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Found 11,863 Skills
Evaluate and improve Claude Code commands, skills, and agents. Use when testing prompt effectiveness, validating context engineering choices, or measuring improvement quality.
Understand the components, mechanics, and constraints of context in agent systems. Use when writing, editing, or optimizing commands, skills, or sub-agents prompts.
Coding rules generator — Extract and generate coding-rules.md from project conventions. Scans CLAUDE.md, AGENTS.md, config files, existing source code, and installed skills to produce a unified coding-rules.md that spec-implement uses as a quality gate. English triggers: "Generate coding rules", "Create coding-rules.md", "Extract project rules" 日本語トリガー: 「コーディングルールを生成」「coding-rules.mdを作成」「プロジェクトルールを抽出」
Run agentlint CLI after code changes to catch patterns for AI evaluation. Activate when finishing code modifications, before committing, or when the developer asks to lint, scan, or review code with agentlint. Covers agentlint check, agentlint list, agentlint review, agentlint init, inline suppression, and output interpretation.
Classify a code quality concern into the right enforcement tool and act on it. Activate when the user wants to enforce a pattern, catch a mistake, add a check, create a rule, prevent a practice, guard against regressions, set up linting, improve their feedback loop, or asks "how do I make sure X."
Use when executing implementation plans with independent tasks in the current session
Use when facing 2+ independent tasks that can be worked on without shared state or sequential dependencies
Iris is Redis's umbrella for AI-focused products. Use this skill when integrating with the Iris Redis Agent Memory (RAM) data plane on Redis Cloud — recording session events for an AI agent, creating or searching long-term memories, configuring a memory store, or tuning background memory promotion. Code examples use the official `redis-agent-memory` (Python) and `@redis-iris/agent-memory` (TypeScript) SDKs.
Use ktx to build a self-improving context layer that teaches AI agents how to query data warehouses accurately with approved metrics, semantic layers, and business knowledge
Interactive skill creation and import with automated validation and marketplace compliance. Use when: - "Create a new skill" - "Import an existing skill" - "Create a new agentic pack" - "Add skill to <pack>" - "Build skill for <rh-product>" - User mentions "skill builder", "contribute", "new skill", "import skill", or "new pack" Two modes: create from scratch or import existing SKILL.md. Guides through discovery, definition, generation, and validation. Enforces SKILL_DESIGN_PRINCIPLES.md and agentskills.io spec.
Install and update manually hosted skills in this repository, keeping canonical skill sources under `skills/`, installed output under `.agents/skills`, and `skills-lock.json` in sync. Use when asked to add, refresh, verify, or dogfood repo-local skills for this shared agent setup.
Programmatically create and manipulate Obsidian Canvas (.canvas) files using JSON Canvas Spec 1.0. Enables agents to generate visual flowcharts, architecture diagrams, and planning boards. Use when creating or editing visual canvas files.