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概要と使いどころ
Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.
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Build and maintain a Markdown guide that helps humans understand a repository through real behavior, concepts, and evidence. Use when a user asks to understand a repo, generate or update repo-docs, answer repo-architecture/onboarding questions, seed docs for a new project, sync docs after code changes, or delete generated repo docs.
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。
Behavioral guardrails for LLM-assisted coding. Use when writing, reviewing, or refactoring code in any project to avoid overcomplication, keep changes surgical, surface assumptions early, and execute against verifiable success criteria.
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。
Lifecycle SOP for **per-model prompt artifacts** — the compiled prompts, instructions, few-shot demos, edit-format pins, and embedding-bound indices that change behavior when the underlying LM, dataset, or framework version changes. Activate when adopting compiled prompts (DSPy, GEPA, BootstrapFewShot output), when supporting multiple LMs in production, when a provider deprecates a model snapshot, or when a framework deprecates a config surface (LlamaIndex `ServiceContext` → `Settings`, Aider edit-format defaults). Do NOT activate for one-off raw prompt edits or for truly model-agnostic system prompts that have been swap-tested. Search keywords: prompt portability, model swap, recompile prompt, model deprecation, prompt per model, prompt breaks on new model, version compiled prompts.
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。
Decomposed, multi-criteria metric design for LLM pipelines. The metric IS the model — change the metric and the optimizer changes behavior. Decompose by default; bool during compile, float during eval; calibrate against human; mitigate judge bias. Search keywords: LLM-as-judge, llm as judge, eval metric, evaluation score, scoring function, rubric, RAGAS, G-Eval, judge bias, verbosity bias, how to evaluate LLM output.
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 encountering any bug, test failure, or unexpected behavior, before proposing fixes
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。
Explores intent and design before implementation. MUST use before creating or modifying any feature, component, or behavior.
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。
Structured trade logging, performance review, behavioral pattern detection, and strategy attribution for systematic improvement
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。
Behavioral classification, performance analysis, and trading style detection for Solana wallets
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。
Systematically investigate bugs, test failures, build errors, performance issues, or unexpected behavior by cycling through characterize-isolate-hypothesize-test steps. Use when the user asks to "investigate this bug", "debug this", "figure out why this fails", "find the root cause", "why is this broken", "troubleshoot this", "diagnose the issue", "what's causing this error", "look into this failure", "why is this test failing", or "track down this bug".
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。
Clean only the current Enterprise diff before autoreview, preserving behavior, security boundaries, required TODOs, and useful test-intent comments.
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。
Develop OpenClaw Enterprise changes with proportional verification and source-backed flow documentation for non-trivial behavior.
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。
Use when working with Payload CMS projects (payload.config.ts, collections, fields, hooks, access control, Payload API). Use when debugging validation errors, security issues, relationship queries, transactions, or hook behavior.
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。
Analyzes meeting transcripts and recordings to uncover behavioral patterns, communication insights, and actionable feedback. Identifies when you avoid conflict, use filler words, dominate conversations, or miss opportunities to listen. Perfect for professionals seeking to improve their communication and leadership 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。
Use when users need to debug, modify, or extend the code-forge application's CLI commands, argument parsing, or CLI behavior. This includes adding new commands, fixing CLI bugs, updating command options, or troubleshooting CLI-related issues.
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。
Master systematic debugging techniques, profiling tools, and root cause analysis to efficiently track down bugs across any codebase or technology stack. Use when investigating bugs, performance issues, or unexpected behavior.
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。
Migrate prompts and code from Claude Sonnet 4.0, Sonnet 4.5, or Opus 4.1 to Opus 4.5. Use when the user wants to update their codebase, prompts, or API calls to use Opus 4.5. Handles model string updates and prompt adjustments for known Opus 4.5 behavioral differences. Does NOT migrate Haiku 4.5.
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。
This skill should be used when the user asks about "plugin settings", "store plugin configuration", "user-configurable plugin", ".local.md files", "plugin state files", "read YAML frontmatter", "per-project plugin settings", or wants to make plugin behavior configurable. Documents the .claude/plugin-name.local.md pattern for storing plugin-specific configuration with YAML frontmatter and markdown content.
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 tests have race conditions, timing dependencies, or inconsistent pass/fail behavior - replaces arbitrary timeouts with condition polling to wait for actual state changes, eliminating flaky tests from timing guesses
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。
Define database models with clear naming, appropriate data types, constraints, relationships, and validation at multiple layers. Use this skill when creating or modifying database model files, ORM classes, schema definitions, or data model relationships. Apply when working with model files (e.g., models.py, models/, ActiveRecord classes, Prisma schema, Sequelize models), defining table structures, setting up foreign keys and relationships, configuring cascade behaviors, implementing model validations, adding timestamps, or working with database constraints (NOT NULL, UNIQUE, foreign keys). Use for any task involving data integrity enforcement, relationship definitions, or model-level data validation.
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。
Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
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Use when bypassing EDR/AV to run a payload — hook unhooking, direct/indirect syscalls, PPID spoofing, process injection, AMSI bypass, ETW patching, memory/sleep encryption, behavioral evasion
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Generate and interpret security-research diffs between Windows versions or patch levels using this repo's WinDiff CLI and databases. Use when comparing Windows builds or binaries such as ntoskrnl.exe, ntdll.dll, win32k*.sys, ci.dll, or cng.sys to find changed syscalls, symbols, types, mitigation flags, callbacks, ETW/EtwTi telemetry, code-integrity behavior, drivers, or attack surface. Explain likely intent and security relevance with Windows-internals knowledge, and frame findings for anti-malware/EDR, anti-cheat, and vulnerability-research audiences rather than returning an uninterpreted symbol list.
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。