「capabilities」の検索結果

Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Claude's capabilities with specialized knowledge, workflows, or tool integrations.

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。

Extend the Strategy Coach agent with new capabilities. Use when user says "add coaching phase", "new research pillar", "extend agent", "add strategic output", or asks to enhance the AI coaching methodology.

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。

Assesses agent capabilities by analyzing agent files and determining what tasks they can handle. Use when you need to evaluate if an existing agent can handle a task or if a new agent is needed. Reviews agent descriptions, skills, and workflows to determine capability match.

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 for creating new skills in Kai's personal AI infrastructure. Use when user wants to create, update, or structure a new skill that extends capabilities with specialized knowledge, workflows, or tool integrations. Follows both Anthropic skill standards and PAI-specific 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。

Search and read locally-stored Claude documentation covering Claude Code CLI, Claude API (Messages, tool use, vision, streaming, batch), Agent SDK (Python and TypeScript), prompt engineering, and all Anthropic platform docs. Use this skill whenever the user asks about Claude Code features (hooks, MCP servers, skills, plugins, settings, permissions, keybindings, sub-agents), the Anthropic API or any of its SDKs (Python, TypeScript, Go, Java), the Agent SDK (sessions, hooks, custom tools, MCP), model capabilities (context windows, extended thinking, pricing, rate limits, vision), prompt engineering best practices, or troubleshooting any Claude-related error. This skill provides instant access to official documentation files without web searches — always prefer it over web lookups for Claude and Anthropic topics.

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 govern local Skills: inventory, usage evidence, duplicates, broken links, Core/On-demand proposals, Plans, Receipts, Apply, and Undo. Also find a specialized local Skill when the user requests one or the task would benefit from instructions beyond the visible catalog. Ordinary work can proceed with the Agent's existing capabilities. Not for installing third-party Skills or migrating, distributing, synchronizing, or repairing shared Skill-manager directories.

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。

The official Brave Search MCP server integrates the Brave Search API with AI assistants, providing comprehensive web search, local business search, image search, video search, news search, and AI-powered summarization capabilities through the Model Context Protocol.

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。

AI Engine is a WordPress plugin by Meow Apps that connects sites to OpenAI, Claude, Gemini, and other models while exposing WordPress actions through MCP and REST interfaces. This skill helps agents configure providers, enable the plugin's MCP capabilities, and automate content, chatbots, media, and site-management workflows from WordPress.

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。

Updates tdmcp docs and roadmap for newly shipped or newly merged capabilities. Use when asked to update docs, update the roadmap, reconcile release docs, document new features, sync docs with CHANGELOG/package release, refresh cookbook examples after a feature wave, re-run/continue/fix a docs-roadmap pass, or check whether docs/ROADMAP.md is current. Runs a sub-agent workflow: release audit -> roadmap/docs edit + cookbook sync -> QA.

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。

Survey the whole tdmcp project and produce a prioritized list of NEW features it could implement — across artist controls, CLI/DX, AI/LLM integration, and TouchDesigner depth. Use whenever the user wants to brainstorm, discover, survey, list, or audit what features/tools/effects/controls/commands/prompts/capabilities tdmcp *could* add, asks 'what could we build / what's missing / what are the opportunities / ideas for the project', or wants a feature backlog or gap analysis. Also for follow-ups: re-run, refresh, update, re-survey, deepen, or re-prioritize the backlog, or survey just one surface (e.g. 'CLI ideas only'). This is the IDEATION harness — it produces a list to decide from; it does NOT build. When the user has already chosen a feature and wants it implemented/shipped/added, use tdmcp-pipeline instead.

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。

Linux local privilege escalation from a low-privilege shell to root. Covers quiet vs loud enumeration, sudo/GTFOBins, CVE-2025-32463 chwoot and CVE-2025-32462 host bypass, polkit/udisks CVE-2025-6018/6019, SUID/capabilities, systemd timers/units, cron/PATH/LD_PRELOAD, dangerous groups and docker.sock, host-local container escape, and version-gated kernel LPE including Copy Fail CVE-2026-31431. Use when the operator has a Linux foothold and needs root. Kubernetes RBAC stays in /k8s. Endpoint blocks hand off to /edr-bypass then return here.

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 for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Claude's capabilities with specialized knowledge, workflows, or tool integrations.

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 or update a Wardley Map of how any domain delivers value. Maps the needs it serves, the capabilities involved, how each is evolving (genesis to commodity), and strategic plays. Applies to any domain, not only software.

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。

Synthesizes exploration findings into unified analysis with deep investigation capabilities and completeness evaluation (converted from 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。

Explores codebases to find relevant files, trace execution paths, and map architecture with team communication capabilities for collaborative analysis (converted from 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。

Synthesizes exploration findings into unified analysis with deep investigation capabilities (shell commands, git history, dependency analysis) and completeness evaluation (converted from 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。

Sequential thinking and structured problem solving. Break down complex problems into steps with revision and branching capabilities. Use for multi-step analysis, planning, and hypothesis 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。

Intent-to-command mappings for fullstack-typescript tech pack features.

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。

Writes and maintains user-facing documentation for the SDD plugin. Proactively detects when docs are out of sync with plugin capabilities.

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 command, adapter, or runtime smoke depends on proving an agent runtime's capabilities before trusting command routing, writes, shell access, or validation claims.

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。

Establish and maintain longitudinal baselines of CLI binary contents across versions. Covers marker selection by category (API / identity / config / telemetry / flag / function), weighted scoring, threshold-based system-presence detection, and per-version baseline records. Use when tracking a feature's lifecycle across releases, when probing for dark-launched or removed capabilities, or when verifying that a scanning tool itself still catches known-good markers on old binaries.

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。

Install skills, agents, and teams from agent-almanac into any supported agentic framework using the CLI. Covers framework detection, content search, installation with dependency resolution, health auditing, and manifest-based syncing. Use when setting up a new project with agentic capabilities, installing specific skills or entire domains, targeting multiple frameworks simultaneously, or maintaining a declarative manifest of installed 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。

Deploy machine learning models to production serving infrastructure using MLflow, BentoML, or Seldon Core with REST/gRPC endpoints, implement autoscaling, monitoring, and A/B testing capabilities for high-performance model inference at scale. Use when deploying trained models for real-time inference, setting up REST or gRPC prediction APIs, implementing autoscaling for variable load, running A/B tests between model versions, or migrating from batch to real-time 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。

Evolve an existing agent definition by refining its persona in-place or creating an advanced variant. Covers assessing the current agent against best practices, gathering evolution requirements, choosing scope (refinement vs. variant), applying changes to skills, tools, capabilities, and limitations, updating version metadata, and synchronizing the registry and cross-references. Use when an agent's skills list is outdated, user feedback reveals capability gaps, tool requirements have changed, an advanced variant is needed alongside the original, or the agent's scope needs sharpening after real-world use.

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。