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kling-3-0
# Kling 3.0 - Pro Pack on RunComfy [runcomfy.com](https://www.runcomfy.com/?utm_source=skills.sh&utm_medium=skill&utm_campaign=kling-3-0) Β· [docs](https://docs.runcomfy.com/cli/introduction) Β· [GitHub](https://github.com/agentspace-so/runcomfy-agent-skills/tree/main/kling-3-0) [Kling 3.0](https://www.runcomfy.com/models/kling/kling-3.0) is Kuaishou Technology's third-generation cinematic video model. This skill covers all six Kling 3.0 rendering endpoints on RunComfy: three quality tiers (Standard, Pro, 4K)
momentic-test
# Momentic model Momentic turns structured natural language into browser automation using forked Playwright, CDP, and custom agents. Interactive steps resolve natural-language targets, assertions can use multimodal models, and AI actions can complete broader goals. # Project files and formats Tests use `*.test.yaml`; reusable modules use `*.module.yaml`. Test IDs live in the test file's `id` field. Check `momentic.config.yaml` and test-level metadata before assuming project defaults apply. `fileType: moment
context-engineering
# Context Engineering ## Overview Feed agents the right information at the right time. Context is the single biggest lever for agent output quality β too little and the agent hallucinates, too much and it loses focus. Context engineering is the practice of deliberately curating what the agent sees, when it sees it, and how it's structured. ## When to Use - Starting a new coding session - Agent output quality is declining (wrong patterns, hallucinated APIs, ignoring conventions) - Switching between different
agent-pulse
# Agent Pulse ## Purpose Use the installed `agent-pulse` CLI as the source of truth for local AI-agent activity. The PyPI package is `agentpulse-cli`, while the command remains `agent-pulse`. Prefer running commands and summarizing their output over reading the Agent Pulse source code. Always enable UTF-8 on Windows before running commands because Agent Pulse output contains emoji and box drawing: ```powershell $env:PYTHONUTF8='1' $env:PYTHONIOENCODING='utf-8' ``` If `agent-pulse` is not on PATH, ask before
golang-code-style
**Orchestration mode:** Fan out the sub-agents described in the "Parallelizing Code Style Reviews" section, each covering an independent style concern, when reviewing code style across a large codebase, and merge their findings. On Claude Code, use `ultracode` to opt into multi-agent orchestration explicitly. > **Community default.** A company skill that explicitly supersedes `samber/cc-skills-golang@golang-code-style` skill takes precedence. # Go Code Style Style rules that require human judgment β linters
convex-test
<!-- GENERATED from convex-agents content/capabilities/test.json β do not edit by hand. --> # Generate Convex tests Use convex-test + vitest to test functions against an in-memory backend: args/returns, auth paths, indexes, and scheduled functions. ## Workflow 1. Install convex-test + vitest. 2. Write tests using convexTest(schema): seed via t.run, call t.query/t.mutation, assert. 3. Cover auth (withIdentity), error paths, and scheduled functions (t.finishInProgressScheduledFunctions). 4. Run vitest; keep t
convex-agent
<!-- GENERATED from convex-agents content/capabilities/agent.json β do not edit by hand. --> # Add an AI agent / RAG backend Install @convex-dev/agent for durable threads, message history, tool-calls, and vector search/RAG β the backend for an in-app AI agent. Call models through the Convex AI Gateway by default: Convex holds the provider credentials, so there is no LLM key to obtain, store, or rotate. ## Workflow 1. Install @convex-dev/agent + @convex-dev/ai-sdk-provider; add the agent component to convex.
agent development
# Agent Development for Claude Code Plugins ## Overview Agents are autonomous subprocesses that handle complex, multi-step tasks independently. Understanding agent structure, triggering conditions, and system prompt design enables creating powerful autonomous capabilities. **Key concepts:** - Agents are FOR autonomous work, commands are FOR user-initiated actions - Markdown file format with YAML frontmatter - Triggering via description field with examples - System prompt defines agent behavior - Model and c
agent-device
# agent-device For a normal app-driving task, start immediately. Do not probe first with `--help`, `--version`, `devices`, `appstate`, `snapshot`, or `screenshot`: ```bash agent-device open <app> --foreground ``` That starts the session and returns the initial interactive snapshot with `@refs`. Loop: act with `press|click|fill|longpress <target> ... --settle`, `scroll <direction> --settle`, or `back --settle`; continue from the printed diff, verify the named expectation (`wait text "..."`, `is`, `get`, or `
huawei-cloud-openviking-agent-integration
# Huawei Cloud Agent Integration (OpenViking Long-Term Memory) Integrate / unbind OpenViking long-term memory for coding agents running in bwrap sandboxes (`/root/job-envs/sandboxes/`). Every agent uses its **native mechanism** (MCP, HTTP memory provider, TS extension hooks, settings.json), so integration survives agent upgrades. Writes are **template-level persistent**: config is injected into `/root/template/<agent>/start.sh`, so sandbox `stop + start` preserves the integration. ## Supported Agents | Agen
agent-email-inbox
# AI Agent Email Inbox ## Overview This skill covers setting up a secure email inbox that allows your application or AI agent to receive and respond to emails, with content safety measures in place. **Core principle:** An AI agent's inbox receives untrusted input. Security configuration is important to handle this safely. The skill builds the agent on a Resend **Inbox**. An inbox groups email into threads, and your agent replies inside the thread with one call. Resend sets the recipients and the threading h
Skill Vetter
Security-first skill vetting for AI agents. Use before installing any skill from ClawdHub, GitHub, or other sources. Checks for red flags, permission scope, and suspicious patterns.
scoutqa-test
# ScoutQA Testing Skill Perform AI-powered exploratory testing on web applications using the `scoutqa` CLI. **Think of ScoutQA as an intelligent testing partner** that can autonomously explore, discover issues, and verify features. Delegate testing to multiple parallel ScoutQA executions to maximize coverage while saving time. ## When to Use This Skill Use this skill in two scenarios: 1. **User requests testing** - When the user explicitly asks to test a website or verify functionality 2. **Proactive verifi
agent-governance
# Agent Governance Patterns Patterns for adding safety, trust, and policy enforcement to AI agent systems. ## Overview Governance patterns ensure AI agents operate within defined boundaries β controlling which tools they can call, what content they can process, how much they can do, and maintaining accountability through audit trails. ``` User Request β Intent Classification β Policy Check β Tool Execution β Audit Log β β β Threat Detection Allow/Deny Trust Update ``` ## When to Use - **Agents with tool acc
agent-platform-skill-registry
# Skill Registry This skill provides instructions for interacting with the **Skill Registry** on the Gemini Enterprise Agent Platform. ## Core Capabilities - **Skill Discovery** - Query the registry to easily search, list, get specific skills, and inspect revision histories. - **Skill Lifecycle Management** - Upload, update, or permanently delete skills. - **Operation Monitoring** - Utility to check the completion status of long-running state changes (LROs). - **Generate Skill** - Automate the initial scaff
emblem-portfolio-tracker
# Emblem Portfolio Tracker Cross-chain crypto portfolio monitoring powered by **EmblemAI**. Aggregated balances with USD values across Solana, Ethereum, Base, BSC, Polygon, Hedera, and Bitcoin. Conditional trade P&L tracking and DeFi position viewing via Nansen. **Requires**: `npm install -g @emblemvault/agentwallet` --- ## What This Skill Can Do | Capability | Tools Used | |-----------|------------| | Wallet addresses (all chains) | `wallet` | | Solana balances + USD values | `solanaBalances` | | Ethereum
emblem-market-research
# Emblem Market Research Crypto market intelligence powered by **EmblemAI**. Real-time data from CoinGecko, CoinGlass, Birdeye, and Nansen β trending tokens, derivatives analytics, on-chain smart money tracking, and token deep dives. **Requires**: `npm install -g @emblemvault/agentwallet` --- ## What This Skill Can Do | Capability | Tools Used | |-----------|------------| | Trending tokens (all chains) | `getTrendingCoins`, `birdeyeTrendingTokens` | | Token price lookup | `getCryptoPrice`, `searchCryptoByNa
agent-platform-deploy
# Agent Platform Model Garden Deploy Skill This skill provides instructions for deploying Open Models from Agent Platform Model Garden to endpoints, and subsequently undeploying them to clean up resources. ## 1P Tuned Model Copy & Deployment If you need to copy a **1P (First-Party) Tuned Model** from a source project to a destination region or project and deploy it to a newly created endpoint, refer to the [1P Tuned Model Copy & Deployment Guide](references/copy_deploy_guide.md). ## Safety & Confirmation Ti
emblem-defi-yield
# Emblem DeFi Yield DeFi yield research and liquid staking powered by **EmblemAI**. Research yield opportunities across protocols, review existing DeFi positions via Nansen, and enter liquid staking positions through token swaps on Solana, Ethereum, Base, BSC, Polygon, and Hedera. **Requires**: `npm install -g @emblemvault/agentwallet` --- ## What This Skill Can Do | Capability | How | Tools Used | |-----------|-----|------------| | Research yield opportunities | Ask about yields, APYs, protocols | LLM know
agent-platform-tuning
# Agent Platform Model Tuning ## Overview This skill provides procedural knowledge for fine-tuning Large Language Models (both Open Models and Gemini Models) using Agent Platform's tuning service. It covers the entire lifecycle from environment setup and data preparation to job configuration, monitoring, and deployment. ## Workflow Decision Tree 1. **Project & Region Verification Check**: Has the user provided the Google Cloud project and region? - **No** β **STOP tool execution immediately**. Do NOT run ve
agent-platform-prompt-management
## Usage Guide To use this skill effectively: 1. **Execute Operations via Python**: Run the Python snippets below using `run_command` in the execution environment to manage prompts in Agent Platform on behalf of the user. Do not delegate execution to the user or claim lack of access once approved. 2. **No File System Search**: Do not try to find Python files or scripts on the file system for these operations. ## Safety & Confirmation Tiers (CRITICAL) Before executing any commands or scripts on behalf of the
agent-platform-tuning-management
# Agent Platform Tuning Management This skill provides instructions on how to manage GenAI Tuning Jobs using the Agent Platform Python SDK. Use this skill when a user wants to check the status of their tuning runs, find an active tuning job, or cancel a job that is running too long. ## Safety & Confirmation Tiers (CRITICAL) Before executing any commands on behalf of the user, you MUST adhere to the following safety tiers based on the action requested: 1. **Tier R: Read-only (`list`, `get`)** * **Rule**: No
agent-platform-eval-flywheel
# Agent Platform Eval Flywheel Skill Help users evaluate and iteratively improve GenAI models and agents using the Agent Platform GenAI Evaluation SDK (`google.genai` / `agentplatform`). ## When to use this skill - Evaluating GenAI agents or models with the Agent Platform GenAI Evaluation SDK (`client.evals.evaluate()`). - Creating evaluation datasets from session traces, pandas DataFrames, or synthetic generation. - Selecting, configuring, or writing custom evaluation metrics. - Analyzing rubric verdicts,
agent-tools
> **Install the belt CLI skill:** `npx skills add belt-sh/cli` # [inference.sh](https://inference.sh) Run AI apps in the cloud with a simple CLI. No GPU required. ](https://cloud.inference.sh/app/files/u/4mg21r6ta37mpaz6ktzwtt8krr/01kgjw8atdxgkrsr8a2t5peq7b.jpeg) ## Install CLI ```bash curl -fsSL https://cli.inference.sh | sh belt login ``` > **What does the installer do?** The [install script](https://cli.inference.sh) detects your OS and architecture, downloads the co