Skip to content
cccskills
FreePublished on GitHub

behavior-1k

CPU-only operating guidance for the BEHAVIOR-1K BDDL 3.7.0 package: symbolic activity definitions, condition parsing/evaluation, object taxonomy, and generated-data-backed knowledge-base inspection.

The English summary is being prepared. Showing the original description.

See how to install

Files included (14)

  • SKILL.md3.1 KB
  • references/api-overview.md3.5 KB
  • references/repo-provenance.md1.8 KB
  • references/repo-routing-metadata.json421 B
  • references/troubleshooting.md4.5 KB
  • scripts/smoke_bddl_install.py3.1 KB
  • sub-skills/knowledge-base/references/api-reference.md15.4 KB
  • sub-skills/knowledge-base/references/troubleshooting.md10.8 KB
  • sub-skills/knowledge-base/scripts/inspect_bddl_taxonomy.py15.0 KB
  • sub-skills/knowledge-base/SKILL.md3.0 KB
  • sub-skills/symbolic-tasks/references/api-reference.md18.2 KB
  • sub-skills/symbolic-tasks/references/troubleshooting.md14.6 KB
  • sub-skills/symbolic-tasks/scripts/inspect_bddl_activity.py10.9 KB
  • sub-skills/symbolic-tasks/SKILL.md2.1 KB

SKILL.md (original)

Review the instructions the agent will receive before you install.

BEHAVIOR-1K BDDL

Use this skill when a Researcher needs the BDDL portion of BEHAVIOR-1K: activity-definition lookup, parsing, symbolic condition reasoning, object synset/category taxonomy, or read-only knowledge-base inspection.

Route by task

  • Activities, .bddl files, predicates, initial/goal conditions, scope, grounding, or symbolic evaluation: read symbolic-tasks.
  • Synsets, categories, substances, abilities, generated metadata, or KnowledgeBase models: read knowledge-base.
  • Package compatibility, shared installation failures, or scope limits: read API overview and troubleshooting.

Supported contract

This generated skill intentionally covers only CPU-verifiable BDDL package workflows from the pinned repository snapshot. Install the public distribution in a compatible Python environment:

python -m pip install "bddl==3.7.0"
python -c "from importlib.metadata import version; import bddl; print(version('bddl'))"

The package includes activity/domain definitions and generated taxonomy data as package-relative runtime data. Use the bundled read-only inspectors for quick checks, then consult the sub-skill API references for programmatic workflows:

python sub-skills/symbolic-tasks/scripts/inspect_bddl_activity.py --help
python sub-skills/knowledge-base/scripts/inspect_bddl_taxonomy.py --help
python scripts/smoke_bddl_install.py --help

Hard boundary

This skill does not cover OmniGibson, Isaac Sim, simulator/environment execution, object-state runtime behavior, robots, controllers, sensors, action primitives, cuRobo, GPU backends, physical hardware, asset production, benchmark orchestration, or the excluded BEHAVIOR-1K monorepo packages. A symbolic BDDL condition is not evidence that a simulator can instantiate or satisfy that condition. The source snapshot's OmniGibson/Isaac Sim workflows were not generated or verified because the required runtime was unavailable; do not route such requests here as if CPU guidance were a substitute.

The generated-data files are runtime inputs. Do not run maintainer-only data_generation pipelines as a routine repair or query method. Use the troubleshooting references when package data is missing or inconsistent.

Verification entry point

For a quick, read-only package/data smoke check, run scripts/smoke_bddl_install.py. For activity-level and taxonomy-level validation, use the inspectors linked above. Keep all simulator-dependent claims outside this skill.

See provenance for the source revision and evidence overview for the retained package surface.

Reviews

No reviews yet. Tell others what you thought.

Comments

No login needed. Share how it worked for you, ask a question, or note where it did not run.

No comments yet. Be the first to write one.

Comments are published right away. Do not include personal information such as email addresses or phone numbers. Up to 2 URLs.

0 / 1,000 characters

This form is protected by reCAPTCHA, and the Google Privacy Policy and Terms of Service apply.

More skills from this repository

Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.

The English summary is being prepared. Showing the original description.

VectorSpaceLab/AREX-Skill339Updated Sep 3, 2026

3ddfa

Free

Guide 3DDFA Python inference, geometry rendering, training/evaluation, and optional C++ ONNX workflows for 3D dense face alignment.

The English summary is being prepared. Showing the original description.

VectorSpaceLab/AREX-Skill339Updated Sep 3, 2026

Routes 3DDFA_V2 face-alignment setup, still-image demos, video tracking, and ONNX benchmarking workflows.

The English summary is being prepared. Showing the original description.

VectorSpaceLab/AREX-Skill339Updated Sep 3, 2026

Operate AB3DMOT 3D multi-object tracking workflows for KITTI and nuScenes data, tracking, evaluation, and visualization.

The English summary is being prepared. Showing the original description.

VectorSpaceLab/AREX-Skill339Updated Sep 3, 2026

Use Hugging Face Accelerate for PyTorch training-loop migration, distributed launch/configuration, DeepSpeed/FSDP/TPU backend setup, big-model inference/offload, checkpointing, tracking, and troubleshooting.

The English summary is being prepared. Showing the original description.

VectorSpaceLab/AREX-Skill339Updated Sep 3, 2026

acme

Free

Route Acme reinforcement-learning framework tasks across core loops, replay/data, JAX agents, and TensorFlow/Sonnet agents.

The English summary is being prepared. Showing the original description.

VectorSpaceLab/AREX-Skill339Updated Sep 3, 2026

See all skills by VectorSpaceLab

Report a problem with this skill