Results for “quality-filtering”

Benchmark-relative quality gate. Scores the draft against the research dossier's beat spec (depth, consensus coverage, evidence) plus AI-tell and voice signals, runs an adversarial read armed with the SERP benchmark, and emits the verdict that gates the pipeline.

Audits code quality against a shared contract, producing structured findings and blocking pipeline phases on critical issues without modifying code.

Audits code quality against a shared contract, returning structured findings without modifying code. Supports gate, audit, diff, changed, and verify modes.

Data quality framework — completeness, accuracy, consistency, validation, and contracts. Use when implementing data validation, setting up quality checks for a pipeline, defining data contracts between teams, or investigating data anomalies.

Runs a multi-phase verification pipeline including build, type-check, lint, tests, security scan, and diff review to ensure code quality before creating a PR.

Runs a structured bug-fix pipeline: scouts evidence, diagnoses root cause, applies minimal fixes, and reviews before committing.

Use for the search-planning stage of a literature review, when database lanes and language layers must be chosen, when DOI coverage policy is unclear, or when inclusion and exclusion criteria need drafting before any source is read; not for running the screening and PRISMA pipeline of a formal review, which is prisma-scoping-review-pipeline.

Implements a feature phase by phase from a phased JSON or Markdown plan, with configurable unit-test and quality-gate choices per phase, token-budgeted execution, resumable state, and TDD handoff.