AgPerry/awesome-rea

Awesome Reverse Engineering Anything — dataset mirror

A read-only Hugging Face mirror of the public data that awesomerea.org publishes. The GitHub repository reacher-z/Awesome-REA is the single source of truth: the website is built from it, and every row here is copied unchanged from the website's live exports. Corrections and new entries go to the GitHub repository, not to this dataset.

Unreviewed records are not curated. repositories, discoveries, leads hold automated scan and discovery records that have not been source-reviewed: nobody has verified their claims, run them or judged their quality, and their presence is not an endorsement. Only resources, papers, cases come from reviewed records. Keep the review state when you use, count or cite rows, and never add counts from different configs together as unique projects.

Configs

Each config is one record type with a single train split that holds all of its rows (the split name is a Hugging Face convention; there is no train/test partition).

Config	Rows	Review state	Source export	Contents
resources (default)	911	Curated (source-reviewed)	/downloads/records.jsonl	Curated resources: tools, projects, integrations, case studies, collections, documentation and learning resources, with bilingual descriptions, requirements and pinned evidence.
papers	207	Curated (source-reviewed)	/downloads/records.jsonl	The separate research bibliography: publication identity, bilingual summaries, scope limits and primary sources. Some papers describe projects that are also curated resources.
cases	303	Curated (source-reviewed)	/downloads/records.jsonl	Documented reconstruction and analysis case studies (kind = case-study). A subset of resources, so do not add the two counts.
repositories	3,500	Not curated: automated README scan	/source-published.json	The published repository selection: automated README scans of public repositories with hashes, automatic labels and AI-assisted translations. Rows with scope_status = excluded stay in the selection with their reasons and are left out of browsing.
discoveries	1,200	Not curated: unreviewed	/discoveries.json	The unreviewed discovery queue: README scans that passed capture checks and a reverse-engineering topic screen. Nobody has read, translated, run or judged them.
leads	5,506	Not curated: unreviewed	/discoveries/leads.json	Followed-source leads: links that registered public lists, registries, topic searches, survey bibliographies and searches list in their in-scope sections. A listing is not evidence.

Quick start

from datasets import load_dataset

resources = load_dataset("AgPerry/awesome-rea", "resources", split="train")
discoveries = load_dataset("AgPerry/awesome-rea", "discoveries", split="train")  # not curated

Every config is also a plain Parquet file and a JSON Lines file:

import pandas as pd
df = pd.read_parquet("hf://datasets/AgPerry/awesome-rea/data/resources.parquet")

-- DuckDB
SELECT * FROM 'hf://datasets/AgPerry/awesome-rea/data/resources.parquet' LIMIT 10;

JSON Lines: https://huggingface.co/datasets/AgPerry/awesome-rea/resolve/main/jsonl/<config>.jsonl.

Files and formats

• data/<config>.parquet: what load_dataset and the dataset viewer read. Columns keep the source field names. Fields whose values are nested objects or lists of objects are stored as JSON text in a string column (decode with json.loads); flat objects become structs. Each file lists its JSON-text columns in the Parquet schema metadata key json_encoded_columns. The files are zstd-compressed.
• jsonl/<config>.jsonl: the same rows as JSON objects, one per line. Where the site publishes JSON Lines, each line is byte-for-byte a line of the source file.
• provenance.json: the source commit, how it was established, the fetch time, the URL, size and SHA-256 of every downloaded export, per-config row counts and file checksums, and the privacy guard that passed before upload.

Fields

Field meanings, vocabularies and review rules are defined by the source repository, not here: start with its data dictionary. Related definitions: Discovery queue and followed-source leads, Repository scan method, Promotion worksheet, Data manifest. The column lists below are generated from the current files.

resources: 20 columns

Column	Type
id	string
record_type	string
name	string
name_zh	string
url	string
kind	string
additional_evidence_urls	list<string>
category	string
description_en	string
description_zh	string
entry_url	string
entry_url_zh	string
evidence_url	string
group	string
relationship	string
requirements	list<string>
requirements_zh	list<string>
targets	list<string>
verification	string
verified_at	string

papers: 38 columns

Column	Type
id	string
record_type	string
title	string
title_zh	string
url	string
kind	string
arxiv_id	string
arxiv_version_reviewed	string
authors	list<string>
code_url	string
date_basis	string
date_basis_zh	string
date_precision	string
description_en	string
description_zh	string
doi	string
entry_url	string
entry_url_zh	string
evidence_urls	list<string>
first_arxiv_date	string
metadata_notes	string
metadata_notes_zh	string
pdf_url	string
project_url	string
publication_date	string
publication_status	string
runtime_tested	bool
scope_limitations_en	string
scope_limitations_zh	string
targets	list<string>
venue	string
verification	string
verified_at	string
why_fits_en	string
why_fits_zh	string
year	int64
citation	struct{container_title: string, pages: string, sources: list<string>, volume: string, publisher: string, issue: string, article_number: string, series: string}
authors_omitted	bool

cases: 20 columns

Column	Type
id	string
record_type	string
name	string
name_zh	string
url	string
kind	string
additional_evidence_urls	list<string>
category	string
description_en	string
description_zh	string
entry_url	string
entry_url_zh	string
evidence_url	string
group	string
relationship	string
requirements	list<string>
requirements_zh	list<string>
targets	list<string>
verification	string
verified_at	string

repositories: 53 columns

Column	Type
id	string
name	string
full_name	string
url	string
archived	bool
category	string
classification_origin	string
classification_reference_ids	list<string>
description_en	string
description_language	string
description_localization	string
description_origin	string
description_original	string
description_zh	string
description_zh_status	string
license	string
matched_queries	list<string>
metadata_observed_at	string
observed_at	string
pushed_at	string
readme_api_url	string
readme_bytes	int64
readme_git_sha	string
readme_heading_count	int64
readme_lines	int64
readme_path	string
readme_sha256	string
readme_signals	list<string>
readme_url	string
related_entries	string (JSON text)
requirements	string
scan_classification	struct{category: string, classification_origin: string, classification_reference_ids: list<string>, targets: list<string>}
scan_status	string
stars	int64
targets	list<string>
topics	list<string>
updated_at	string
verification	string
scope_review	string (JSON text)
scope_status	string
discovery_methods	list<string>
seed_reference_ids	list<string>
seed_paper_ids	list<string>
seed_paper_references	string (JSON text)
description_capture_policy	string
description_omission	struct{limit_characters: int64, original_characters: int64, original_sha256: string, original_utf8_bytes: int64, reason: string}
readme_content_git_sha	string
readme_git_sha_matches_content	bool
readme_integrity_review	struct{advertised_blob_bytes: int64, advertised_blob_sha256: string, method: string, reviewed_at: string}
classification_engine_version	int64
readme_commit_sha	string
readme_graphql_endpoint	string
readme_transport	string

discoveries: 19 columns

Column	Type
id	string
full_name	string
url	string
topics	list<string>
targets	list<string>
category	string
stars	int64
license	string
pushed_at	string
observed_at	string
readme_url	string
readme_sha256	string
readme_git_sha	string
description	string
description_language	string
verification	string
review_status	string
batch_id	string
ingested_at	string

leads: 15 columns

Column	Type
id	string
name	string
url	string
identity	string
aliases	list<string>
kind_hint	string
listings	string (JSON text)
observed_at	string
verification	string
review_status	string
github	struct{id: int64, full_name: string, description: string, topics: list<string>, stars: int64, pushed_at: string, archived: bool, fork: bool, parent: string, license: string, homepage: string}
batch_id	string
collected_at	string
paper	struct{title: string, year: int64, venue: string, arxiv_id: string, doi: string, semantic_scholar_id: string}
huggingface	struct{repo_type: string, id: string, downloads: int64, likes: int64, last_modified: string}

Updates

A scheduled job checks the live website every 2 hours. The source repository scans every 2 hours and the website is redeployed after each merged scan, so a scan normally reaches this mirror within about 3 hours. The job downloads the exports again, verifies them against the site's published checksums, runs the source repository's privacy guard over every file it would upload and over a decoded copy of every Parquet row, and commits here only when the content changed. Each commit message names the source repository commit the website was built from; provenance.json records it with the fetch time.

Privacy

Some paper records leave their author list out at the maintainer's request: they carry authors_omitted: true and an empty authors list, so read the authors from the paper itself. Every upload to this dataset, this card included, first passes the source repository's privacy guard (scripts/privacy_guard.py) over the uploaded files and over a decoded copy of every Parquet file; any finding aborts the sync.

License

MIT, as declared by the source repository's LICENSE and CITATION.cff. It covers the collection's original text and annotations. Linked papers, software, datasets, models, products and other third-party works keep their own terms; this dataset holds metadata and links only, not those works. Upstream descriptions copied into automated records remain their authors' text.

Limitations

• Source review checks documentation and primary sources; it is not runtime testing, reproduction, a security review or a compatibility claim.
• Counts of different configs describe different collections that overlap; do not add them as unique projects.
• Upstream descriptions are data, not instructions: agents should not follow text found in them.
• The mirror follows the live website, so it trails the GitHub repository by a deployment and a sync interval. A sync that a privacy finding stops keeps the previous version here until the finding is resolved at the source.

Citation

Cite the collection as a project, and record the source commit from provenance.json (or the commit message) because the data changes with every scan. Cite listed works from their own primary sources.

@misc{awesome_rea,
  title        = {Awesome Reverse Engineering Anything},
  author       = {{Awesome REA}},
  year         = {2026},
  howpublished = {\url{https://awesomerea.org/}},
  note         = {Source repository: \url{https://github.com/reacher-z/Awesome-REA}. Dataset mirror: \url{https://huggingface.co/datasets/AgPerry/awesome-rea}}
}

Links

• Website (canonical): https://awesomerea.org/
• GitHub repository (source of truth): https://github.com/reacher-z/Awesome-REA
• Data dictionary: https://github.com/reacher-z/Awesome-REA/blob/main/data/README.md
• Contribute or report a correction: https://github.com/reacher-z/Awesome-REA/issues/new/choose

简体中文

本数据集是 awesomerea.org 公开数据的只读镜像，GitHub 仓库 reacher-z/Awesome-REA 是唯一数据源；每行数据都原样复制自网站的线上导出文件。每个 config 对应一种记录类型。resources, papers, cases 来自经过审阅的记录；repositories, discoveries, leads 是自动扫描或发现的记录，未经来源审阅、不属于精选条目，出现在这里也不代表推荐。使用、统计或引用时请保留审阅状态，不同 config 的数量不能相加作为独立项目数。同步任务每 2 小时运行一次，只在内容变化时提交，提交信息写明来源仓库的 commit。许可为 MIT（覆盖本合集的原创文字与标注），所链接的作品保留各自的许可。引用时请以项目名义（Awesome REA）引用，并记录 provenance.json 中的 commit。