primary_category": "", "metadata_source": "repository", "github_repo": "", "github_stars": "", "github_forks": "", "github_license": "", "github_created_at": "", "github_updated_at": "", "arxiv_id": "", "audited_at": "2026-07-17T17:58:18+00:00"}
+{"row_id": "ale-0539", "section": "Explore And Reuse", "section_slug": "explore-and-reuse", "resource_type": "Template", "marker": "🧾", "title": "Runtime selection guide", "url": "meta/RUNTIME_SELECTION.md", "url_kind": "local_path", "domain": "", "annotation": "Compare session, scheduled, CI, cron, and durable runtimes by persistence, isolation, permissions, and state.", "description": "Compare session, scheduled, CI, cron, and durable runtimes by persistence, isolation, permissions, and state.", "key_contribution": "Compare session, scheduled, CI, cron, and durable runtimes by persistence, isolation, permissions, and state.", "novelty": "Durable execution and replay are treated as first-class loop infrastructure. Compare session, scheduled, CI, cron, and durable runtimes by persistence, isolation, permissions, and state.", "impact": "Use Runtime selection guide to reuse a concrete artifact or connect it to the wider ecosystem.", "signal": "Local artifact maintained with automated validation checks.", "signal_strength": "medium", "source_readme": "README.md", "source_line": 1241, "source_url": "https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1241", "date_added": "", "collection": "Apply", "collection_slug": "apply", "user_goal": "Reuse, adapt, and contribute concrete loop artifacts.", "lifecycle_stages": "trigger;workspace;state", "audience": "builder", "evidence_class": "repository-native", "source_status": "local_ok", "canonical_url": "https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/meta/RUNTIME_SELECTION.md", "source_title": "Runtime selection guide", "source_description": "", "authors": "", "publication_date": "", "publication_year": "2026", "publication_venue": "GitHub", "publisher": "GitHub", "doi": "", "publication_note": "", "primary_category": "", "metadata_source": "repository", "github_repo": "", "github_stars": "", "github_forks": "", "github_license": "", "github_created_at": "", "github_updated_at": "", "arxiv_id": "", "audited_at": "2026-07-17T17:58:18+00:00"}
+{"row_id": "ale-0540", "section": "Explore And Reuse", "section_slug": "explore-and-reuse", "resource_type": "Template", "marker": "🧾", "title": "Future Directions agenda", "url": "FUTURE-DIRECTIONS.md", "url_kind": "local_path", "domain": "", "annotation": "Turn 15 open problems into measurable studies, runtime projects, product pilots, and shared standards.", "description": "Turn 15 open problems into measurable studies, runtime projects, product pilots, and shared standards.", "key_contribution": "Turn 15 open problems into measurable studies, runtime projects, product pilots, and shared standards.", "novelty": "Turns open gaps into measurable research, infrastructure, and product directions. Turn 15 open problems into measurable studies, runtime projects, product pilots, and shared standards.", "impact": "Use Future Directions agenda to reuse a concrete artifact or connect it to the wider ecosystem.", "signal": "Local artifact maintained with automated validation checks.", "signal_strength": "medium", "source_readme": "README.md", "source_line": 1242, "source_url": "https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1242", "date_added": "", "collection": "Apply", "collection_slug": "apply", "user_goal": "Reuse, adapt, and contribute concrete loop artifacts.", "lifecycle_stages": "whole-loop", "audience": "builder", "evidence_class": "repository-native", "source_status": "local_ok", "canonical_url": "https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/FUTURE-DIRECTIONS.md", "source_title": "Future Directions agenda", "source_description": "", "authors": "", "publication_date": "", "publication_year": "2026", "publication_venue": "GitHub", "publisher": "GitHub", "doi": "", "publication_note": "", "primary_category": "", "metadata_source": "repository", "github_repo": "", "github_stars": "", "github_forks": "", "github_license": "", "github_created_at": "", "github_updated_at": "", "arxiv_id": "", "audited_at": "2026-07-17T17:58:18+00:00"}
+{"row_id": "ale-0541", "section": "Shape What Comes Next", "section_slug": "shape-what-comes-next", "resource_type": "Template", "marker": "🧾", "title": "Release notes", "url": "https://github.com/ChaoYue0307/awesome-loop-engineering/releases", "url_kind": "external", "domain": "github.com", "annotation": "Versioned changelog of new resources, patterns, and repository changes.", "description": "Versioned changelog of new resources, patterns, and repository changes.", "key_contribution": "Versioned changelog of new resources, patterns, and repository changes.", "novelty": "Turns open questions and operating lessons into visible next work. Versioned changelog of new resources, patterns, and repository changes.", "impact": "Use Release notes to reuse a concrete artifact or connect it to the wider ecosystem.", "signal": "Inspectable GitHub source (28 stars; 6 forks; CC0-1.0 license; updated 2026-07-17); popularity is context, not proof of reliability.", "signal_strength": "medium", "source_readme": "README.md", "source_line": 1269, "source_url": "https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L1269", "date_added": "2026-07-15", "collection": "Apply", "collection_slug": "apply", "user_goal": "Reuse, adapt, and contribute concrete loop artifacts.", "lifecycle_stages": "whole-loop", "audience": "builder", "evidence_class": "reusable-artifact", "source_status": "ok", "canonical_url": "https://github.com/ChaoYue0307/awesome-loop-engineering/releases", "source_title": "Releases · ChaoYue0307/awesome-loop-engineering · GitHub", "source_description": "🔁 545 source-audited resources · 20 operational patterns · 20 validated contracts · 8 runtime starters · interactive atlas + dataset for recurring AI-agent systems. - Releases · ChaoYue0307/awesome-loop-engineering", "authors": "", "publication_date": "2026-06-09", "publication_year": "2026", "publication_venue": "GitHub Releases", "publisher": "GitHub", "doi": "", "publication_note": "", "primary_category": "", "metadata_source": "github-api", "github_repo": "ChaoYue0307/awesome-loop-engineering", "github_stars": "28", "github_forks": "6", "github_license": "CC0-1.0", "github_created_at": "2026-06-09T16:17:27Z", "github_updated_at": "2026-07-17T17:42:51Z", "arxiv_id": "", "audited_at": "2026-07-17T17:58:18+00:00"}
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