ecnu-icalk/SkillGym

SkillGym Dataset

Human-written skills, executable environments, and verified long-horizon agent trajectories

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GitHub · SkillGym-Agent · Paper

Dataset Summary

SkillGym is the data release accompanying the SkillGym framework. It connects human-written procedural knowledge to executable task environments, code-based outcome verifiers, paired with-skill / without-skill executions, and long-horizon agent trajectories.

The dataset is intended to support research on skill-grounded task construction, agent training, verifier-based evaluation, procedural transfer, and outcome-based learning.

Statistic	Value
Accepted environments	2,756
Paired task variants	5,512
Skill-Dep. / Verifier-Passed	1,081 / 1,675
Successful trajectories	8,364
Sampled trials	48,152
Overall sampled-trial success rate	17.4%
Deduplicated task coverage	2,302 unique tasks
Taxonomy	12 major / 63 sub-categories

Successful trajectories cover 62 of the 63 sub-categories represented by accepted environments.

Released Artifacts

Artifact	Contents	Use it for
Tasks.tar.zst	Published skill-derived task environments, paired variants, and verifiers	Re-running or studying executable tasks
skill_library.tar.zst	Human-written skills and supporting assets	Skill analysis or new task construction
task_templates.tar.zst	Reusable seed-task templates and fixtures	Extending the task-construction pipeline
Trajectories/	Eight trajectory collections organized by result type, harness, and teacher model	SFT, trajectory analysis, and procedural-learning research

Release Snapshot

The large-artifact dataset snapshot is associated with GitHub commit 6ebabba.

The GitHub codebase, dataset repository, and model repository are versioned independently. For exact reproduction, record the revision used from each repository.

Quick Start

Download Trajectory Collections

python -m pip install -U huggingface_hub datasets

hf download ecnu-icalk/SkillGym \
  --include "Trajectories/*.jsonl" \
  --repo-type dataset \
  --local-dir .hf/skillgym

Stream One Collection with 🤗 Datasets

from datasets import load_dataset

records = load_dataset(
    "json",
    data_files={
        "train": (
            "hf://datasets/ecnu-icalk/SkillGym/"
            "Trajectories/"
            "skill_dependent_claude_code_deepseek_v4_pro.jsonl"
        )
    },
    split="train",
    streaming=True,
)

example = next(iter(records))
print(example["session_id"])

Download Task-Construction Artifacts

hf download ecnu-icalk/SkillGym \
  Tasks.tar.zst skill_library.tar.zst task_templates.tar.zst \
  --repo-type dataset \
  --local-dir .hf/skillgym

For building new environments, use the SkillGym Task Builder.

Dataset Organization

SkillGym/
├── Tasks.tar.zst
├── skill_library.tar.zst
├── task_templates.tar.zst
└── Trajectories/
    ├── skill_dependent_*.jsonl
    └── verifier_passed_*.jsonl

The task archives contain the source skill/task relationship, task instructions, environment configuration, task-specific verification logic, and paired execution variants used during construction and validation.

Trajectory collections preserve agent interaction traces and verification outcomes for successful sampled executions.

Terminology

Context	Skill available	Skill removed
Paper / benchmark tables	w/ Skills	w/o Skills
Released dataset variants	withskill	withoutskill
Task Builder internals	with_skill	no_skill

These names refer to the same paired evaluation concept; the differences reflect presentation, released-storage, and implementation conventions.

Task Labels and Paired Variants

SkillGym distinguishes task-construction labels from trajectory-level outcomes.

Skill-Dep.

A task is labeled Skill-Dep. when, under the reference construction configuration:

1. the environment and verifier are valid;
2. the paired with-skill execution succeeds; and
3. the paired without-skill execution produces a valid reward failure.

Verifier-Passed

A Verifier-Passed task has a valid executable environment and outcome verifier but does not satisfy the stricter contrastive Skill-Dep. criterion under the reference configuration.

These labels characterize accepted environments at construction time. They do not guarantee that every later model or harness will succeed or fail in the same way.

Trajectory Data

Successful trajectories retain the interaction sequence that led to a verifier-confirmed outcome. Depending on the source harness, records may include:

• task and session identifiers;
• task prompt and skill metadata;
• environment/runtime metadata;
• actions and tool calls;
• observations and tool outputs;
• final outputs or artifacts;
• verifier results and execution logs;
• available diagnostic fields.

Schema Note

The eight trajectory files preserve source-specific trace structures. Some nested fields differ across harness/model collections, so the release should not be assumed to have one perfectly uniform nested JSON schema across every file.

For the most robust loading behavior:

• load one trajectory collection at a time; or
• explicitly normalize compatible files before concatenating them.

Using an explicit data_files path, as in the streaming example above, is preferable to treating every trajectory JSONL file as a single homogeneous split.

Trajectory Statistics

Statistic	Value
Successful trajectories from Skill-Dep. tasks	3,722
Successful trajectories from Verifier-Passed tasks	4,642
Explicit target-skill invocation	3,077 (36.8%)
Average tool calls per successful trajectory	49.0
Average logged text tokens	63.4k
Average interaction steps	35.2
Corpus-wide maximum tool calls	350
Corpus-wide maximum logged text tokens	342.9k
Corpus-wide maximum interaction steps	318

The maxima above are corpus-wide maxima for their respective measures and need not come from the same trajectory. Logged/saved text-token counts are dataset statistics and should not be interpreted as API billing usage or as a single-request context length.

Collection Configurations

Harness	Teacher model	Successful trajectories
Claude Code	DeepSeek V4 Pro	1,722
Claude Code	GLM-5.2	1,769
Codex	GPT-5.4	1,967
Codex	Nex-N2-Pro	2,906

This diversity broadens the interaction traces beyond one agent configuration, but the released trajectory distribution should not be treated as representative of every model, harness, task domain, or deployment setting.

Intended Uses

SkillGym can support research on:

• supervised fine-tuning from verified agent trajectories;
• agentic problem solving and long-horizon workflow learning;
• skill-conditioned versus skill-free execution;
• executable-environment and verifier-based evaluation;
• outcome-based reinforcement learning;
• task-generation and verifier design;
• trajectory analysis and procedural failure modes.

Limitations

• Skill-Dep. and Verifier-Passed are reference-configuration labels, not universal properties.
• Category coverage is broad but uneven.
• Trajectory collections are generated by a finite set of harness–model configurations.
• Successful trajectories are a selected subset of sampled executions and should not be interpreted as an unbiased behavioral distribution.
• Some trajectory files preserve harness-specific schema differences.
• Source skills, fixtures, and supporting assets may carry upstream notices or additional licensing requirements.

Related Resources

Resource	Scope
GitHub / SkillGym	Task Builder, construction pipeline, documentation, and paper materials
SkillGym-Agent	Released All Teachers checkpoint trained on successful SkillGym trajectories
Task Builder guide	Building and validating new skill-grounded environments
Paper	Full method, dataset analysis, experiments, and ablations

Citation

@misc{skillgym2026,
  title = {Internalizing Large-Scale Human-Written Skills into LLMs for Real-World Problem Solving},
  year  = {2026},
  url   = {https://github.com/ECNU-ICALK/SkillGym}
}

License

The dataset is marked license: other because it aggregates artifacts that may retain distinct upstream notices or additional terms.

Repository-level SkillGym code is released under the MIT License. The SkillGym-Agent checkpoint is released separately under Apache-2.0.

When redistributing source skills, fixtures, or supporting assets, preserve the corresponding upstream notices and licensing terms.