「power」の検索結果

Find similar vulnerabilities and bugs across codebases using pattern-based analysis. Use when hunting bug variants, building CodeQL/Semgrep queries, analyzing security vulnerabilities, or performing systematic code audits after finding an initial issue.

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 and fine-tune transformer language models using TRL (Transformers Reinforcement Learning). Supports SFT, DPO, GRPO, KTO, RLOO and Reward Model training via CLI commands.

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。

Discover the active AWS profile, region, account, and caller identity before SageMaker or AWS work. Use when local AWS context is needed or unspecified; do not use for cloud-agnostic model planning.

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 a feature architecture by analyzing existing codebase patterns and conventions, then provide a comprehensive implementation blueprint with specific files to create or modify, component designs, data flows, and a build sequence. Use this skill when the user asks for an architecture design, an implementation plan for a non-trivial feature, or when dispatched as a sub-task during feature-dev architecture phase.

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。

Review a pull request or a set of code changes for bugs, logic errors, and project-convention violations using a confidence-filtered, multi-agent process. Use this skill when the user asks to review a PR, audit pending changes, or inspect a diff for problems before merging.

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 CodeQL database creation and security queries, add data-extension models, or process CodeQL SARIF. Use when CodeQL is explicitly requested; use security-review for a broader manual security review.

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。

Deeply analyze an existing codebase feature by tracing execution paths, mapping architecture layers, understanding patterns and abstractions, and documenting dependencies. Use this skill when you need to understand how a feature works before modifying or extending it, when dispatched as a sub-task during feature-dev exploration, or when the user asks "how does X work in this codebase".

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 and improve project-rules files (AGENTS.md, CLAUDE.md, .agents/instructions, local overrides) so the agent keeps accurate project context. Use when the user asks to check, audit, review, update, improve, or fix their AGENTS.md or CLAUDE.md, mentions "project rules maintenance" or "agent context optimization", or when the codebase has changed enough that the rules file may be stale. Scans the repository for every rules file, grades each against a quality rubric, outputs a quality report, and applies targeted edits only after user 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。

Audit GitHub Actions that run AI agents for prompt injection, unsafe interpolation, sandbox gaps, and permissive actor rules. Use for agentic CI workflows, not general application code review.

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 and publish a Gradio Hugging Face Space for a user-provided image or video LoRA, including pipeline selection and model-card settings. Use for LoRA demos; use huggingface-spaces for general Space work.

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 distinctive, production-grade frontend interfaces with high design quality and accessible markup. Use this skill when the user asks to build or beautify web components, pages, applications, landing pages, dashboards, artifacts, or React/HTML/CSS UI. Generates creative, polished code that avoids generic AI aesthetics, then self-checks it against an objective accessibility and quality rubric.

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。

Agents should invoke this skill when choosing patterns, designing traits/interfaces/components, deciding abstraction boundaries, evaluating dependency injection/callbacks, or comparing implementation approaches in Rust, TypeScript/React, or Django/Python.

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。

Rewrite AI-generated copy in Spanish and English so it sounds direct, concrete and human while preserving meaning, facts and numbers exactly. Use when drafts feel robotic, verbose or translated and need short active sentences without em-dashes.

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。

Guide the creation of high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when the user wants to build an MCP server to integrate an external API or service, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).

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。

Identify error-prone APIs, dangerous configuration, and footgun designs that enable security mistakes. Use for API ergonomics, misuse resistance, secure defaults, or pit-of-success reviews; use code-review for general defects.

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 a repository-grounded AppSec threat model covering assets, trust boundaries, attackers, abuse paths, and mitigations. Use only for explicit threat-modeling requests, not general architecture or code review.

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。

Perform a focused security review of pending git changes to identify high-confidence security vulnerabilities with real exploitation potential. Use this skill when the user asks for a security review, security audit, vulnerability scan, or wants to check pending changes on a branch for security issues before merging. This is NOT a general code review.

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。

Parses and processes SARIF files from static analysis tools like CodeQL, Semgrep, or other scanners. Triggers on "parse sarif", "read scan results", "aggregate findings", "deduplicate alerts", or "process sarif output". Handles filtering, deduplication, format conversion, and CI/CD integration of SARIF data. Does NOT run scans — use the Semgrep or CodeQL skills for that.

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 Semgrep static analysis across a codebase, optionally using Semgrep Pro for cross-file taint analysis. Use when Semgrep or a static-analysis scan is requested; use security-review for a manual 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。

Hugging Face CLI to estimate the required memory to load Safetensors or GGUF model weights for inference from the Hugging Face Hub.

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。

Find and compare recommended Hugging Face models for a task using benchmarks, model size, and device constraints. Use for model selection questions; use huggingface-local-models for GGUF setup and hf-cli for Hub operations.

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 Gradio web UIs and demos in Python. Use when creating or editing Gradio apps, components, event listeners, layouts, or chatbots.

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 evaluations for Hugging Face Hub models using inspect-ai and lighteval on local hardware. Use for backend selection, local GPU evals, and choosing between vLLM / Transformers / accelerate. Not for HF Jobs orchestration, model-card PRs, .eval_results publication, or community-evals automation.

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。