| --- |
| license: apache-2.0 |
| language: |
| - en |
| - ko |
| tags: |
| - skill-retrieval |
| - information-retrieval |
| - agents |
| - benchmark |
| - retrieval |
| pretty_name: SkillRetBench |
| size_categories: |
| - n<1K |
| --- |
| |
| # SkillRetBench |
|
|
| ## Dataset summary |
|
|
| **SkillRetBench** is a benchmark for **query-to-skill retrieval**: given a natural-language user request, systems must rank or select the correct procedural skill document(s) from a fixed library. The corpus is derived from a real-world production-style agent skill library (**501** skills). The benchmark includes **1,250** queries with gold skill identifiers and optional distractors, spanning five evaluation settings (single skill, multi-skill composition, distractors, outdated/redundant, budget-constrained). Packaged **baseline experiment results** (BM25, dense, hybrid, LLM-style, and SADO-style runs) report standard IR metrics (Recall@k, nDCG@k, MRR, MAP) per setting. |
|
|
| SkillRetBench accompanies the **SEFO** framework (Self-Evolving Federated Orchestration) and targets research on skill-aware orchestration, large skill libraries, and retrieval under composition and noise. |
|
|
| ## Dataset structure |
|
|
| The release consists of three JSON artifacts (also listed in `croissant.json`): |
|
|
| | File | Description | |
| |------|-------------| |
| | `skill_corpus.json` | Full skill index: metadata, corpus statistics, and the `skills` array (one object per skill). | |
| | `skillretbench_queries.json` | Benchmark definition: `meta` (counts, seed, generator) and `queries` array. | |
| | `baseline_results.json` | Aggregated baseline metrics: per-method, per-setting IR scores and a markdown summary table. | |
|
|
| ### `skill_corpus.json` |
| |
| - **`meta`**: `generated_at` (ISO 8601), `skills_root`, `output_path`, `skill_file_count`. |
| - **`statistics`**: `total_skills`, `skills_per_category`, description length stats, `token_count_distribution`, composition graph stats (`skills_with_composition_references`, `total_composition_edges`). |
| - **`skills`** (array, length 501): each element includes: |
| - `skill_id` (string, canonical id) |
| - `skill_name` (string) |
| - `description` (string, short routing description) |
| - `trigger_phrases` (string array) |
| - `anti_triggers` (string array) |
| - `korean_triggers` (string array; may be empty) |
| - `category` (string) |
| - `full_text` (string, full SKILL.md-derived body) |
| - `token_count` (integer) |
| - `composable_skills` (string array; referenced skill ids) |
| - `parse_warnings` (string array) |
|
|
| ### `skillretbench_queries.json` |
| |
| - **`meta`**: `benchmark`, `generator`, `corpus_meta`, `total_queries` (1250), `counts_by_setting`, `rng_seed` (42). |
| - **`queries`** (array, length 1250): each element includes: |
| - `query_id` (string, e.g. `SS-0001`) |
| - `setting` (enum-like string): `single_skill` | `multi_skill_composition` | `distractor` | `outdated_redundant` | `budget_constrained` |
| - `query` (string; English and/or Korean natural language) |
| - `gold_skills` (string array; expected skill ids) |
| - `distractor_skills` (string array) |
| - `budget_tokens` (number or `null`) |
| - `difficulty` (string, e.g. `easy`, `medium`) |
| - `source` (string, e.g. `trigger_paraphrase`) |
|
|
| **Query counts by setting** |
|
|
| | Setting | Count | |
| |---------|------:| |
| | `single_skill` | 400 | |
| | `multi_skill_composition` | 200 | |
| | `distractor` | 300 | |
| | `outdated_redundant` | 150 | |
| | `budget_constrained` | 200 | |
|
|
| ### `baseline_results.json` |
| |
| - **`timestamp`**, **`meta`**: `corpus_skills`, `queries_total`, `queries_by_setting`, `dense_backend` (e.g. `jaccard_fallback`). |
| - **`baselines`**: nested object — top-level keys **`BM25`**, **`Dense`**, **`Hybrid`**, **`NaiveLLM`**, **`SADO`**. Under each method, keys match query `setting` names; each leaf holds metrics: `recall@1`, `recall@3`, `recall@5`, `recall@10`, `ndcg@1`, `ndcg@3`, `ndcg@5`, `ndcg@10`, `mrr`, `map` (floats). |
| - **`summary_table`**: pre-rendered markdown table (macro-style summary over settings). |
| |
| > **Note:** As documented in the artifact, `NaiveLLM` and `SADO` entries may be **simulated** (no live API) for reproducible baselines; see project script `run_baselines.py`. |
|
|
| ## Supported tasks |
|
|
| - **skill-retrieval** — Rank or classify skills given a query; evaluate with gold `gold_skills` and IR metrics. |
| - **information-retrieval** — Same retrieval formulation; comparable to passage/tool retrieval with long procedural documents. |
|
|
| ## Languages |
|
|
| - **English** — Majority of skill text and many queries. |
| - **Korean** — Present in `korean_triggers`, many `query` strings, and mixed Korean/English skill descriptions. |
|
|
| ## Dataset creation |
|
|
| ### Sources |
|
|
| - Skills were built from **Cursor agent `SKILL.md` files** under a single repository’s `.cursor/skills` tree (501 files), parsed into structured records plus full markdown bodies. |
| - Queries were **programmatically generated** by `app.sefo.benchmark.query_generator` with fixed **`rng_seed`: 42** for reproducibility. |
| |
| ### Methodology (high level) |
| |
| 1. **Corpus build** — Parse frontmatter and body; extract triggers, categories, composition edges, and token counts. |
| 2. **Query generation** — Instantiate templates and paraphrases aligned to skills and settings (single vs. multi-skill, distractors, outdated/redundant, token budget hints). |
| 3. **Baselines** — Run packaged retrievers/rankers (lexical, dense with configured fallback, hybrid, simulated LLM/SADO-style) and aggregate metrics per setting. |
| |
| ## Considerations |
| |
| ### Biases |
| |
| - **Corpus domain** — Skills reflect one organization’s automation stack (dev tooling, PM, trading, cloud, etc.); frequency and vocabulary are **not** uniformly representative of all industries or locales. |
| - **Authoring style** — Descriptions follow a consistent “when to use / do not use” pattern; models may exploit **format bias** rather than deep semantics. |
| - **Query generation** — Synthetic and template-driven queries may **over- or under-represent** realistic user phrasing compared to production logs. |
| |
| ### Limitations |
| |
| - **Scale** — 501 skills is medium-scale vs. ecosystems with 10⁵+ tools; retrieval hardness may not transfer linearly. |
| - **Gold labels** — Defined by benchmark construction rules; edge cases and valid alternative skill sets may exist. |
| - **Static snapshot** — Skill text and ids are frozen for the benchmark revision tied to `meta.generated_at` in the JSON files. |
| - **Baseline simulation** — Some methods are noted as simulated in-code; compare only under the same harness and seeds. |
|
|
| ## License |
|
|
| This dataset is released under the **Apache License 2.0**. See [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0). |
|
|
| ## Citation |
|
|
| If you use SkillRetBench or this corpus, please cite: |
|
|
| ```bibtex |
| @misc{han2026sefo, |
| title = {{SEFO}: Self-Evolving Federated Orchestration with Trusted Skill Governance and Skill Retrieval Benchmark for Recursive Agentic Systems}, |
| author = {Han, Hyojung and {ThakiCloud AI Research}}, |
| year = {2026}, |
| howpublished = {\url{https://github.com/hyojunguy/ai-model-event-stock-analytics}}, |
| note = {Includes SkillRetBench: 501-skill corpus and 1,250-query retrieval benchmark} |
| } |
| ``` |
|
|
| Adjust `howpublished` / venue fields when a formal publication DOI is available. |
|
|
| ## Dataset repository layout (recommended) |
|
|
| After upload to the Hugging Face Hub, a typical layout is: |
|
|
| ``` |
| skill_corpus.json |
| skillretbench_queries.json |
| baseline_results.json |
| croissant.json |
| README.md |
| ``` |
|
|
| ## Additional documentation |
|
|
| - Croissant (ML Commons) metadata: **`croissant.json`** in this folder. |
| - Implementation and regeneration: `backend/app/sefo/benchmark/` in the source repository. |
|
|
