「wrap」の検索結果

Use when an agent or harness needs to drive the full tiny.place network from the command line — register an @handle identity, search the open directory, read/post to channels and broadcasts, send and acknowledge encrypted messages, manage inbox items, fund and win bounties (contest-style work), read reputation, fetch pricing, manage approved signers, settle/verify x402 payments and subscriptions, and read the ledger. Wraps the flagship TypeScript SDK CLI (`tinyplace`), which carries the complete API surface.

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 structured user interview script with warm-up, core exploration, and wrap-up sections. Use when preparing for user research interviews to ensure consistent, insightful conversations.

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 production-quality CLIs with language detection and a five-step approval-gated workflow. Use when wrapping an existing module or app. Don't use for GUI/TUI apps, web APIs, or one-off shell scripts.

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 the Crabbox wrapper for OpenClaw remote validation across Linux, macOS, Windows, and WSL2, including delegated Blacksmith Testbox proof. Report the actual provider and id.

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。

Generate a self-contained handoff prompt that lets the user continue work in a fresh coding agent session with empty context. The prompt captures everything a new agent needs: project, goal, progress, decisions, current state, and remaining work. **Triggers — use this skill when:** - User says "handoff", "hand off", "hand-off" - User asks to "generate a continuation prompt" - User says "give me a prompt to continue this" - User asks to "wrap up for a new session" - User says "context transfer", "session transfer" - User asks "how do I pick this up later" - User wants to "pass this to another agent"

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 diagnosing Superloopy doctor, install, wrapper, plugin cache, hook bootstrap, bundled agents, marketplace, Codex, Claude Code, stale-version, evidence-floor, or host-wiring health problems.

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。

Automated iterative agent runner for spec-based development in Kiro. Wraps kiro-cli in a self-correcting bash loop that picks up tasks from a Kiro spec, implements them one at a time, verifies against exit criteria, and accumulates corrections and codebase patterns across iterations. Use this skill when the user mentions "ralph loop", "ralph", "spec loop", "iterative spec runner", "run my spec tasks automatically", "kiro spec automation", "self-correcting agent loop", "implement spec tasks in a loop", "run kiro-cli in a loop", "automated task implementation", or wants to drive a Kiro spec to completion through repeated agent iterations. Also use when the user wants to set up, configure, troubleshoot, or understand the Ralph Loop workflow — including progress tracking, corrections, codebase patterns, timing logs, and the summary dashboard.

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。

Simplifies recently changed or explicitly scoped code while preserving observable behavior, error semantics, side effects, ordering, and public contracts. Use for readability cleanup, dead-code removal, reducing nesting, eliminating redundant wrappers, or making an implementation easier to maintain. Not for architecture redesign, new behavior, speculative cleanup, or a refactor without an adequate safety net.

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。

Package spike findings into a persistent project skill for future build conversations

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。

Package sketch design findings into a persistent project skill for future build conversations

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。

UV unwrap, atlas-map, project textures, use alpha decals, bake maps/lightmaps, and prepare texture-driven Blender assets for glTF/GLB export. Use whenever the user provides texture packs, texture atlases, decals, UV layouts, lightmaps, wants a texture to fit a mesh 1:1, or reports stretched/off textures. Pairs with reference-to-3d for template-accurate reconstruction and blender-materials for PBR values.

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。

OpenAI Codex CLI wrapper — three modes. (gstack)

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。

Session state backend for WDS. Called by wrap, start, and handoff tools — never directly by users. Writes to progress/ in the project repo.

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。

General mode operational spec for the team lead. Returns lead identity, facilitator identity, suggest-members guidance, and phase arc for general-purpose teams. The silent fallback when outcomes fit no specific mode, and a wrapper base for custom workflows — it has no shortcut command.

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, test, inspect, install, and deploy MCP servers with FastMCP in Python. Use when creating a new MCP server, wrapping an API or database as MCP tools, exposing resources or prompts, or preparing a FastMCP server for Claude Code, Cursor, or HTTP deployment.

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 SwiftUI code for view composition, state management, performance, and accessibility. Use when reviewing .swift files containing SwiftUI views, property wrappers (@State, @Binding, @Observable), or UI code.

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 a Meta-Harness-style optimization loop NATIVELY — automatically search over the scaffolding around a FIXED base model (memory, retrieval, context construction, prompt templates, summarization, tool-selection logic) by proposing candidate variants, scoring each on a cheap deterministic eval, and keeping a Pareto frontier of quality vs cost — using native Agent / Workflow / loop tools instead of a standalone Python harness. Use this whenever the user wants to optimize, evolve, tune, distill, or search over a harness, scaffold, prompt system, memory or retrieval policy, context-assembly code, or summarizer while keeping the model fixed; whenever they mention Meta-Harness, harness optimization, scaffold evolution, automatic prompt/memory optimization, an evolutionary or Pareto search over candidate implementations, or "make the harness/agent better without retraining"; and whenever the gain must come from the code AROUND the model rather than the model weights. Reproduces the Meta-Harness paper's method natively, with no claude_wrapper.py and no metered solver API.

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。

プリミティブ型をドメイン固有型でラップすべきか否かの判断を支援する。 Primitive Obsession（プリミティブ型の過剰使用）と Value Object Obsession（過剰なラップ）の 両極端を避け、投資対効果に基づく合理的な判断基準を提供する。 Value Objectの定義が文脈（PofEAA/DDD/一般）で異なることによる用語混乱の防止も含む。 コードレビュー、新規実装、設計議論時にプリミティブ型のラップ判断が必要な場合に使用。 対象言語: 言語非依存（Rust, TypeScript, Java, Kotlin, Scala, Go, Python等すべて）。 トリガー：「この値をラップすべきか」「プリミティブ型のままでいいか」 「Value Objectにすべきか」「型を作りすぎでは」「Primitive Obsession」 「ラップしすぎ」「型が多すぎる」「Stringのままでいいか」 といったプリミティブ型ラップ判断関連リクエストで起動。

Inspect and update RIPDPI execution steps through the repository-pinned mdtask wrapper.

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 the Crabbox wrapper for OpenClaw remote validation across Linux, macOS, Windows, and WSL2, including delegated Blacksmith Testbox proof. Report the actual provider and id.

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 parallel agents in a convoy or across git worktrees hit a write conflict on shared state — a git tree, a config, or a shared document — and you must decide whether the conflict is semantically real. Classifies each conflicting hunk as independent (keep both), redundant (pick one), or a genuine clash (escalate), instead of blind locks, abort-and-retry, or escalate-everything. Sits ABOVE refinery-merge and NEVER blind auto-merges: it wraps an LLM judgment, defaults to escalate on any uncertainty, and unconditionally escalates any hunk touching .planning/, config, credentials, or safety-critical paths. Backed by CoAgent (arxiv 2606.15376v1). Triggers on write conflicts between parallel agents on shared repo state.

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。

Classify an image and describe its contents with a local vision LLM. Wraps kuzco's tool_llm_image_classification() ellmer tool. Use when the task needs image classification, object identification, or a structured description of what an image contains.

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 a custom computer-vision prompt against an image with a local vision LLM. Wraps kuzco's tool_llm_image_custom() ellmer tool. Use when none of the specialized kuzco image skills (classification, OCR, recognition, sentiment, alt text) fit the task.

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 the sentiment or mood conveyed by an image with a local vision LLM. Wraps kuzco's tool_llm_image_sentiment() ellmer tool. Use when the task needs the emotional tone of an image, e.g. for content moderation, marketing, or curation.

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。