Discover skills
Discover and install skills to enhance your AI agent's capabilities.
| Name | Contains | Score |
|---|---|---|
softaworks/agent-toolkit This skill should be used when creating a Claude Code slash command. Use when users ask to "create a command", "make a slash command", "add a command", or want to document a workflow as a reusable command. Essential for creating optimized, agent-executable slash commands with proper structure and best practices. | Skills | |
Jamie-BitFlight/claude_skills Verifies claims in backlog items, skill documentation, or plugin content against primary sources using web lookups. Spawns parallel verification agents that must use WebFetch/WebSearch/gh — training data recall is explicitly rejected as evidence. Produces VERIFIED/REFUTED/INCONCLUSIVE verdicts with citations. Use when items are marked UNVERIFIED or when verifying tool API claims, CLI flags, or documented software behavior. | Skills | |
shousper/claude-kit Removes git worktrees safely, cleans up associated branches, and pulls latest mainline after removal. Use when finished with a worktree, done with a branch, cleaning up after a merge or PR, abandoning work in a worktree, or when "git worktree list" shows stale entries. Checks for uncommitted changes, verifies no open PRs before branch deletion, and handles force-removal of locked worktrees. | Skills | |
rohitg00/pro-workflow End-of-session ritual that audits changes, runs quality checks, captures learnings, and produces a session summary. Use when saying "wrap up", "done for the day", "finish coding", or ending a coding session. | Skills | |
upstash/skills Work with any Upstash TypeScript/JavaScript SDK including Redis, Box, QStash, Workflow, Vector, Search and Ratelimit. Use when the user is working with any Upstash product or SDK. | Skills | |
genkit-ai/skills Develop AI-powered applications using Genkit in Go. Use when the user asks to build AI features, agents, flows, or tools in Go using Genkit, or when working with Genkit Go code involving generation, prompts, streaming, tool calling, or model providers. | Skills | |
mckinsey/vizro Use this skill when choosing chart types, applying Plotly Express conventions, configuring colors, building KPI cards, or adding tables (AG Grid) to Vizro dashboards. Activate when the user asks which chart fits their data, needs custom chart functions, wants to set colors or palettes, is creating KPI metric cards, or needs a tabular detail view. | Skills | |
ArabelaTso/Skills-4-SE Incrementally implement new features in Java repositories from natural language descriptions. Use when adding functionality to existing Java codebases (Maven or Gradle projects). Takes a feature description as input and outputs modified repository with implementation code, corresponding JUnit tests, and verification that all tests pass. Supports method additions, new class creation, and method modifications with proper Java conventions. | Skills | |
hookdeck/webhook-skills Receive and verify ElevenLabs webhooks. Use when setting up ElevenLabs webhook handlers, debugging signature verification, or handling call transcription events. | Skills | |
jeremylongshore/claude-code-plugins-plus-skills Production readiness checklist for Gamma integration. Use when preparing to deploy Gamma integration to production, or auditing existing production setup. Trigger with phrases like "gamma production", "gamma prod ready", "gamma go live", "gamma deployment checklist", "gamma launch". | Skills | |
jeremylongshore/claude-code-plugins-plus-skills Execute Fireflies.ai production deployment checklist with health checks and rollback. Use when deploying Fireflies.ai integrations to production, preparing for launch, or implementing go-live procedures. Trigger with phrases like "fireflies production", "deploy fireflies", "fireflies go-live", "fireflies launch checklist". | Skills | |
headout/pm-os-marketplace The L1 Reviewer is the quality gate for all product specs at Headout before they reach Atish. Use this skill to review any PRD, spec, or requirements document for completeness, logical soundness, scenario coverage, metric clarity, and design coherence. Structured critique, not a polish pass. Rejects incomplete specs with specific, actionable failure reasons. A passing spec is ready for Atish. A failing spec returns to the PM with exactly what needs fixing. Trigger for: "review this PRD", "is this spec good enough", "L1 check", "review before I send to Atish", or whenever the Spec Writer finishes a draft. Checks both the LOGIC layer (scenario coverage, metric rigor, AC quality) and the DESIGN layer (design coherence, prototype alignment). | Skills | |
Python project structure — pyproject.toml, src layout, __init__.py, .gitignore, dependency groups, type hints, py.typed, test structure, entry points, ruff/mypy configuration Contains: python-project-structure Python project structure best practices — pyproject.toml, src layout, __init__.py, .gitignore, virtual environments, type hints, py.typed, test structure, entry points, dependency groups, and tool configuration. Use when starting a new Python project, restructuring an existing one, setting up a Python package, configuring dependencies, or when reviewing project organization. Triggers on: new FastAPI/Flask/Django project, CLI tool setup, Python library scaffolding, "where should I put this", monolithic files, missing pyproject.toml, setup.py migration, or dependency management questions. | Skills | |
v0.2.2 Interact with Shortcut - view, search, update, and create stories, epics, objectives, iterations, docs, labels, and teams using the short CLI. IMPORTANT - When you see URLs matching `app.shortcut.com/*`, use this skill instead of WebFetch. Contains: shortcut Manage Shortcut stories, epics, objectives, iterations, docs, labels, teams, and more using the short CLI — view, search, update, and create items for project management and issue tracking. Use when the user mentions Shortcut, references tickets or tasks in Shortcut, or when you see URLs matching `app.shortcut.com/*` (use this skill instead of WebFetch). | Skills | |
v0.1.4 Secure PubNub applications with Access Manager, encryption, and TLS Contains: pubnub-security Secure PubNub applications with Access Manager, encryption, and TLS | Skills | |
v0.4.10 Koog 1.0 idioms, gotchas, and scaffolding skills for Kotlin agents on the JVM Contains: add-observability Install OpenTelemetry observability into a Koog 1.0 agent — the multiplatform feature, the GenAI span/metric vocabulary, and one of the built-in backend integrations (Langfuse, Weave, Datadog, raw OTLP). Use when the user asks to "add telemetry", "wire up observability", "send traces to Langfuse", "add OpenTelemetry", "instrument the agent", or names any specific backend. add-persistence Add checkpoint-and-resume to a Koog 1.0 agent. Two modes — `runFromCheckpoint` for replay-only use without installing a feature, and the full Persistence feature when you need rolling checkpoints, replay-with-modifications, or planner-agent durability across restarts. Use when the user asks to "make the agent resumable", "save progress", "checkpoint the agent", "restart from where it left off", or describes a long-running workflow that may be interrupted. add-rag Add Retrieval-Augmented Generation to a Koog 1.0 agent — pick an embedding source (LLM-backed or local), index documents into a vector store, and query the store inside the agent's prompt pipeline or as a tool. Use when the user asks to "add RAG", "embed and search documents", "use a vector store", "build retrieval-augmented generation", or describes grounding the LLM in a corpus. add-structured-output Get typed structured output from a Koog 1.0 agent — pick between `nodeLLMRequestStructured` (graph DSL, schema-driven JSON) and `responseProcessor` (top-level on the agent factory, simpler shape). Defines the `@Serializable` output class and wires it into the strategy or the factory. Use when the user asks to "return JSON", "get a typed response", "use structured output", "return a data class from the agent", or shows a `data class` they want as the agent's output. add-token-budgeting Add token-budgeting and per-provider tokenizer support to a Koog 1.0 agent — install the tokenizer feature, set per-run or per-node budgets, and react to budget exhaustion (compress history, abort, swap models). Use when the user asks to "limit tokens per run", "add a token budget", "prevent runaway agent costs", "use a tokenizer", or describes cost containment requirements. add-tool Add a new tool to an existing Koog 1.0 agent. Pick the right registration style (@Tool + ToolSet annotation, Tool[TArgs,TResult] subclass, or sub-agent-as-tool), define the args, and wire the tool into the agent's ToolRegistry. Use when the user asks to "add a tool to my agent", "expose something to the LLM", "let the agent call a function", or "wrap this agent as a tool for another agent". Assumes a scaffolded Koog 1.0 project — for new projects start with the scaffold-agent skill. author-strategy Author a custom graph strategy for a Koog 1.0 agent — pick the right node types, chain tool execution correctly, build edges with the infix vocabulary, and reach for subgraphs (`subgraphWithTask`, `subgraphWithVerification`) when steps deserve their own model, prompt, or tool subset while still sharing the agent's message history. Use when the user asks to "write a custom strategy", "build a graph for the agent", "author a strategy DSL", "add a verify-and-fix loop", "use subgraphs", or describes multi-step orchestration that won't fit inside `singleRunStrategy()`. Subgraphs are not for context isolation — for an independent agent that does not see the parent's history, use `Skill(skill: "add-tool")` Step 3 (sub-agent-as-tool). cache-llm-calls Add in-process caching of LLM calls to a Koog 1.0 agent via `prompt-executor-cached` — cache whole prompt→response pairs locally so identical calls skip the API. Distinct from provider-side Anthropic prompt caching (covered by `enable-prompt-caching`). Backends include in-memory (default), file-based, and Redis. Use when the user asks to "cache LLM responses", "avoid duplicate API calls", "add a response cache", "cache to Redis", or describes repeated identical prompts in dev/test. define-prompt Author prompts for a Koog 1.0 agent using the `prompt { ... }` DSL — system messages, user turns, few-shot examples, mixed media, and runtime augmentation via the `SystemPromptAugmenter` / `UserPromptAugmenter` family. Use when the user asks to "write a system prompt with examples", "add few-shot examples", "build a prompt", "augment the prompt at runtime", or moves beyond the single-string `systemPrompt` parameter on the factory. domain-model-subtask-pipeline Author a Koog 1.0 agent as a typed pipeline of domain-modeled subtasks — tools sliced by access pattern (read / write / communication) into separate ToolSets, inter-subtask handoffs as `@Serializable` `@LLMDescription`-annotated data classes (not text prompts), each subtask wired with `subgraphWithTask[In, Out]` using its own model and tool subset, self-correction loops via `subgraphWithVerification[T]` + `CriticResult[T]`. The integration pattern Koog's own banking demo uses. Use when the user asks to "model the agent as a pipeline", "build a multi-stage agent with typed handoffs", "give each stage its own tools", "build a verify-and-fix loop with typed data", or describes a workflow with distinct phases that should hand structured data to each other. enable-prompt-caching Enable Anthropic prompt caching for a Koog 1.0 agent — automatic caching is on by default in 1.0, but explicit `cacheControl` breakpoints let you control which parts of long prompts get cached. Surfaces cache-hit metrics through the OpenTelemetry token-usage span. Use when the user asks to "enable prompt caching", "reduce Anthropic costs", "add cache_control", "set cache breakpoints", or describes expensive repeated calls with shared system prompt content. handle-agent-events Install per-step event handlers on a Koog 1.0 agent — tool-call start/end, LLM request/response, agent finish, error events. Useful for stdout logging during development, visualizing planner decisions on stage during demos, or pushing events to a non-OTel sink. Use when the user asks to "log tool calls", "see what the agent is doing", "add event handlers", "visualize the planner", "trace each step" — anything where the goal is human-readable per-step output, not production metrics. manage-state Work with Koog 1.0 agent state — typed key-value `storage` on `AIAgentContext`, history compression strategies (TL;DR, sliding window, fact retrieval), and the `LongTermMemory` feature (which replaces the removed `AgentMemory`) for cross-session recall. Use when the user asks to "store state across nodes", "compress conversation history", "remember things across sessions", "add long-term memory", or names any of these surfaces. migrate-from-0-x Migrate a Koog 0.x codebase to 1.0. Koog 1.0 (2026-05-21) removed every @Deprecated API in one sweep — 0.x code does not compile against 1.0. Walks through construction surface, planner module split, graph DSL renames, Java interop overhaul, HTTP transport decoupling, memory APIs, OpenTelemetry, persistence, prompt package move, retired models, and JDK/tooling minima. Use when the user asks to "migrate from 0.x", "bump Koog to 1.0", "upgrade Koog", or shows code that uses pre-1.0 APIs (e.g., AIAgent.invoke, AgentMemory, AIAgentPlannerStrategy.builder). model-planner-subtasks Model a problem domain as a tree of typed subtasks the Koog 1.0 planner can execute — the `PlannerNode` data model, parallel vs sequential composition, accessing in-flight subtasks through `AIAgentStorage`, retry-on-parse-failure edges, and TL;DR compression between phases. Goes deeper than `use-planner` (which only picks the planner factory). Use when the user asks to "decompose into subtasks", "model the agent's work as a tree", "compose parallel and sequential steps", "retry failed subtasks", or describes a richer planner usage than the basic factory. persist-chat-history Persist a Koog 1.0 agent's chat history to a durable backend — JDBC database (`chat-history-jdbc`), AWS storage (`chat-history-aws`), or SQL-typed chat memory (`chat-memory-sql`) — so conversations survive process restarts and can be retrieved by session ID. Distinct from generic agent persistence (state checkpoints) and from LongTermMemory (fact retrieval). Use when the user asks to "save chat history", "persist conversations", "resume a chat by session", "store chat in Postgres / DynamoDB". query-sql-from-agent Give a Koog 1.0 agent the ability to query a SQL database safely — install `agents-features-sql`, register the database connection, and expose schema-aware query tools the LLM can call. Includes safety guidance (read-only by default, schema scoping, parameterized queries). Use when the user asks to "let the agent query the database", "add SQL to my agent", "expose a database to the LLM", or describes data-retrieval needs the LLM should drive. scaffold-agent Bootstrap a new Koog 1.0 Kotlin agent project from scratch: Gradle setup with the right dependencies, JDK 17 toolchain, application Main that constructs an AIAgent via the top-level factory, and an environment-variable wiring for the LLM API key. Use when the user asks to "create a new Koog agent", "start a Koog project", "scaffold an agent app", or provides a directory and says "set up Koog here". Produces a runnable hello-world agent that the user can extend with tools, strategies, or features. Do NOT use when the user is constructing a planner, picking a strategy variant, or naming a specific agent shape inside an existing project — use `use-planner` or `author-strategy` instead. snapshot-and-restore Snapshot a running Koog 1.0 agent's state at arbitrary points and restore later — distinct from the persistence checkpoint loop in `add-persistence`. Snapshots are caller-triggered; persistence is automatic and continuous. Use when the user asks to "snapshot the agent", "save state at this point", "restore from a snapshot", or names the `agents-features-snapshot` module. test-koog-agents Test Koog 1.0 agents deterministically — install `agents-test`, mock the prompt executor with scripted responses, inject a fake `KoogClock` for time-sensitive logic, and assert on tool-call sequences. Use when the user asks to "test the agent", "mock the LLM in tests", "write unit tests for my Koog agent", or describes flaky or expensive tests that hit a real LLM. trace-agent-internals Install the `agents-features-trace` feature to capture detailed internal trace events from a Koog 1.0 agent — node entries, edge transitions, planner decisions, feature lifecycle. Distinct from OpenTelemetry (production signal, GenAI vocabulary) and event handlers (high-level callbacks). Use when the user asks to "debug what the strategy is doing", "trace internal agent decisions", "see why the planner picked that step", or describes deep diagnostic needs. use-attachments Send non-text content (images, files, audio) to the LLM as message attachments in a Koog 1.0 agent — provider-aware encoding and the `attachments` block in the prompt DSL. Use when the user asks to "send an image to the LLM", "use multimodal input", "attach a file", "pass a PDF", or describes input the LLM should process that isn't plain text. use-functional-agent Use `FunctionalAIAgent` — the third concrete agent subtype in Koog 1.0 (alongside `GraphAIAgent` and `PlannerAIAgent`). Wraps a single suspending block, no graph DSL, no planner — just programmer-written logic that calls the LLM and tools directly. Use when the user asks to "skip the graph DSL", "write the agent body as plain code", "use AIAgentFunctionalStrategy", or describes a one-shot agent shape that doesn't warrant a topology. use-llm-node-variants Use a non-default LLM node variant inside a Koog 1.0 strategy — streaming output, multiple-choice sampling, content moderation, or forcing a specific tool call. Use when the user asks for "streaming", "multiple completions / sampling", "moderation", "force one tool", "force the LLM to call a specific tool", or names any of `nodeLLMRequestStreaming`, `nodeLLMRequestMultipleChoices`, `nodeLLMModerateText`, `nodeLLMRequestForceOneTool`. use-planner Pick and wire a planner-driven Koog 1.0 agent — either LLM-based (the LLM picks the next action each turn, optionally with a critic loop) or GOAP (a classical planner searches a typed state space toward a goal). Pulls `ai.koog:agents-planner`, constructs the planner strategy, and wires it into `AIAgent(...)`. Use when the user asks to "use a planner", "let the agent plan", "use GOAP", "build a planning agent", names any of `Planners.llmBased`, `Planners.llmBasedWithCritic`, `Planners.goap`, `PlannerAIAgent`, `agents-planner`, or describes an open-ended task whose step sequence depends on runtime context. wire-a2a Wire the Agent-to-Agent (A2A) protocol — expose a Koog 1.0 agent as an A2A server, or consume a remote A2A server as a client (typically to make a remote agent callable as a tool by a local agent). Use when the user asks to "expose the agent via A2A", "use A2A protocol", "call a remote agent", "register an A2A client", or names any of `a2a-server`, `a2a-client`. wire-acp-server Expose a Koog 1.0 agent through the Agent Client Protocol (ACP) — the lower-level bidirectional protocol used by tooling that needs fine-grained control over agent invocation lifecycle (cancellation, streaming progress, multi-turn negotiation). Use when the user asks to "expose the agent via ACP", "use Agent Client Protocol", "wire ACP", or names the `agents-features-acp` module. wire-ktor-server Expose a Koog 1.0 agent through a Ktor server — install the `koog-ktor` plugin, load agent configuration from `application.conf` / `.yaml`, and register MCP servers inside the plugin block. Use when the user asks to "expose the agent over HTTP", "add Koog to my Ktor app", "wire the Ktor plugin", or describes a server-shaped deployment. wire-mcp-server Connect a Koog 1.0 agent to an MCP (Model Context Protocol) server, using the primary 1.0 Streamable HTTP transport — or fall back to SSE / stdio when the remote server doesn't speak Streamable HTTP yet. Adds the agents-mcp dependency, builds a ToolRegistry from the MCP server's exposed tools, and merges it with the agent's existing tool registry. Use when the user asks to "connect to an MCP server", "use the GitHub MCP server", "add MCP tools to my agent", "wire Playwright MCP" or similar. Assumes a scaffolded Koog 1.0 project. wire-spring-boot Wire Koog 1.0 into a Spring Boot application via `koog-spring-boot-starter` — per-provider autoconfig, `MultiLLMAutoConfiguration` aggregation, and the `application.yml` shape for agent name, model, system prompt, and tools (including MCP entries). Use when the user asks to "use Koog in Spring Boot", "wire the Spring starter", "configure providers via application.yml", "add Koog to my Spring app". | SkillsRules | |
v0.5.3 Implement frontend designs from figma using Chakra UI v3 and Storybook Contains: building-composite-components Implement frontend designs from Figma using Chakra UI v3 and Storybook. Use when implementing UI from Figma files, when user mentions "implement design", "generate code", "implement component", "build Figma design", provides Figma URLs, or asks to build components matching Figma specs. Requires Figma MCP server connection. building-low-level-components Build generic, reusable low-level UI components that extend or complement Chakra UI v3, including form field components. Use when building primitives not tied to a specific page — custom inputs, display elements, layout helpers, form fields, or interactive widgets that Chakra doesn't provide out of the box. Use when user mentions "build a component", "create a reusable component", "extend Chakra", "low-level component", "UI primitive", "form component", "form field", "custom input", or asks to build something generic that could be used anywhere. Requires Figma MCP server connection if building from a design. building-page-components Build page-level components that assemble composite and low-level components, handle data loading and mutations, and define page layout. Use when building a new page or route, wiring up data fetching, connecting mutations, or laying out a full page view. Use when user mentions "build a page", "create a route", "page component", "page layout", "wire up data loading", or asks to assemble existing components into a full page. Requires Figma MCP server connection if building from a design. plan-components Break down a Figma page design into a structured component plan — identify reusable existing components, classify new ones into tiers, map the component hierarchy, and suggest an implementation order. Use when the user provides a Figma URL and asks to "plan components", "break down the design", "what components do we need", "component audit", or wants to understand the component hierarchy before implementation. Requires Figma Desktop MCP server connection. | Skills | |
equinor/fusion-framework Batch-process Dependabot PRs end-to-end: checkout, rebase, review, changeset, validate, and auto-merge high-confidence PRs. USE FOR: process all Dependabot PRs, clear dependency backlog, batch-merge safe Dependabot updates, batch-process dependency PRs. DO NOT USE FOR: single-PR deep review (use fusion-dependency-review), feature PRs, non-Dependabot dependency PRs, or PRs requiring manual code changes. | Skills | |
joelhooks/joelclaw Build and update person dossiers from communication history. Use when a person is discussed for strategy, follow-up, opportunities, relationship context, or decisions. Automatically pull evidence from Front email, Granola meetings, memory recall, and event logs; then write/update a structured dossier in Vault/Resources/. | Skills | |
jeremylongshore/claude-code-plugins-plus-skills Generate trading signals using technical indicators (RSI, MACD, Bollinger Bands, etc.). Combines multiple indicators into composite signals with confidence scores. Use when analyzing assets for trading opportunities or checking technical indicators. Trigger with phrases like "get trading signals", "check indicators", "analyze for entry", "scan for opportunities", "generate buy/sell signals", or "technical analysis". | Skills |
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