A source-audited dataset of 545 papers, official docs, tools, benchmarks, patterns, critiques, and implementation guides for recurring AI-agent systems.
Resource Atlas · GitHub field guide · Curation standard · Report a correction
## Dataset Summary Each row represents one resource from the canonical English field guide. It pairs a concise editorial assessment with bibliographic metadata, lifecycle and audience facets, evidence classification, audit status, and point-in-time repository statistics when applicable. Current release: **v0.7.0** | Surface | Count | | --- | ---: | | Source-audited resources | 545 | | Operational patterns | 20 | | Validated loop contracts | 20 | | Runtime starters | 8 | The source audit dated **2026-07-17** reports these row statuses: **496 reachable public**, **1 access restricted**, **48 repository-native**, and **0 broken or unreachable**. ## Load The Data ```python from datasets import load_dataset resources = load_dataset( "cy0307/awesome-loop-engineering", "resources", split="train", ) papers = resources.filter(lambda row: row["resource_type"] == "Paper") verification = resources.filter( lambda row: "verification" in row["lifecycle_stages"].split(";") ) ``` For pandas: ```python import pandas as pd resources = pd.read_csv( "https://huggingface.co/datasets/cy0307/awesome-loop-engineering/resolve/main/data/resources.csv" ) ``` Use `url` as the durable join key. `row_id` and `source_line` are positional and can change when the README is reordered. ## Intended Uses - 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, executable examples, and copy/paste runtime templates. Do not use `signal_strength`, GitHub stars, forks, or inclusion in this collection as a quality label, endorsement, or automated ranking of scientific validity. ## Dataset Structure The primary configuration is `resources`, with one `train` split backed by `data/resources.jsonl`. `data/resources.csv` contains the same rows for spreadsheet and dataframe workflows. | Field group | Fields | What it describes | | --- | --- | --- | | Identity | `row_id`, `title`, `url`, `canonical_url`, `resource_type`, `domain` | What the resource is and where it lives. | | Editorial assessment | `annotation`, `key_contribution`, `novelty`, `impact` | Why the resource matters in this collection. | | Navigation | `section`, `collection`, `user_goal`, `lifecycle_stages`, `audience` | Where the resource fits and who it serves. | | Evidence | `evidence_class`, `signal`, `signal_strength`, `source_status`, `audited_at` | What kind of evidence or provenance is available. | | Publication | `authors`, `publication_date`, `publication_year`, `publication_venue`, `publisher`, `doi`, `arxiv_id`, `primary_category` | Bibliographic data exposed by the canonical source. | | Source provenance | `source_title`, `source_description`, `metadata_source`, `publication_note` | Where metadata came from and what caveats accompany it. | | Repository context | `github_repo`, `github_stars`, `github_forks`, `github_license`, `github_created_at`, `github_updated_at` | Point-in-time adoption and maintenance context for GitHub projects. | | Export trace | `source_readme`, `source_line`, `source_url`, `date_added` | How the row maps back to the canonical field guide. | The complete field-by-field schema is documented in [`data/README.md`](https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/data/README.md). ## Curation And Provenance The collection prioritizes primary papers, official documentation, project repositories, and implementation-heavy practitioner sources. Every released row must pass the public [curation standard](https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/meta/CURATION.md). 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). ## Limitations - The collection is maintained by one person and reflects editorial judgment despite a public acceptance standard. - Coverage is skewed toward English-language, publicly available material. - A successful URL check proves reachability at audit time, not correctness, permanence, or independent validation. - Access-restricted rows could not be fully retrieved during the latest audit and should be checked manually. - Publication dates and authors remain blank when the primary source does not expose reliable metadata. - Resource statistics are snapshots and will drift after the recorded `audited_at` timestamp. - Linked third-party works retain their own licenses and terms; CC0-1.0 covers only original repository curation, metadata, templates, and documentation. ## Versioning And Corrections 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 ```bibtex @misc{chaoyue2026awesome_loop_engineering, author = {He, Chaoyue}, title = {Awesome Loop Engineering}, year = {2026}, howpublished = {\url{https://github.com/ChaoYue0307/awesome-loop-engineering}}, note = {Version 0.7.0} } ```