Provisions the oracle ML inference daemon with onnxruntime via uv. Use when setting up local ONNX model inference for skill quality evaluation.
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See how to installReview the instructions the agent will receive before you install.
Provision the ML inference environment.
abstract:skills-eval)cd plugins/oracle && uv run python -c "
from oracle.provision import provision_venv, get_venv_path
result = provision_venv(get_venv_path())
print(result.message)
"
provision_venv() returns a result with a non-error message
and the venv path exists on disk under plugins/oracle/onnxruntime is importable inside the provisioned venv;
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Shapes turns action-first: action leads, steps numbered, state restated. Use for ADHD-friendly output. Do not use to trim tokens; use response-compression.
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Inverts burden of proof for code additions. Use when reviewing PRs, planning refactors, or running unbloat to challenge every addition's necessity.
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Tracks per-agent token usage and flags waste in parallel dispatch. Use when evaluating parallel agent efficiency or after a multi-agent run.
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Coordinates Claude agent teams via filesystem protocol. Use when orchestrating parallel agents with task dependencies. Do not use for single-agent tasks.
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Evaluates API surface design, consistency, and exemplar alignment. Use when reviewing public API changes or before releasing a new API surface.
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Selects architecture paradigm via research before scaffolding. Use when architecture is undecided and the choice needs justification and documentation.
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