「egress」の検索結果

Review local code changes for bugs, regressions, missing tests, and pragmatic improvements. Use when the user asks to review the current changes from `git status`, or to review a specific backlog task by id such as `task-65` or `TASK-65`. Also trigger when the user says things like "review", "review current changes", "check my changes", "look over the diff", or "review task-42".

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 writing E2E/integration tests, testing native interactions like permissions or system dialogs, capturing UI regressions, or validating cross-platform behavior (Patrol 4.x).

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。

Bug fix workflow: root cause analysis → user review → regression test + fix via TDD

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。

Monthly, file work only, no browser at all. Re-reads a month of real change outcomes and rewrites the file that decides what the fix runner may change without asking first. A class the member merged unchanged repeatedly widens by exactly one step. A class they edited, closed unmerged, or that regressed narrows immediately. It never touches the outer boundary, which no volume of evidence can widen.

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。

Diagnose hard bugs and performance regressions with reproducible evidence, focused hypotheses, and proportional instrumentation. Use when something is broken, failing, throwing, intermittently wrong, or unexpectedly slow.

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。

Reviews a completed change for acceptance, regressions and release risk; read-only, not a whole-codebase audit.

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。

Upgrades dependencies in reversible batches with version-specific compatibility and regression checks.

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。

Strict quality-first TDD specialist. Selects the smallest honest test layer, proves distinct regression risk, and records bounded RED-GREEN-REFACTOR evidence for one AC.

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。

Test Python services with pytest, async coverage, monkeypatch, and boundary-focused fakes. Use when writing Python tests, fixtures, async tests, regression tests, or dependency-isolated 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。

Guides quality-first TDD for new behavior, bug fixes, and test changes. Selects the smallest test layer, proves a distinct regression risk, and runs bounded RED-GREEN-REFACTOR 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。

Performs security-focused differential review of code changes (PRs, commits, diffs). Adapts analysis depth to codebase size, uses git history for context, calculates blast radius, checks test coverage, and generates comprehensive markdown reports. Automatically detects and prevents security regressions.

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。

Design system token management, component API design, Storybook, and visual regression testing patterns

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。

Framework for measuring and tracking agent response quality over time. Detects regressions before they reach production. Use when evaluating agent changes, auditing quality, or establishing performance baselines.

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 validating system performance under load, identifying bottlenecks through profiling, or optimizing application responsiveness. Covers load testing (k6, Locust), profiling (CPU, memory, I/O), and optimization strategies (caching, query optimization, Core Web Vitals). Use for capacity planning, regression detection, and establishing performance SLOs.

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。

Precision-first adversarial bug hunting for runtime, logic, data, concurrency, and security defects. Uses deterministic risk triage, evidence-bounded retrieval, Hunter/Skeptic/Referee review, optional hybrid verification, and explicit immutable Fixer scope. Scan-only and single-pass by default; complete-coverage loops, edits, autonomous fixes, and commits each require explicit intent. Use for code review, security audits, regression hunting, PR review, and evidence-backed remediation planning in skills-capable coding agents.

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。

Audit/regression checks for the evidence-first survey pipeline: citation health, per-section coverage, placeholder leakage, and template repetition.

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。

Regression-check citation anchoring (citations stay in the same subsection) to prevent “polish drift” that breaks claim→evidence alignment.

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。

Diagnosis loop for hard bugs and performance regressions. Use when the user says "diagnose"/"debug this", or reports something broken/throwing/failing/slow.

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。

Inspect and update NuGet package families using ManagedCode NuGetPackageManager, the nuget-manager CLI, or its VS Code extension. USE FOR: checking outdated packages; dry-run family reviews; applying reviewed literal package and explicit project SDK version updates. DO NOT USE FOR: adding/removing packages; dependency graph resolution; authenticated private-feed setup. INVOKES: inspect declarations, preview and review exact versions, apply authorized changes, then restore and run focused regressions.

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。

ALWAYS USE for test work that requires changes: write, add, generate, repair, or strengthen tests for existing code in xUnit, MSTest, NUnit, pytest, Vitest/Jest, Go, or another framework. Includes regression cases, failing or flaky tests, coverage-driven additions, and audit-then-fix requests. Focused work stays direct; broad or multi-stage work invokes test-engineer. DO NOT USE for only running tests, analysis-only audits, framework/platform migrations, a test blocked on a missing production seam (testability-obstacle), or MSTest API/configuration corrections that do not design new cases (writing-mstest-tests). Within an active test-engineer pipeline, reuse supplied guidance and do not re-enter this skill.

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。

Guides technology selection and implementation of AI and ML features in .NET 8+ applications using ML.NET, Microsoft.Extensions.AI (MEAI), Microsoft Agent Framework (MAF), GitHub Copilot SDK, ONNX Runtime, and OllamaSharp. Covers the full spectrum from classic ML through modern LLM orchestration to local inference. Use when adding classification, regression, clustering, anomaly detection, recommendation, LLM integration (text generation, summarization, reasoning), RAG pipelines with vector search, agentic workflows with tool calling, Copilot extensions, or custom model inference via ONNX Runtime to a .NET project. DO NOT USE FOR projects targeting .NET Framework (requires .NET 8+), the task is pure data engineering or ETL with no ML/AI component, or the project needs a custom deep learning training loop (use Python with PyTorch/TensorFlow, then export to ONNX for .NET inference).

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。

Enhancement overlay — version the WHOLE deployable LLM-app artifact as one bundle: prompts + compiled programs + model snapshot pins + retrieval config + eval-set version, versioned together so a deploy is reproducible and rollback is atomic. Activate when preparing to deploy an LLM app, when asking "what exactly is running in prod right now?", when a deploy must be reproducible months later, or when an incident needs a clean rollback. The core reframe: an LLM app artifact is NOT an ML model — it is a manifest over many independently-mutable parts, not one weights file. Do NOT activate for one-off prompt edits with no deploy, for a single-component demo, or where a vendor owns the whole prompt lifecycle. For versioning ONE compiled prompt use [[agentsop-per-model-artifacts]]; for the CI comparison mechanism use [[agentsop-regression-gate]]. Search keywords: prompt versioning, reproducible deploy, what is running in prod, rollback LLM app, model pinning, prompt registry, version prompts and config.

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。

Best practices for creating ISPC lit tests. Use when writing regression tests, verifying code generation, or checking compiler diagnostics.

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。

Gate new or changed Enterprise tests and audit existing tests for observable behavior, credible regressions, duplicate coverage, and test-only production seams.

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。