---

name: pyod description: "Operate PyOD anomaly-detection workflows across classic detectors, ADEngine automation, specialized modalities, model operations, and source maintenance." disable-model-invocation: true metadata: disco-role: operating license: BSD 2-Clause

PyOD Repo Skill

Use this skill when a task involves PyOD, Python outlier/anomaly detection, pyod.models, ADEngine, PyOD's agentic od-expert skill, PyOD MCP tools, model persistence, thresholding, optional detector extras, or maintaining the PyOD source repository.

PyOD is a Python anomaly-detection library with a classic fit/predict API, ADEngine lifecycle orchestration, and optional routes for time series, graph, text/image/audio embeddings, neural detectors, agent skills, and MCP.

Install and quick check

pip install pyod
pyod info
python - <<'PY'
import pyod
from pyod.models.iforest import IForest
print(pyod.__version__, IForest)
PY

Read references/installation-and-extras.md when choosing optional extras. Use scripts/check_pyod_install.py for a read-only import/optional-module smoke check.

Route by task

Task intent	Read
Fit one or more classic tabular detectors, inspect decision_scores_, tune contamination, generate synthetic fixtures, evaluate scores with labels	sub-skills/classic-detectors/SKILL.md
Let PyOD choose detectors, run ADEngine profile/plan/run/analyze/report, drive an agentic anomaly investigation, use pyod info, install od-expert, or expose PyOD MCP tools	sub-skills/automated-lifecycle/SKILL.md
Work with time series, graph anomaly detection, text/image/audio embeddings, multimodal inputs, torch-backed neural detectors, PyG, or backend/credential decisions	sub-skills/specialized-modalities/SKILL.md
Save/load models, recover legacy joblib artifacts, validate trusted loaded models, use thresholding or score-combination helpers, or operate SUOD/XGBOD/pythresh/combination extras	sub-skills/model-operations/SKILL.md
Edit PyOD source, package metadata, tests, docs, CLI, MCP import safety, packaged od-expert skill, or KB-derived skill sections	sub-skills/repo-maintenance/SKILL.md

Cross-cutting references

• references/troubleshooting.md — start here for install/import, optional extras, data validation, GPU-claim, persistence, or routing failures.
• references/repo-provenance.md — read before deciding whether this skill is current for a PyOD checkout.
• references/repo-routing-metadata.json — structured metadata used by DisCo's managed repo-skills router.

Operating guardrails

• PyOD's unsupervised detector labels are thresholded anomaly decisions, not proof of ground truth. Report contamination assumptions and validate with labels or domain review when available.
• Higher PyOD anomaly scores normally mean more abnormal. Compare ranks or percentiles across detectors rather than raw magnitudes.
• Do not install pyod[all] by default. Install the smallest optional extra set required by the selected workflow.
• Do not claim CUDA/GPU/accelerator support unless the current runtime has been explicitly probed. Core PyOD and many smoke checks are CPU-friendly.
• Treat pickle/joblib model artifacts as executable code; load only trusted artifacts and use the model-operations trust guidance.
• For source-repo maintenance, use focused tests by edited area and avoid publishing, credential, or release commands without explicit maintainer approval.