343 件 ・ 関連度順
概要と使いどころ
Use when starting ANY conversation involving Bethesda Game Studio modding, MO2, xEdit, or modpack curation. Bootstrap that loads the toolkit overview, lists available task skills, and enforces the hard rules of this plugin. Auto-injected by the OpenCode plugin's chat.messages.transform hook and by the hooks/ session-start chain in Claude Code and Codex.
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。
Talk to other coding agents on this machine (Claude Code, Codex, Copilot, Cursor, Gemini, Grok, OpenCode) through the magents MCP. Use when the user asks what another agent was working on, wants to carry on that work, send them a message, start an independent agent session, or check the shared inbox. Prefer magents MCP tools over hunting session files on disk.
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。
Orchestrate a configurable, multi-member CLI planning council (Codex, Claude Code, Gemini, OpenCode, or custom) to produce independent implementation plans, anonymize and randomize them, then judge and merge into one final plan. Use when you need a robust, bias-resistant planning workflow, structured JSON outputs, retries, and failure handling across multiple CLI 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。
Drive the Skills Manager CLI (`skm`) to initialize the hub, adopt unmanaged skills, list/enable/disable skills per AI tool, and doctor/fix symlink sync. Use whenever the user or an agent needs to manage skills from a terminal, SSH session, CI job, or headless machine; when a skill is missing in Claude Code, Codex, Cursor, Gemini, OpenCode, or any other supported tool; when asked to run skm / Skills Manager CLI; or when setting up Skills Manager without the GUI. Do not use for `npx skills` / skills.sh search (different CLI), for writing SKILL.md content (skill-creator), or for marketplace browse/install (not in skm).
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。
Drive one AI agent from another through kolu's terminals: spawn a Claude Code / Codex / opencode session in a PTY, prompt it, watch the screen for its reply, read it, and prompt again — a create→send→snapshot loop run with the `kaval-tui` CLI directly (no MCP). `kaval-tui` writes input and reads scrollback; `pulam-tui` adds a precise agent-state done-signal when you drive hooked terminals. Triggers on "drive another agent", "send a prompt to a terminal agent", "have one agent prompt another", "agent drives agent", "orchestrate agents in terminals", "make Claude drive Codex", "prompt the agent running in that terminal", or wiring a loop where one coding agent supervises another.
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。
Cross-tool AI consultation — get second opinions from Gemini CLI, Codex CLI, Claude Code, OpenCode, or Copilot CLI
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。
Local user-profile maintenance skill for Codex, Claude Code, OpenClaw, OpenCode, and other agent harnesses. Use only when the user explicitly invokes $user-profile-keeper to create, initialize, update, query, correct, delete, export, or audit a local persistent user profile. It can extract durable collaboration preferences, requirement-expression habits, capability boundaries, recurring omissions, risk preferences, privacy boundaries, and typical events from the current session into auditable, confirmable, retractable local profile data. Do not auto-invoke, upload profile data, or replace task-clarifier's normal clarification flow.
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。
Scaffold a ready-made AI agent harness in one command from the 19 published @metaharness/* example packages — 9 host integrations (Claude Code, Codex, Hermes, pi.dev, OpenClaw, RVM, Copilot, OpenCode, GitHub Actions) + 10 vertical pods (devops, research, trading, support, legal, coding, education, sales, gaming, repo-maintainer).
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。
Called when users need to create new OpenCode skills. Automatically creates directory structure and writes SKILL.md files conforming to specifications.
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 the prompt surface of Avibe Agents across backends (Claude, Codex/GPT, OpenCode) — global and project rules, Agent system prompts, Skills, delegation briefs, and Task and Watch messages — using real run evidence. Use when an Agent misbehaves (stalls, over-asks, over-reaches, ignores or over-applies a rule), after a model or backend change, or when the user asks to review, clean up, or tighten 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。
Investigate Elixir/Phoenix bugs root-cause first. Reproduce failures, cite evidence, and use optional native OpenCode subagents only when useful.
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 a phase plan should be peer-reviewed by other AI CLIs before execution, or the user asks for a cross-AI review. Invokes Gemini, Claude, Codex, or OpenCode and writes REVIEWS.md for /gsd-plan-phase --reviews.
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 or fine-tune SentenceTransformer bi-encoders, CrossEncoder rerankers, or SparseEncoder models, including losses, negatives, evaluation, distillation, LoRA, and Matryoshka. Use for sentence-transformers training tasks.
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。
Turn one confirmed security finding into a disclosure-ready GitHub advisory with root cause, proof of concept, impact, and source evidence. Use for reporting an established vulnerability, not discovering or validating one.
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 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。
Run Hugging Face models in JavaScript or TypeScript with Transformers.js, WebGPU, or WASM across browser, Node.js, Bun, and Deno. Use for client-side or JS-runtime inference, not Python training.
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。
Agents should invoke this skill for code reviews, linting/formatting setup, maintainability checks, complexity concerns, warning cleanup, coding standards, or quality gates in Rust, TypeScript, Python, shell, and mixed repos.
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。
Review code for bugs, logic errors, security vulnerabilities, code quality issues, and adherence to project conventions, using confidence-based filtering to report only high-priority issues that truly matter. Use this skill when reviewing a small set of changes locally (such as unstaged diff), when dispatched as a sub-task during feature-dev quality review, or when the user wants a critique of a specific file or function.
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。
Plan and coordinate a model deployment to Amazon SageMaker, including serving stack and real-time versus async inference. Use as the entry point for SageMaker hosting requests, before image, IAM, and endpoint implementation 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。
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。