| --- |
| 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). |
|
|
