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README.md

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+---
+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and optional distractors, spanning five evaluation settings (single skill, multi-skill composition, distractors, outdated/redundant, budget-constrained).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.