--- pretty_name: SkillScreen Data license: other license_name: mixed-source-terms license_link: https://huggingface.co/datasets/SkillScreen/SkillScreen-Data#sources-and-terms task_categories: - text-classification configs: - config_name: markdown_random default: true data_files: - split: train path: data/markdown_random/train.parquet - split: validation path: data/markdown_random/validation.parquet - split: test path: data/markdown_random/test.parquet - config_name: markdown_sd data_files: - split: train path: data/markdown_sd/train.parquet - split: validation path: data/markdown_sd/validation.parquet - split: test path: data/markdown_sd/test.parquet - config_name: package_random data_files: - split: train path: data/package_random/train.parquet - split: validation path: data/package_random/validation.parquet - split: test path: data/package_random/test.parquet - config_name: package_sd data_files: - split: train path: data/package_sd/train.parquet - split: validation path: data/package_sd/validation.parquet - split: test path: data/package_sd/test.parquet --- # SkillScreen Data Agent-skill text, labels and source records for training decision models. Choose Markdown alone or Markdown with companion files, with the same case IDs. **v0.1** provides four full-text configurations in [SkillScreen/SkillScreen-Data](https://huggingface.co/datasets/SkillScreen/SkillScreen-Data). [GitHub](https://github.com/JerroldH/SkillScreen/tree/community-v0.1) keeps the index, export scripts and contribution workflow. ## Load the data Install `datasets` and choose an input view and split protocol. No upstream recovery, GPU, API key or custom dataset code is needed. ```python from datasets import load_dataset ds = load_dataset("SkillScreen/SkillScreen-Data", "package_sd") sample = ds["train"][0] skill_text = sample["skill_text"] companion_files = sample["files"] # [{"path": ..., "text": ..., "sha256": ...}] label = sample["label"] # 0 = benign, 1 = malicious ``` For a downloaded copy, replace the repository name with the local dataset folder. Each configuration contains `train`, `validation` and `test` sets. | Configuration | Input | Train | Validation | Test | | --- | --- | ---: | ---: | ---: | | `markdown_random` | SKILL.md | 9,461 | 1,350 | 2,703 | | `markdown_sd` | SKILL.md | 10,607 | 1,516 | 1,364 | | `package_random` | SKILL.md + eligible companion text | 8,290 | 1,178 | 2,363 | | `package_sd` | SKILL.md + eligible companion text | 9,563 | 1,369 | 874 | The pool has **13,514 records**, comprising 7,500 malicious and 6,014 benign skills. **11,831 packages** have verified complete inventories. Of those, 7,036 have eligible companion text and 4,795 have no additional eligible text. Files are stored as data inside Parquet, never installed or executed. Treat their instructions as untrusted input. ## Inputs and provenance | Column | Meaning | | --- | --- | | `id`, `group_id` | Stable normalized-Markdown content group | | `label` | ClassLabel, 0 benign / 1 malicious | | `skill_text`, `skill_sha256` | Exact canonical Markdown and its UTF-8 hash | | `files` | Companion paths, decoded text and hashes, sorted by path; empty in Markdown configs | | `coverage` | Original package availability, independent of the selected input mode | | `source_id`, `source_ids`, `source_name` | Primary source collection and all public source aliases | | `source_url`, `revision`, `member_ids` | Fixed source version and original public record IDs | | `label_origin`, `evidence` | Origin and basis of the reference label | | `upstream_license`, `package_source` | Source terms and the selected package's URL, revision and terms | Use `skill_text` and, for package models, `files` as model inputs. Labels, source names and evidence are metadata. Canonical SKILL.md is identical across the two input views. Package text includes eligible scripts, manifests and documents. Binary files and benchmark annotations are excluded; runtime dependencies are not bundled. Models choose their own context limit. The accompanying `records.jsonl` contains full inventories, original byte hashes, public ID mappings and recovery locations. Its text fields stay null because text lives in Parquet. `sources.json` pins the download assets. `splits/` preserves the original assignment CSVs, including exclusions. ## Splits and labels Grouped random is source/label-stratified with seed 42 and keeps normalized duplicates together. SD holds out SkillHarm (SRC009), ATR (SRC011) and SkillFortifyBench (SRC012). Train and validation may share source collections. These groups refer to benchmark collections, not individual repositories. **The existing SD assignment is unchanged.** Its Markdown test has 838 malicious and 526 benign cases. Twenty-seven excluded IDs remain in the index and split CSV, outside the SD Parquet sets. Package configs select complete packages within each existing partition without reshuffling. The package SD test has 784 malicious and 90 benign cases, reflecting incomplete ATR package coverage. For paired comparisons, filter Markdown to the IDs in the corresponding package partition before comparing models. These community splits are separate from the paper's SBR/MSD/SD protocols. Existing checkpoints used earlier protocols, so these partitions are not fresh held-out tests for those checkpoints. The original paper artifacts are unchanged. Labels are inherited upstream references. Identical normalized Markdown shares one ID. Six content groups with unresolved label questions are withheld. The pool contains no private labels or weak positives. Additional coverage is 1,634 Markdown-only, 25 partial and 24 ambiguous records. Ambiguous package versions are not combined. ## Build or audit a local copy From the code repository with Python 3.12+, restore the fixed source data once, then build and verify all four configs. Use a new output directory. ```sh python -m pip install -r requirements/data.txt python scripts/restore_community.py --mode markdown --output outputs/community-markdown.jsonl python scripts/restore_community.py --mode package --output outputs/community-packages.jsonl python scripts/export_community.py build --markdown-text outputs/community-markdown.jsonl --package-text outputs/community-packages.jsonl --output outputs/hf-fulltext ``` `build` also runs `load_dataset` for every config and verifies every ID, label, eligible text hash and split membership. It writes `verification.json` in the export folder and never uploads. Verify an existing export with: ```sh python scripts/export_community.py verify --output outputs/hf-fulltext ``` Recovery is an optional provenance check for maintainers, not a step required by users loading a generated release. Downloads are cached as inert hash blobs. No source file is executed. The index-only export remains available through `python scripts/community_data.py export --output outputs/hf-index`. ## Contribute Start with a public source URL, fixed revision and evidence through the [case form](https://github.com/JerroldH/SkillScreen/issues/new?template=case.yml). Maintainers check labels, duplicates, coverage and terms. Unknown cases stay out of supervised sets. New cases remain excluded from frozen protocols until the next release. Markdown and package views are generated from one shared index. ## Sources and terms - [MaliciousSkillBench](https://huggingface.co/datasets/ProtectSkills/MaliciousSkillBench/tree/d4b42ce5766a6e0359c987cf59c1007cb3795a90) supplies benchmark labels and Markdown. Its metadata and splits use CC BY 4.0; third-party skill contents retain their original terms. - [MalSkillBench](https://github.com/lxyeternal/MalSkillBench/tree/3548e2837adf58b96497f2c688f96beff26485da) supplies additional benign packages and SRC001 cases. Its notice states academic research use only. - [SkillTrustBench](https://huggingface.co/datasets/cuhk-zhuque/SkillTrustBench/tree/f90517b7058fdcfea89af114c069fbf973f42bc7) supplies SRC010 packages under its CC BY-NC-SA 4.0 notice and retained upstream terms. - [SkillHarm](https://huggingface.co/datasets/osunlp/SkillHarm/tree/cc21f90982ea49de43bc30a08f338788a92916f4) supplies SRC009 cases; its dataset card lists CC BY 4.0. - [SkillFortifyBench](https://github.com/qualixar/skillfortifybench/tree/eb9d5a9cbfa13b82ec3392d26dbae82fc6454b39) supplies native malicious and benign cases under MIT. - [Official package archives](https://github.com/protectskills/MaliciousSkillBench/tree/893afd609be22a2dfac5a2b94bbebb4eeb99515e/packages) provide fixed package snapshots without relicensing their contents. Our original documentation and annotations use CC BY 4.0. Imported bodies retain their source terms; the project code's Apache-2.0 license does not apply to them. `unknown` means a record's original content license has not been resolved. The dataset does not grant additional rights over imported text. Please cite the source datasets you use and the [SkillScreen study](https://arxiv.org/abs/2610.17727).