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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 โ linters
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.
literature-review
# Literature Review Conduct deep literature reviews through multi-perspective dialogue and systematic search. ## Input - `$0` โ Research topic or question - `$1` โ Optional: specific focus or angle ## References - Multi-perspective dialogue prompts (STORM): `~/.claude/skills/literature-review/references/dialogue-prompts.md` - Literature review workflow (AgentLaboratory): `~/.claude/skills/literature-review/references/review-workflow.md` ## Scripts (from literature-search skill) ```bash # Search Semantic Sch
agent-platform-troubleshooting
# Agent Platform Troubleshooting > [!IMPORTANT] **CRITICAL RULE**: You MUST ONLY use the reference files located > in this skill's `references/` directory (e.g., `references/field-manual.md`, > `references/known-issues.md`, `references/agent-registry.md`). Do NOT search > for or read other external playbooks or files outside this directory. The > files in the local `references/` directory contain workspace-specific fixes > and are the sole source of truth for this troubleshooting session. Diagnose issues ac
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.
code-testing-agent
# Code Testing Generation Skill An AI-powered skill that generates comprehensive, workable unit tests for any programming language using a coordinated multi-agent pipeline. ## Non-negotiable execution contract Classify scope **before editing**: - **Broad** (a project/package-wide suite, or multiple production files/modules): create `research.md` and `plan.md` in a resolved non-stageable `<TESTAGENT_DIR>` before implementation, then `status.md` there after the final test-quality review. When `code-testing-ge
agentx
# ๐ AgentX The Starchild community forum. **Script skill** โ call the functions in `core.skill_tools.agentx` from bash and read the returned JSON. Auth is automatic (container JWT); no API key needed. Read this file **before** the first call to get function signatures and posting rules. ## How to call ```bash python3 -c "from core.skill_tools import agentx; import json; print(json.dumps(agentx.list_posts(sort='hot')))" ``` Every function returns a dict: `{"success": true, ...}` or `{"success": false, "erro
agent-fs
# agent-fs CLI agent-fs is an agent-first filesystem with full versioning, full-text search (FTS5), and semantic search. It provides a CLI that outputs JSON, making it ideal for agent workflows. Files are organized in drives within orgs. ## Storage Backends agent-fs stores file bytes in a pluggable storage backend. The durable value โ version history, comments, and search โ lives in SQLite and works identically on every backend. | Backend | Setup | Versioning tier | `signed-url` | |---------|-------|-------
agent-hooks
# Agent Hooks Shell hooks let a user run **their own script** at fixed points in the agent's lifecycle โ to **block** a dangerous action, **rewrite** an input or an outbound message, **inject context** into the model, or **warn the user**. The script can be written in any language; it talks to the agent over a simple JSON-on-stdin, JSON-on-stdout protocol. Tools: `read_file`, `write_file`, `bash` ## When to use Reach for hooks when the user wants the agent to **automatically enforce a rule or react to an ev
agent-browser-automation
# agent-browser > Skill by [ara.so](https://ara.so) โ Daily 2026 Skills collection. `agent-browser` is a headless browser automation CLI built in Rust, designed for AI agents. It wraps Chrome via the Chrome DevTools Protocol (CDP) and exposes a fast, ergonomic command-line interface for navigation, interaction, accessibility snapshots, screenshots, network interception, and more โ with no Node.js or Playwright runtime required. ## Installation ### Recommended (npm global) ```bash npm install -g agent-browse
data-engineering-medallion-pipeline
# Data Engineering Medallion Pipeline Skill > Skill by [ara.so](https://ara.so) โ Data Skills collection. This skill enables AI agents to work with a complete data engineering pipeline implementing the Medallion Architecture (Bronze โ Silver โ Gold) using modern open-source tools: MinIO (S3-compatible storage), Airbyte (data ingestion), PostgreSQL (data warehouse), DBT (transformations), Apache Airflow (orchestration), and Grafana (monitoring). ## What This Project Does The data-engineering-medallion projec
agents-build
# build Add capabilities to your AgentCore agent project. ## When to use - Adding cross-session memory to your agent - Calling your deployed agent from a web app, mobile app, or backend service - Configuring VPC networking for private resources (RDS, internal APIs) - Building multi-agent systems with orchestrator/specialist patterns - Migrating an existing Bedrock Agent to AgentCore - Adding the Browser tool so the agent can navigate websites - Adding the Code Interpreter so the agent can execute code in a
agents-deploy
# deploy Deploy your AgentCore agent to AWS, or diagnose why a deploy failed. ## When to use - You're ready to deploy and want to validate config first - `agentcore deploy` failed with an error - You want to preview what deploy will create without actually deploying - You want to deploy to a specific target (staging, production) - You need to roll back to a previous version, pin to a specific version, or set up canary deployments ## Input `$ARGUMENTS` is optional: ``` /agents-deploy # interactive โ pre-flig
agents-debug
# debug Diagnose why your AgentCore agent or environment isn't working correctly. ## When to use - Your agent is returning wrong answers or errors - Tool calls are failing or timing out - Agent works locally but fails after deploying - Logs aren't showing up in CloudWatch - The AgentCore CLI isn't working or environment seems broken - `agentcore` command not found or prerequisites are missing Do NOT use for: - Deploy failures (CDK errors, IAM during deploy) โ use `agents-deploy` - Scaffolding a new project
agents-connect
# connect Give your AgentCore agent access to external APIs, tools, and services via the AgentCore Gateway โ and control what it can access with Cedar policies. ## When to use - You want your agent to call an external API or MCP server - You want to expose Lambda functions as agent tools - You have an OpenAPI spec you want to turn into agent tools - Your agent needs credentials to call an external service - You want to restrict which tools your agent can call (Cedar policies) - You want role-based or amount
agents-optimize
# optimize Measure and improve your AgentCore agent's quality through evaluation, monitoring, and observability. ## When to use - You want to know if your agent is giving good answers - You want to set up continuous quality monitoring in production - You want to add a quality gate to your CI/CD pipeline - You want to understand agent behavior through logs, metrics, and traces - You want to set up CloudWatch dashboards or X-Ray tracing Do NOT use for: - Debugging a specific broken agent (wrong answers, error
agents-get-started
# get-started Walk a developer from zero to a running agent on AWS. ## When to use - Developer wants to build an agent on AWS and doesn't know where to start - Developer wants to create a new AgentCore project - Developer is choosing between frameworks (Strands, LangGraph, GoogleADK, OpenAI Agents) - Developer just ran `agentcore create` and wants to know what to do next Do NOT use for: - Environment/prerequisite issues (CLI not found, credentials broken) โ use `agents-debug` - Adding capabilities to an exi
agents-harden
# harden Prepare your AgentCore agent for production โ security, reliability, and performance. ## When to use - You're about to take an agent to production - You want a checklist of what to review before launch - You want to restrict who can call your agent - You want to scope down IAM permissions from the defaults - You're hitting throttling or quota errors (loads [`references/limits.md`](references/limits.md)) - You need to tune session lifecycle for your workload - You're running long-running background
everything-claude-code-harness
# Everything Claude Code (ECC) โ Agent Harness Performance System > Skill by [ara.so](https://ara.so) โ Daily 2026 Skills collection. Everything Claude Code (ECC) is a production-ready performance optimization system for AI agent harnesses. It provides specialized subagents, reusable skills, custom slash commands, memory-persisting hooks, security scanning, and language-specific rules โ all evolved from 10+ months of daily real-world use. Works across Claude Code, Cursor, Codex, OpenCode, and Antigravity. -
agent-graphs
# Config Agent Graphs You're using a skill that will guide you through creating and managing agent graphs in LaunchDarkly. Your job is to design the graph topology, create it with the right edges and handoffs, and verify the routing between config nodes. ## Prerequisites This skill requires the remotely hosted LaunchDarkly MCP server to be configured in your environment. **Required MCP tools:** - `create-agent-graph` -- create a new graph with nodes and edges - `get-agent-graph` -- inspect a graph's structu