「coverage」の検索結果

Run pytest tests with coverage, discover lines missing coverage, and increase coverage to 100%.

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。

Coverage review and verification sufficiency routing. Use when test coverage, uncovered paths, coverage reports, or verification adequacy need a read-only QA review; do not use for writing tests or running browser/E2E artifacts.

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。

An innate exploratory drive that guarantees no graph node remains permanently invisible to an agent population. After a warm-up period (step > 3), each agent fires a coverage scan with probability |unseen_nodes| / |all_nodes|. When triggered, the agent teleports directly to the globally unseen node with the highest belief uncertainty, works it, and returns control to the normal action-selection loop. Probability is self-annealing: it starts near 1.0 when almost everything is unseen and decays toward 0.0 as coverage saturates, requiring no manual schedule. The mechanism is "innate" (hard-coded into the agent step loop) as distinct from the "adaptive" EFE path, which requires local neighbors and learned precision weights. Together they form a two-layer exploration architecture: adaptive for locally-guided foraging, innate for topological completeness.

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。

OpenClawのテストやプラグイン検証の遅さ・メモリ使用量を計測し、読み込みや起動処理の原因を調査。テストの検証範囲を保ちながら改善し、前後の数値を比較します。

• 遅いテストや重い読み込みの原因調査
• プラグイン検証の時間とメモリ削減
• テスト中のメモリ増加を調べたいとき

Laravelの環境設定からコード検査、テスト、依存関係の脆弱性、データベース変更、キューまで順に確認し、マージやデプロイ前の問題を調べるスキル。

• Laravelのプルリクエスト前の確認
• 大きな改修や依存関係更新後の検証
• ステージング・本番へのデプロイ前確認

Laravelの機能をテストから実装し、モデルやAPI、認証・権限を検証するスキル。PHPUnitやPestでのテスト作成、外部サービスの模擬化、カバレッジ確認を支援します。

• Laravelの機能をテストから実装したいとき
• APIの認証・権限を検証したいとき
• 決済・メール・キューを模擬化したいとき

Kotlinのテストを先に書き、KotestとMockKで動作や依存先を検証し、非同期処理のテストからKoverによる網羅率の確認まで進めるスキル。

• 既存のKotlinコードにテストを追加
• テストを先に書いて新機能を作りたいとき
• コルーチンやFlowの動作検証

Goのテストを先に書く開発手順に沿い、複数ケースの検証、HTTP応答の確認、性能測定、入力の自動生成による検査、カバレッジ確認を支援するスキル。

• テストを先に書いてGoを開発したいとき
• 既存テストの正常系・異常系を補いたいとき
• 処理時間とメモリ確保量の比較

Djangoの公開や変更提案の前に、環境・コード品質・データベース変更・テスト・脆弱性・設定を順に検証し、段階別の合否と修正すべき問題をレポートにまとめます。

• Djangoの変更提案前の確認
• マイグレーションを確認したいとき
• テストカバレッジの目標確認

Map advanced persistent threat (APT) group TTPs to the MITRE ATT&CK framework using the attackcti Python library to query STIX/TAXII data for group-technique associations, then generate ATT&CK Navigator layer files to visualize and compare defensive coverage against adversary profiles. Use when profiling an APT group's techniques, building Navigator coverage heatmaps, or assessing technique coverage gaps against a specific threat actor.

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。

Author, validate, test, and deploy YARA-L 2.0 detection rules and evaluate end-to-end detection coverage gaps in Google SecOps. Use when writing new detection rules, tuning existing rules, validating syntax, testing logic against historical telemetry, or evaluating detection coverage against threat intelligence blogs, CVE disclosures, and Threat Detection Opportunities (TDOs) using synthetic UDM events and long-running coverage analysis. Don't use for alert triage (use secops-triage), deep forensic event reconstruction on infected hosts (use secops-investigate), or case management operations (use secops-cases).

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。

Measures and interprets what a fuzzing campaign actually reaches, using llvm-cov, lcov, or a fuzzer's own coverage output. Covers baselining a new campaign, reading coverage reports, and turning uncovered regions into harness, seed, or dictionary work. Use when a fuzzer plateaus, when judging whether a harness is effective, after changing a harness, or when asking why some code is never reached.

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。

Calculates CRAP (Change Risk Anti-Patterns) for a named .NET method, class, or file. USE FOR: explicit CRAP calculation or coverage-and-complexity risk within that named target, including which tests to prioritize. DO NOT USE FOR: project-wide coverage/CRAP, plateaus, or project-wide blockers/priorities (coverage-analysis); behavioral/pseudo-mutation gaps (test-gap-analysis); writing tests; test runs without CRAP context.

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。

Add gcov code coverage instrumentation to C/C++ projects

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。

AWS security service suitability, coverage and cost recommendations: WAF/origin overlap, AWS Network Firewall inspection coverage and policy review, VPC endpoint, Transit Gateway/Cloud WAN segmentation and DNS Firewall security-control suitability, certificate renewal ownership and monitoring, incident response triage ownership, centralized security log store (CloudWatch or Security Lake). Also factual configuration, findings, data sources and severity scoring for GuardDuty, Inspector, Security Hub V2/OCSF (Exposure, connectors, automation), Security Hub CSPM/ASFF (standards, controls), Macie, Detective, Security Lake, AWS Security Incident Response membership and case coverage, and Organizations security policies. Applies to advisory security-service choices even when one service is named. Implementation, rule or code authoring, deployment, scans and troubleshooting outside these factual procedures belong to specialist skills. Supports local AWS CLI and AWS MCP.

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。

When the user wants to get press coverage, media mentions, or editorial features for their app — including writing press releases, pitching journalists, getting on "best apps" lists, or building an app press kit. Use when the user mentions "press", "PR", "media coverage", "TechCrunch", "journalist", "press release", "app press kit", "get featured in media", "editorial coverage", "review from a blogger", or "app launch announcement". For Apple editorial featuring, see app-store-featured. For launch strategy, see app-launch.

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。

Use after writing tests to assess coverage quality across structural, mutation, requirements, and API/integration dimensions; organized knowledge for choosing and interpreting coverage analyses.

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。

AI DevKit · Testing phase guidance for adding and validating feature test coverage. Use when the user wants to write tests, update testing docs, run coverage, close coverage gaps, or run dev-lifecycle phase 8.

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。

Run a Google Alerts-style keyword coverage tracker. Uses news-search for recent keyword queries, lets the LLM dedupe and classify real features versus junk, stores decisions in SQLite, and alerts only on new real coverage.

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。

Build, refresh, report, or probe an accessibility coverage matrix across criteria, surfaces, and evidence methods. Use when assessing coverage with the accessibility runtime harness and generated evidence bundle.

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。

Map an organisation's risks against its insurance policy portfolio to find what's uncovered, underinsured, or double-covered. Use when asked to run a coverage gap analysis, review an insurance programme against a risk register, check what risks aren't insured, or audit a policy portfolio. Produces a risk-by-coverage matrix, flagged gaps and overlaps, a deductible stack review, and recommendations ranked by expected-loss severity.

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。

Generates, normalizes, and converts bedGraph signal tracks (4-column chrom/start/end/value, 0-based half-open) with bedtools genomecov, deepTools bamCoverage/bamCompare/bigwigCompare, bedtools unionbedg, and UCSC bedGraphToBigWig. Covers why a raw coverage bedGraph is not comparable across samples until normalized, the CPM/RPKM/BPM/RPGC normalization menu and the conserved-total assumption that makes them wrong under a global perturbation, the strict sorted-non-overlapping-chrom.sizes bedGraphToBigWig contract that silently corrupts a bigWig, effective-genome-size selection, and bin-size aliasing. Use when building or normalizing a coverage/signal track from a BAM, comparing tracks across samples or conditions, converting bedGraph to a browser-ready bigWig, or diagnosing a track that looks plausible but reports wrong heights.

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。

Calls significant loops from protein-directed and targeted 3C assays (HiChIP, PLAC-seq, Capture Hi-C/PCHi-C, ChIA-PET) where the contact background is peak-anchored and coverage-biased, so generic Hi-C loop callers (cooltools dots, Juicer HiCCUPS) use the wrong null. Covers FitHiChIP (config-driven coverage+distance-decay spline regression, peak-to-peak vs peak-to-all foreground, loose vs stringent background, coverage vs ICE bias), MAPS (positive Poisson regression on bias factors for PLAC-seq/HiChIP), hichipper (restriction-site-distance bias model + library QC), CHiCAGO (Delaporte two-component Brownian+technical background for asymmetric bait x other-end Capture Hi-C), the with/without separate-ChIP anchor decision, and differential loops via diffloop. Use when calling loops from HiChIP/PLAC-seq/Capture Hi-C, choosing FitHiChIP/MAPS/CHiCAGO, picking peak-to-all vs peak-to-peak, setting the loop FDR, supplying ChIP peaks as anchors, QCing a HiChIP library, or comparing loops between conditions.

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。

Build genome-browser-style multi-track figures with pyGenomeTracks (config-driven), Gviz (R), and IGV batch screenshotting. Covers BigWig coverage tracks, BED/peak overlays, gene-model rendering, Hi-C matrix tracks, BedPE link arcs, spike-in-aware normalization, and the bamCoverage --normalizeUsing trap. Use when producing publication figures of genomic loci with stacked aligned tracks (coverage, peaks, genes, interactions) for ChIP-seq, ATAC-seq, RNA-seq, Hi-C, or generic locus visualization.

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。