「diff」の検索結果

Spring Bootの変更をビルド、コード解析、テスト、セキュリティ検査で確認するスキル。差分もレビューし、公開・デプロイ前の合否と修正点をまとめます。

• Spring BootのPR前に確認したいとき
• リファクタリングや依存更新後の検証
• テストカバレッジの目標を確認したいとき

機能追加やコード整理の後に、ビルド・型・lint・テスト・秘密情報の検索・差分を6段階で確認するスキル。合否と修正事項をまとめ、PRを作成できる状態か報告します。

• 機能追加後のコードを検証したいとき
• リファクタリング後の不具合確認
• PR作成前の準備状況確認

正常に動いている既存機能を新しい仕様へ変更するスキル。テストを先に更新し、実装の修正、レビュー、承認を挟んだコミットまで順に進めます。

• 既存の通知しきい値を変えたいとき
• 動作中の機能を新仕様に合わせたいとき
• 振る舞いの変更前にテストを更新したいとき

採点済みの競合情報を、市場マップや比較表、差別化の余地、優先施策を含むレポートにまとめます。経営層への報告や、チームで競争方針を決める場面に役立ちます。

• 経営層や取締役会向けの競合報告
• 完成済み競合プロフィールの統合
• 差別化の余地と競合脅威の検討

Conducts multi-axis code review. Use before merging any change. Use when reviewing code written by yourself, another agent, or a human. Use when you need to assess code quality across multiple dimensions before it enters the main branch. Use when asked to review a diff or a pull request, even when the diff is pasted inline.

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。

Render the patch-edit run's accumulated changes as a reviewable diff, surface it through a GenUI choice surface, and persist the user's accept / reject decision into the artifact manifest.

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 when you need to analyze git diffs or pull requests to understand what changed, affected components, and risks

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。

Automate Diffbot tasks via Rube MCP (Composio). Always search tools first for current schemas.

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。

Analyze git diffs for risk scoring, reviewer recommendations, and change classification. Use when preparing a PR, reviewing a large or cross-module change, or before merging to assess risk and pick reviewers.

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。

Visual + DOM diff between two recorded sessions at matching trajectory step ids; used for visual regression and replay verification

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。

Provides access to a collection of open-source molecular design and structural biology tools on the Tamarind Bio platform, via its REST API or MCP server — no local GPUs required. Tamarind bundles popular open-source models for structure prediction (AlphaFold, Boltz, Chai, ESMFold), protein, binder, and de novo design (RFdiffusion, ProteinMPNN, BoltzGen), antibody and nanobody design and developability, protein-ligand docking (DiffDock, Autodock Vina), binding-affinity prediction, MSA generation, and molecular dynamics. Use when the user mentions Tamarind or tamarind.bio, wants to run any of these open-source tools in the cloud, references app.tamarind.bio/api or the x-api-key header, or needs to submit batches of sequences for structural or biophysical characterization.

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。

Writes and edits Markdown without inserting manual hard line breaks at a fixed column (e.g. 72 or 80). GitHub, docs sites, and editors soft-wrap prose; artificial breaks make diffs noisy and reflow awkward. Use when creating or editing .md files, PR bodies, ADRs, READMEs, rulecards, or any Markdown where the user cares about readable source and clean diffs — or when the user asks to avoid "hard wrapping" or "80-column" line breaks in prose.

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 this skill whenever the user wants to analyze binaries with radare2/r2 from the command line, including reverse engineering, disassembly, function analysis, strings/import inspection, patching, binary diffing, hex inspection, or r2 scripting. Also use it when the user mentions PE/ELF/Mach-O/DEX/WASM files together with CLI analysis, `rabin2`, `rasm2`, `radiff2`, `r2pipe`, or asks for radare2 command help on Windows/Linux/macOS.

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 when benchmarking denoise latency or profiling a diffusion bottleneck in SGLang.

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 when choosing the fastest SGLang Diffusion flags for a model, GPU, and VRAM budget.

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 when adding a new model or pipeline to diffusers, setting up file structure for a new model, converting a pipeline to modular format, or converting weights for a new version of an already-supported model.

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。

Quality gate via second model. Spawn a different AI model to review work before committing. Includes refusal routing: if one model refuses, switch silently to the next. Extended in v0.25.1 with structured review-mode gating (when to invoke vs not) and a Codex code-review handoff for the diff-review case.

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 when creating an approachable, self-contained HTML review aid for a pull request; explaining what changed, why it matters, how it works, and how it fits into the broader system; turning PR diffs, commits, tests, and architecture context into a local `.pr-review/` HTML page for reviewers; or helping reviewers understand complex code changes without dumping the full diff.

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。

World-class senior data scientist skill specialising in statistical modeling, experiment design, causal inference, and predictive analytics. Covers A/B testing (sample sizing, two-proportion z-tests, Bonferroni correction), difference-in-differences, feature engineering pipelines (Scikit-learn, XGBoost), cross-validated model evaluation (AUC-ROC, AUC-PR, SHAP), and MLflow experiment tracking — using Python (NumPy, Pandas, Scikit-learn), R, and SQL. Use when designing or analysing controlled experiments, building and evaluating classification or regression models, performing causal analysis on observational data, engineering features for structured tabular datasets, or translating statistical findings into data-driven business decisions.

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 when reviewing or rebalancing direct vs. partner-led channel economics — computing fully-loaded cost-to-serve per channel, channel ROI with cash / LTV / marginal lenses, and optimal channel mix subject to constraints. For Head of Commercial, RevOps, and VP Sales doing quarterly channel review when pipeline is mixed (e.g., 60% direct + 40% partner-led) and nobody actually knows which channel makes money after CAC, support load, partner discount, deal-velocity differences, retention differential, and overhead allocation are all loaded in. Outputs cost to serve, channel ROI verdicts (DOUBLE-DOWN / MAINTAIN / DEFUND / EXIT), a sensitivity-tested channel-mix recommendation, and the diminishing-returns inflection (e.g., 'which channel actually makes money — direct or partner?').

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。

Chief Customer Officer advisory for startups: retention decomposition (gross retention vs NRR honesty, churn root-cause taxonomy), customer segmentation strategy (differential investment across tiers + ICP fit scoring), CS team coverage model (pooled vs named CSM thresholds + ratio math), and CS team org evolution (CS vs Support vs AM distinctions). Use when designing retention strategy, segmenting customers for differential investment, sizing CS team, or sequencing CS hires. Strategic only — does not duplicate engineering/business-growth tactical skills.

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。

Analyze competitors with strengths, weaknesses, and differentiation opportunities. Identifies direct competitors and maps the competitive landscape. Use when doing competitive research, preparing a competitive brief, or finding differentiation opportunities.

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。

Brainstorm product positioning ideas differentiated from competitors. Identifies top competitors and generates positioning statements with rationale. Use when developing product positioning, differentiating from competitors, or crafting brand positioning strategy.

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。

Flag desktop<->den contract drift introduced by this diff. High findings gate Warden clearance; medium findings are advisory only.

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。