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README.md

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+---
+dataset_info:
+  features:
+  - name: skill_id
+    dtype: string
+  - name: name
+    dtype: string
+  - name: description
+    dtype: string
+  - name: type
+    dtype: string
+  - name: task_prompt
+    dtype: string
+  - name: skill_document
+    dtype: string
+  - name: test_code
+    dtype: string
+  - name: repo_url
+    dtype: string
+  - name: repo_commit
+    dtype: string
+  - name: docker_image
+    dtype: string
+  splits:
+  - name: train
+    num_examples: 49
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: swe_skills_bench.jsonl
+language:
+- en
+license: mit
+task_categories:
+- text-generation
+tags:
+- code
+- software-engineering
+- benchmark
+- agents
+- skill-injection
+pretty_name: SWE-Skills-Bench
+size_categories:
+- n<1K
+---
+
+**Dataset Summary**
+
+SWE-Skills-Bench is a benchmark dataset for evaluating whether injected skill documents — structured packages of procedural knowledge — measurably improve LLM agent performance on real-world software engineering tasks.
+
+The dataset contains 49 skills spanning 565 task instances across six software engineering domains (Deployment & DevOps, Analytics & Monitoring, API Development, Data Science & ML, Security & Testing, and Developer Tools). Each skill is grounded in an authentic GitHub repository at a fixed commit, paired with a curated skill document and a deterministic pytest test suite that encodes the task's acceptance criteria.
+
+The dataset is designed to answer: *Does giving an agent a skill document actually help?* The primary evaluation metric is pytest pass rate, measured under two conditions — with and without skill injection — to compute a pass-rate delta (ΔP) per skill.
+
+The dataset was released as part of [SWE-Skills-Bench: Do Agent Skills Actually Help in Real-World Software Engineering?](https://arxiv.org/abs/2603.15401)
+
+**Dataset Structure**
+
+An example of a SWE-Skills-Bench datum is as follows:
+
+```
+skill_id:        (str) - Unique skill identifier, e.g. "fix", "tdd-workflow".
+name:            (str) - Human-readable task name.
+description:     (str) - One-line description of the task.
+type:            (str) - Task category, e.g. "repair", "feature", "fix".
+task_prompt:     (str) - Full task prompt passed to the agent (Markdown).
+skill_document:  (str) - Curated skill document injected as agent context (Markdown).
+test_code:       (str) - Pytest test suite used to evaluate the agent's output.
+repo_url:        (str) - Target GitHub repository URL.
+repo_commit:     (str) - Fixed commit hash for reproducibility.
+docker_image:    (str) - Pre-configured Docker image for the evaluation environment.
+```
+
+**Supported Tasks**
+
+SWE-Skills-Bench proposes a paired evaluation task: given a task prompt (with or without an injected skill document), an agent must complete a software engineering task on a real codebase. Correctness is verified by running the associated pytest test suite inside a Docker container.
+
+**Languages**
+
+The text of the dataset is in English.