AI Agent Skills Marketplace
Showing 24 of 24 results
ai-productivity
The AI Productivity skill acts as a high-performance intake layer for your agentic workflows. It identifies and resolves common execution bottlenecks before they waste tokens or time: vague prompts, overwhelming context "bloat," and high-risk ambiguous requests. Instead of diving blindly into a task, this skill triages the work, compresses relevant data, or rewrites instructions into actionable prompts.
truth-first-lite
The Truth-First Verification skill enforces a rigorous, evidence-based workflow for AI agents. It prevents "hallucinated certainty" by requiring real-time verification of system states, configurations, and file contents before the agent is allowed to provide an answer. Instead of assuming a service is running or a config is correct, the agent must prove it using available diagnostic tools.
Notice Trap Lite
Notice Trap Lite is a free renewal-risk triage for SaaS stacks. Paste a vendor list (and optional renewal window) and get a short memo with honest LEAD vs opportunity labels. It does not write Google Sheets, pull Ramp, or draft vendor emails. Use when the user has SaaS tools renewing in ≤90 days, wants a one-page renewal risk read before a full vendor-ops pack, or asks an agent to triage renewals / unused seats / renegotiation candidates from a pasted list. One-question intake (spend source? window days?). Next: Notice Trap Pack ($24) — inventory, scoring, approval-ready counters: https://agentpowers.ai/skills/notice-trap-pack
CRA Mileage Log Skill for AI Agents
Help your AI assistant keep a CRA-style mileage log for a Canadian vehicle: date, purpose, destination, start and end odometer, and kilometres for each trip, with totals, business-use % and per-km allowance figures from the Tallyfern Rates MCP, each with its source and as-of date. Not tax advice.
Agent Skill Sampler - GitHub PR Review + Gemini Image Generation
Two free agent skills in one: read-only GitHub (PRs awaiting your review, CI status, issues and notifications through your existing gh CLI login, no keys) and nano-banana-pro image generation and editing with Google Gemini 3 Pro Image. Works in Claude Code, Cursor and any agent that loads SKILL.md skills.
Orchestrator Verify Agent Deliverables
Scorecard skill to fact-check Hermes/Agy/Grok outputs before shipping or claiming money moved — blocks confabulation and fake payouts.
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) · [docs](https://docs.runcomfy.com/cli/introduction) · [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 — 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
momentic-mobile-test
# Momentic model Momentic Mobile turns structured natural language into native and webview automation on Android emulators and iOS simulators. Interactive steps resolve targets into device actions; assertions can inspect screenshots, native hierarchies, and webview state. # Project files and formats Mobile tests use `*.test.yaml`; 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: m
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
flutter-add-widget-test
# Writing Flutter Widget Tests ## Contents - [Setup & Configuration](#setup--configuration) - [Core Components](#core-components) - [Workflow: Implementing a Widget Test](#workflow-implementing-a-widget-test) - [Interaction & State Management](#interaction--state-management) - [Examples](#examples) ## Setup & Configuration Ensure the testing environment is properly configured before authoring widget tests. 1. Add the `flutter_test` dependency to the `dev_dependencies` section of `pubspec.yaml`. 2. Place all
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.
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
skill development
# Skill Development for Claude Code Plugins This skill provides guidance for creating effective skills for Claude Code plugins. ## About Skills Skills are modular, self-contained packages that extend Claude's capabilities by providing specialized knowledge, workflows, and tools. Think of them as "onboarding guides" for specific domains or tasks—they transform Claude from a general-purpose agent into a specialized agent equipped with procedural knowledge that no model can fully possess. ### What Skills Provi
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 `
ad-creative-test-plan
# Creative Test Plan You are a paid-media experimentation lead. Design the creative test _before_ launch, then emit a plan document a media buyer can execute without you: - Isolate the variable deliberately. - Structure the cells. - Size the spend and sample. - Pre-register the success criteria and decision rules. The core discipline is honesty about power: most creative "A/B tests" at normal budgets cannot reach statistical significance, and the plan must say so explicitly rather than let a screening heuri
data-storytelling
# Data Storytelling Transform raw data into compelling narratives that drive decisions and inspire action. ## When to Use This Skill - Presenting analytics to executives - Creating quarterly business reviews - Building investor presentations - Writing data-driven reports - Communicating insights to non-technical audiences - Making recommendations based on data ## Core Concepts ### 1. Story Structure ``` Setup → Conflict → Resolution Setup: Context and baseline Conflict: The problem or opportunity Resolution
python-code-style
# Python Code Style & Documentation Consistent code style and clear documentation make codebases maintainable and collaborative. This skill covers modern Python tooling, naming conventions, and documentation standards. ## When to Use This Skill - Setting up linting and formatting for a new project - Writing or reviewing docstrings - Establishing team coding standards - Configuring ruff, mypy, or pyright - Reviewing code for style consistency - Creating project documentation ## Core Concepts ### 1. Automated
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
dart-add-unit-test
# Testing Dart and Flutter Applications ## Contents - [Structuring Test Files](#structuring-test-files) - [Writing Tests](#writing-tests) - [Executing Tests](#executing-tests) - [Test Implementation Workflow](#test-implementation-workflow) - [Examples](#examples) ## Structuring Test Files Organize test files to mirror the `lib` directory structure to maintain predictability. * Place all test code within the `test` directory at the root of the package. * Append `_test.dart` to the end of all test file names
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