AI Agents Marketplace
Showing 24 of 24 results
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 — 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 — linters
convex-test
<!-- GENERATED from convex-agents content/capabilities/test.json — 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 — 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 — 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-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-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 — 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
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-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-inference
# Agent Platform GenAI Inference Skill This skill provides instructions for authenticating and connecting to Google Cloud Agent Platform to use Generative AI models. It covers: * **First-Party publisher models** (Gemini) — section 2. * **Third-Party publisher models** (OpenMaaS: Llama, DeepSeek, Qwen, etc.) — section 3. * **Custom endpoints** (any model on a numeric `projects/.../endpoints/<id>` resource — tuned Gemini models, OSS LLMs self-deployed from Model Garden via the `agent-platform-deploy` skill, a
agent-platform-endpoint-management
# Agent Platform Endpoint Management ## Overview This skill provides procedural knowledge for managing Agent Platform Endpoints. Endpoints are logical serving hosts that provide a stable URL for online predictions. You must create an endpoint before you can deploy a model to it. ## 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`, `describe`, `get`)** * No co
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-platform-alert-configuration
# Agent Platform Alert Configuration ## Critical Steps ### 1. Safety & Confirmation Tiers (CRITICAL) Before executing any commands or writing configurations on behalf of the user, you MUST adhere to the following safety tiers based on the action requested: 1. **Tier R: Read-only (`check_telemetry.py` / `gather_agent_info.py`)** * **Rule**: No confirmation needed. You may execute these scripts immediately to inspect telemetry status or gather agent configuration details. 2. **Tier B: Billing & Resource Creat
ccc
# ccc - Semantic Code Search & Indexing `ccc` is the CLI for CocoIndex Code, providing semantic search over the current codebase and index management. ## Ownership The agent owns the `ccc` lifecycle for the current project — initialization, indexing, and searching. Do not ask the user to perform these steps; handle them automatically. - **Initialization**: If `ccc search` or `ccc index` fails with an initialization error (e.g., "Not in an initialized project directory"), run `ccc init` from the project root
Tavily Search
Search the public web through Tavily and return structured results with URLs and snippets. Use when an agent needs current information, source discovery, link lookup, news or finance search, or a fallback for unavailable built-in web search. Do not use for private or authenticated pages.
Proactive Agent Lite
Transform AI agents from task-followers into proactive partners with memory architecture, reverse prompting, and self-healing patterns. Lightweight version f...
Planning with files
Persistent file-based planning for multi-step AI-agent work. Keeps task_plan.md, findings.md, and progress.md on disk; lifecycle hooks inject selected project planning context. Automatic recovery reads project planning files only. Explicit session-catchup.py --metadata reads same-project local agent session records and emits aggregate counts only; --replay may emit bounded nonce-framed excerpts. Optional gated mode can request continuation only when the host supports it and never runs commands declared in Markdown. The skill has no network upload path. Use for research or work needing 5+ tool calls.
agent
Define agent identity, personality, voice, and boundaries to create assistants that feel authentic rather than generic.
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