「baseline」の検索結果

Simulate transcription factor perturbation effects on cell state in silico with CellOracle and Dynamo, and predict transcriptional responses to genetic perturbations with GEARS, scGen, and CPA. Covers the direction-not-magnitude principle, local-linear validity, the GRN/velocity error it inherits, baseline discipline (mean and additive baselines), and the validation gap. Use when predicting TF knockout or overexpression effects, ranking driver TFs for fate transitions, or planning perturbation experiments. For GRN construction see multiomics-grn; for experimental Perturb-seq see single-cell/perturb-seq.

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。

Normalize read-depth copy-ratio profiles and segment them into copy-number regions using circular binary segmentation (CBS, DNAcopy), hidden Markov models, HaarSeg, and fused-lasso methods. Covers GC-content, mappability, and replication-timing (wave-artifact) bias correction, panel-of-normals/PCA denoising, diploid-baseline centering, and algorithm selection by sequencing depth and event size. Use when choosing a segmentation algorithm, correcting depth bias, diagnosing oversegmentation or a mis-centered baseline, tuning CBS or HMM parameters, or understanding why a downstream CNV caller produced fragmented or shifted segments.

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。

Simulate transcription factor perturbation effects on cell state in silico with CellOracle and Dynamo, and predict transcriptional responses to genetic perturbations with GEARS, scGen, and CPA. Covers the direction-not-magnitude principle, local-linear validity, the GRN/velocity error it inherits, baseline discipline (mean and additive baselines), and the validation gap. Use when predicting TF knockout or overexpression effects, ranking driver TFs for fate transitions, or planning perturbation experiments. For GRN construction see multiomics-grn; for experimental Perturb-seq see single-cell/perturb-seq.

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。

Normalize read-depth copy-ratio profiles and segment them into copy-number regions using circular binary segmentation (CBS, DNAcopy), hidden Markov models, HaarSeg, and fused-lasso methods. Covers GC-content, mappability, and replication-timing (wave-artifact) bias correction, panel-of-normals/PCA denoising, diploid-baseline centering, and algorithm selection by sequencing depth and event size. Use when choosing a segmentation algorithm, correcting depth bias, diagnosing oversegmentation or a mis-centered baseline, tuning CBS or HMM parameters, or understanding why a downstream CNV caller produced fragmented or shifted segments.

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のターミナル画面を固定のサンプルデータで試作し、実際の描画部品を使った2〜6案をtmuxで並べて操作・比較するスキル。

• 初期設定画面の案を比較したいとき
• チャットや選択画面の配置検討
• 既存画面と改善案を比べたいとき

OpenClawのGit履歴からリリース用の変更履歴を作り直します。利用者に関わる変更をまとめ、Issue・PRへの参照や貢献者の記載漏れを検証します。

• stable版公開前の変更履歴作成
• 前回のbeta版からの差分整理
• 貢献者とPR参照の記載確認

Parallelsの仮想マシンでOpenClawの導入・初期設定・更新を検証するスキル。macOS、Windows、Linuxの基本動作確認と失敗の切り分けを扱います。

• 3つのOSで新規導入を確認したいとき
• OpenClawの更新処理を検証したいとき
• 既存スナップショットから検証をやり直す

顧客や取引先への返信案を人の承認に結び付け、承認された本文と送信先、送信結果を記録します。修正前の古い承認による送信や、複数の担当処理による重複送信を防ぐ設計です。

• 顧客への返信を承認制にしたいとき
• 承認本文と送信結果を記録したいとき
• 修正前の承認による送信を防ぎたいとき

変数や関数の命名、読みやすさ、元のデータを直接変更しない更新方法、エラー処理などの共通基準でコードを見直し、保守しやすい形に整えるスキル。

• 既存コードの命名と読みやすさのレビュー
• 長い関数や深い条件分岐を整理したいとき
• 新しいモジュールの規約をそろえたいとき

公開後のURLを点検し、ページやAPIの応答、配信ファイル、継続通信、表示速度の悪化を確認します。単発チェック、一定期間の監視、本番と検証環境の比較に対応します。

• デプロイ後の本番・検証環境の点検
• マージや依存更新後に修正を確認したいとき
• 公開直後のサイトを継続監視したいとき

Turn a Caveman optimization observation into an operator-chosen candidate with a paired baseline evaluation. Use when asked to inspect or evaluate a Caveman optimization report. Needs explicit approval.

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 the ADR-151 per-room calibration pipeline — baseline → enroll → extract → train → a bank of small specialists (presence/posture/breathing/heartbeat/restlessness/anomaly).

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。

Train/evaluate WiFi pose models honestly — camera-supervised (MediaPipe + CSI) and camera-free (WiFlow), always checked against the mean-pose baseline before any PCK is quoted.

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 `@metaharness/darwin security bench` (upstream "Darwin Shield" / ADR-155) — evolves a champion security-detection harness against a 10-vuln / 9-decoy corpus and grades it on TPR/FPR/patch-pass/repro/unsafe vs four baselines (B0 static, B1 LLM-single-pass, B2 fixed-agent, B3 Darwin-champion). Closest reference implementation for ruflo's own ADR-155 nightly self-learning security harness (PR

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。

Read every docs/benchmarks/runs/*.json and surface drift in win rate, latency, escalation rate, and LLM-baseline cost over time

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 the corpus benchmark — booster locally, optional Gemini/Sonnet/Opus baselines — and persist a verifiable measured-vs-claimed table

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。

Runs the evals of a BMad skill through the agent harness the user works in and reports what they show: baseline against the bare model, variant against a stripped or prior version, quality against a rubric, and trigger accuracy of the description with an optional optimization loop. Use when the user asks to evaluate, benchmark, test the triggers of, grade, or compare versions of a skill, or when the bmad-toolsmith skill asks for an eval of a skill it built.

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。

Command-line interface for CloudAnalyzer — Agent-friendly harness for CloudAnalyzer, a QA platform for mapping, localization, and perception outputs. Supports 27 commands across 8 groups: point cloud evaluation, trajectory evaluation, ground segmentation QA, config-driven quality gates, baseline evolution, processing, visualization, and interactive REPL.

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。

Supports multivariate severity assessment and exploratory endpoint-time score forecasting for laboratory animal studies using the RELSA (RELative Severity Assessment) score and ARIMA-based foRcast forecasting. Use when combining welfare readouts — body weight or weight loss, body temperature, clinical or nesting scores, biomarkers, activity, heart rate, burrowing, wheel running — into one severity score per animal per day, when asking which animals are at risk of reaching a humane endpoint at a specified future observation time, when defining attention/danger zones or thresholds on a severity scale by kernel density estimation, or when reporting severity for a 3Rs, refinement, animal-welfare, or EU Directive 2010/63/EU severity-assessment context. Covers directionality ("turned" variables), baseline normalization, reference sets, RELSA weights, ARIMA prediction intervals, and RMSE/PICP/MPIW evaluation.

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。

Processes calibrated one-dimensional complex NMR free-induction decays with nmrglue into phased spectra, peak candidates, and signed integration regions. Use for raw 1D NMR processing, ppm-axis verification, apodization, Fourier transformation, manual phasing, baseline correction, or reproducible spectral integration.

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。

Prompt engineering regression test matrix register: baseline outputs, semantic drift thresholds, judge evaluations, and golden dataset.

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。

Diff a live page's accessibility violations against a baseline — by default compares uncommitted changes (stash-based), or pass --branch [<name>] to diff against a branch. Reports only new violations introduced, violations fixed, and pre-existing count. Use `scan` for a full audit with no diffing.

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。

The baseline method for building ONE stage — a single classroom — with this runtime. Covers how the plan is settled in conversation, the order the generation tools are called in, how the classroom roster is written, when narration has to be re-synthesized, and what must be true before a stage is called done. Load this before generating or rebuilding any stage from scratch, whatever its subject; it is the floor the topic skills build on, not an alternative to them.

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 the evaluation harness that gates every fine-tuning run — golden sets, per-failure-mode graders, judge calibration, and base-model baselines. Use when starting a fine-tuning effort, when converting traces into an eval set, or when calibrating a judge against human labels.

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。