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run-quality-gates

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UpdatedMay 2, 2026 at 18:52

Run Wildlife Watcher ruff, mypy, pytest, coverage, and ML pipeline checks through the repo harness. Use for lint, typecheck, regression, quality gate, and pre-merge validation tasks.

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ianshank

ianshank/Wildlife_Watcher

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Related occupationsSOC

Based on SOC occupation classification

Software Quality Assurance Analysts and TestersComputer and Mathematical Occupations·SOC 15-1253

SKILL.md

readonly

name	run-quality-gates
description	Run Wildlife Watcher ruff, mypy, pytest, coverage, and ML pipeline checks through the repo harness. Use for lint, typecheck, regression, quality gate, and pre-merge validation tasks.
argument-hint	Optional dry-run flag or reason for running the quality gate.
user-invocable	true

Run Quality Gates

When To Use

Validate a change before review or merge.
Reproduce the repo-standard ruff, mypy, pytest, coverage, and ML pipeline flow.
Confirm that setup and CI use the same commands.

Procedure

Activate the project virtual environment.
Run python .agents/harness/orchestrator.py quality.
When the slice touches ml-pipeline/, also run python .agents/harness/orchestrator.py ml-pipeline-lint, ml-pipeline-typecheck, ml-pipeline-test, and ml-pipeline-smoke.
If the gate fails, rerun the narrow failing command before editing more code.
Report the smallest failing slice, not the entire repo history.

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name	run-quality-gates
description	Run Wildlife Watcher ruff, mypy, pytest, coverage, and ML pipeline checks through the repo harness. Use for lint, typecheck, regression, quality gate, and pre-merge validation tasks.
argument-hint	Optional dry-run flag or reason for running the quality gate.
user-invocable	true

Run Quality Gates

When To Use

Validate a change before review or merge.
Reproduce the repo-standard ruff, mypy, pytest, coverage, and ML pipeline flow.
Confirm that setup and CI use the same commands.

Procedure

Activate the project virtual environment.
Run python .agents/harness/orchestrator.py quality.
When the slice touches ml-pipeline/, also run python .agents/harness/orchestrator.py ml-pipeline-lint, ml-pipeline-typecheck, ml-pipeline-test, and ml-pipeline-smoke.
If the gate fails, rerun the narrow failing command before editing more code.
Report the smallest failing slice, not the entire repo history.