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name: automated-lifecycle description: "Operate PyOD's ADEngine lifecycle, agentic investigation sessions, CLI activation commands, and MCP JSON tools as self-contained PyOD package guidance." disable-model-invocation: true metadata: disco-role: operating license: BSD 2-Clause

PyOD Automated Lifecycle

Use this sub-skill when a task asks an agent to automate anomaly detection with PyOD instead of hand-picking a single detector: profile data, choose a detector or detector set, run detection, analyze/explain results, iterate on feedback, generate reports, install PyOD's packaged od-expert activation skill, or expose PyOD through MCP-compatible tools.

Route here for

• ADEngine lifecycle APIs: profile_data, plan_detection, build_detector, detect, run_detection, analyze_results, explain_findings, suggest_next_step, generate_report, and the session methods start, plan, run, analyze, iterate, report, investigate.
• Agent state handling: InvestigationState, state.next_action, multi-detector consensus, quality diagnostics, iteration, and report handoff.
• CLI activation surfaces: pyod info, pyod install skill, pyod install skill --project, pyod install skill --list, and pyod mcp serve.
• MCP wrappers: the ten registered tools, JSON payload shapes, JSON error returns, and missing-mcp behavior.
• Safe smoke verification of a local PyOD install without starting a server.

Route elsewhere

• Low-level detector APIs, detector-family trade-offs, BaseDetector attributes, and synthetic tabular examples -> classic-detectors.
• Time-series, graph, embedding, text/image/audio, torch, PyG, and other modality backends -> specialized-modalities.
• Persistence, thresholding, score combination, SUOD/XGBOD operational extras -> model-operations.
• Packaged skill source regeneration, test maintenance, and repository internals -> repo-maintenance.

Load these bundled files

File	When to use it
references/adengine-api.md	Need exact ADEngine method contracts, input/output shapes, deterministic random_state behavior, result dictionaries, report formats, or knowledge-query helpers.
references/agentic-workflows.md	Need to drive the stepwise session workflow, interpret InvestigationState fields and next_action, recover detector failures, or run an od-expert-style agent investigation.
references/cli-and-mcp.md	Need PyOD CLI commands, od-expert install modes, MCP server availability, MCP tool list, file loading rules, JSON payload examples, or JSON error handling.
references/troubleshooting.md	Need symptom-to-recovery guidance for CLI/MCP install issues, malformed JSON, missing optional extras, phase-order errors, noisy results, nondeterminism, or agent activation problems.
scripts/adengine_smoke.py	Run a safe deterministic ADEngine smoke test. Optional flags probe CLI and MCP availability without starting an MCP server.

Minimal operating pattern

from pyod.utils.ad_engine import ADEngine

engine = ADEngine(random_state=42)  # deterministic shallow-detector routing/run
state = engine.start(X, data_type="tabular")
state = engine.plan(state, constraints={"max_detectors": 2})
state = engine.run(state)
state = engine.analyze(state)

if state.next_action["action"] == "report_to_user":
    report = engine.report(state, format="text")
else:
    # Ask or iterate based on state.next_action before reporting.
    follow_up = state.next_action

Treat ADEngine quality metrics as descriptive, label-free diagnostics. They help triage agreement and cutoff stability; they do not prove anomaly correctness. For high-stakes domains or available labels, add domain/label validation and route supervised detector work to the appropriate PyOD detector guidance.