「quality」の検索結果

Scans the codebase for dead code, tech debt, outdated dependencies, and code quality issues. Delegates to the Centinela (QA) agent.

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Filesystem RAG benchmarks: corpus/, train.json, evaluate_rag.py (RAGAS quality). Not for prod monitoring, latency/throughput benchmarking (use rag-perf), or evals outside this repo layout.

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Performs deep Root Cause Analysis (RCA) on NVIDIA TAO Visual ChangeNet classification experiments with image-evidence-driven investigation. Use when analyzing ChangeNet model failures, investigating poor recall / FAR / PASS-NO_PASS metrics, auditing visual inspection pipeline quality, or running an RCA report for an AOI defect-detection model. Trigger phrases include "RCA on my ChangeNet model", "why is my AOI model failing", "audit ChangeNet predictions", "investigate FAR regressions", "root cause analysis on visual-changenet".

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Exports sanitized metadata, parameters, reproducibility details, quality metrics, and optional review artifacts from Medical AI inference runs or evidence packs to MLflow. Use after inference, including NV-Generate runs; not for live training tracking, model registration, or clinical use.

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Run condition-based vault health diagnostics. 8 categories — schema compliance, orphan detection, link health, description quality, three-space boundaries, processing throughput, stale notes, MOC coherence. 3 modes — quick (schema+orphans+links), full (all 8), three-space (boundary violations only). Returns actionable FAIL/WARN/PASS report with specific fixes ranked by impact. Triggers on "/health", "check vault health", "maintenance report", "what needs fixing".

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Schema validation for notes. Checks against domain-specific templates. Validates required fields, enum values, description quality, and link health. Non-blocking — warns but doesn't prevent capture. Triggers on "/validate", "/validate [note]", "check schema", "validate note", "validate all".

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GitHub Actions CI/CD pipeline configuration for Golang projects — workflow files for test, lint, SAST, coverage and vulnerability-scan jobs, Dependabot and Renovate config files, GoReleaser release pipelines, Docker build/push, repository security settings, and AI-driven PR review. Use when setting up or improving Go project CI, writing or fixing `.github/workflows/*.yml`, adding a linter or security scanner as a pipeline job, wiring automated dependency-update bots, or adding quality gates. Covers wiring tools into a pipeline, not the analysis they perform: do NOT use for choosing or interpreting security findings (→ See `samber/cc-skills-golang@golang-security` skill) or for choosing, upgrading, or auditing dependency versions (→ See `samber/cc-skills-golang@golang-dependency-management` skill).

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Linting best practices and golangci-lint configuration for Golang projects — running linters, configuring .golangci.yml, suppressing warnings with nolint directives, interpreting lint output, and selecting linters. Use when configuring golangci-lint, asking about lint warnings or nolint suppressions, setting up code quality tooling, or choosing linters. Also use when the user mentions golangci-lint, go vet, staticcheck, or revive. Not for wiring a lint step into a GitHub Actions pipeline (→ See `samber/cc-skills-golang@golang-continuous-integration` skill).

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Create, improve, and evaluate agent skills (SKILL.md plus reference files). Use this skill whenever the user wants to build, scaffold, or design a new skill, improve or fix an existing skill that isn't working well, score or benchmark a skill's quality or run evals on it, or turn a repeated manual workflow into a skill ("I keep doing X manually", "can you remember how to do X", "turn this into a skill").

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[omh] Current technical or business fact to cite from web: web lookup lane - settle a current-facts question in one cited retrieval round with retrieval dates and source-quality notes; for pre-spec grounding across reference implementations use `research`. Use when the user says: web-research, web research, web search, search the web, internet search, look up, look up sources, latest sources.

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[omh] Rendered UI needing a visual verdict: prepare observed-only rendered QA gates for web, frontend, image, document, and TUI surfaces. Use when the user says: visual-qa, visual qa, visual QA, visual quality assurance, visual check, web qa, web visual qa, screenshot qa.

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[omh] Proof a change is done before merge: define and record build, lint, typecheck, test, security, docs, generated-output, and CI evidence before completion or merge. Use when the user says: verification-gate, verification gate, quality gate, release gate, test gate, build lint test, lint typecheck tests, verify before merge.

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[omh] Live rate, score, or local time a work task needs: policy overlay for live lookups - add provider, freshness, units, and source-quality gates after preferring native live-data tools for ordinary weather, finance, sports, maps, and time-zone requests. Use when the user says: live-info-operator, live info operator, live information, real time information, real-time information, weather today, current weather, weather forecast.

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[omh] Translated product or content release: make a product or content release locale-ready with terminology, cultural-fit, and quality-review guidance. Use when the user says: localization review, translation QA, locale glossary, 현지화 검토, 번역 QA, 용어집.

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[omh] Aiming for design-award quality: score a web surface against published design-award judging axes and name the binding constraint. Use when the user says: award-bar-score, award bar score, award winning, award-winning, award winning website, award-winning website, award winning design, award ready.

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[omh] Data pipeline work -- an ETL or streaming job, a backfill or replay, duplicate events, a schema change downstream, a lineage question, a data-quality regression: make every rerun idempotent, bound every replay, and gate each load on observed checks. Use when the user says: data-pipelines, data pipeline, data pipelines, etl, elt, etl pipeline, etl job, etl backfill.

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[omh] Choosing between coding agents on evidence: compare executor or agent choices on reproducible tasks using quality, cost, time, tool, and evidence metrics. Use when the user says: agent-evaluation, agent evaluation, agent eval, agent benchmark, executor evaluation, executor benchmark, compare agents, compare codex claude.

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Creating and improving listing content, covering titles, descriptions, attribute completeness, categorization fixes, image callouts written as text, edits written from material the operator supplies, and content-quality audits and bulk fixes across many listings. Not needed for questions about how a listing is performing.

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Evaluate research rigor. Assess methodology, experimental design, statistical validity, biases, confounding, evidence quality (GRADE, Cochrane ROB), for critical analysis of scientific claims.

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Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP), Node/TypeScript (MCP SDK), or C#/.NET (Microsoft MCP SDK).

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All-in-one read preprocessing with fastp including adapter trimming, quality filtering, deduplication, base correction, and HTML report generation. Use when preprocessing Illumina data and wanting a single fast tool instead of separate Cutadapt, Trimmomatic, and FastQC steps.

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Visualize enrichment results using enrichplot package functions. Use when creating publication-quality figures from clusterProfiler results. Covers dotplot, barplot, cnetplot, emapplot, gseaplot2, ridgeplot, and treeplot.

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Quality control and normalization for metabolomics data. Covers QC-based correction, batch effect removal, and data transformation methods. Use when correcting technical variation in metabolomics data before statistical analysis.

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Targeted metabolomics analysis using MRM/SRM with standard curves. Covers absolute quantification, method validation, and quality assessment. Use when quantifying specific metabolites using calibration curves and internal standards.

日本語の概要は準備中です。原文の説明を表示しています。