Agentfolio

AgentFolio

Role: Autonomous Agent Discovery Guide

Use this skill when you want to discover, compare, and research autonomous AI agents across ecosystems. AgentFolio is a curated directory at https://agentfolio.io that tracks agent frameworks, products, and tools.

This skill helps you:

• Find existing agents before building your own from scratch.
• Map the landscape of agent frameworks and hosted products.
• Collect concrete examples and benchmarks for agent capabilities.

Capabilities

• Discover autonomous AI agents, frameworks, and tools by use case.
• Compare agents by capabilities, target users, and integration surfaces.
• Identify gaps in the market or inspiration for new skills/workflows.
• Gather example agent behavior and UX patterns for your own designs.
• Track emerging trends in agent architectures and deployments.

How to Use AgentFolio

1. Open the directory

   • Visit https://agentfolio.io in your browser.
   • Optionally filter by category (e.g., Dev Tools, Ops, Marketing, Productivity).

2. Search by intent

   • Start from the problem you want to solve:
     • “customer support agents”
     • “autonomous coding agents”
     • “research / analysis agents”
   • Use keywords in the AgentFolio search bar that match your domain or workflow.

3. Evaluate candidates

   • For each interesting agent, capture:
     • Core promise (what outcome it automates).
     • Input / output shape (APIs, UI, data sources).
     • Autonomy model (one-shot, multi-step, tool-using, human-in-the-loop).
     • Deployment model (SaaS, self-hosted, browser, IDE, etc.).

4. Synthesize insights

   • Use findings to:
     • Decide whether to integrate an existing agent vs. build your own.
     • Borrow successful UX and safety patterns.
     • Position your own agent skills and workflows relative to the ecosystem.

Example Workflows

1) Landscape scan before building a new agent

• Define the problem: “autonomous test failure triage for CI pipelines”.
• Use AgentFolio to search for:
  • “testing agent”, “CI agent”, “DevOps assistant”, “incident triage”.
• For each relevant agent:
  • Note supported platforms (GitHub, GitLab, Jenkins, etc.).
  • Capture how they explain autonomy and safety boundaries.
  • Record pricing/licensing constraints if you plan to adopt instead of build.

2) Competitive and inspiration research for a new skill

• If you plan to add a new skill (e.g., observability agent, security agent):
  • Use AgentFolio to find similar agents and features.
  • Extract 3–5 concrete patterns you want to emulate or avoid.
  • Translate those patterns into clear requirements for your own skill.

3) Vendor shortlisting

• When choosing between multiple agent vendors:
  • Use AgentFolio entries as a neutral directory.
  • Build a comparison table (columns: capabilities, integrations, pricing, trust & security).
  • Use that table to drive a more formal evaluation or proof-of-concept.

Example Prompts

Use these prompts when working with this skill in an AI coding agent:

• “Use AgentFolio to find 3 autonomous AI agents focused on code review. For each, summarize the core value prop, supported languages, and how they integrate into developer workflows.”
• “Scan AgentFolio for agents that help with customer support triage. List the top options, their target customer size (SMB vs. enterprise), and any notable UX patterns.”
• “Before we build our own research assistant, use AgentFolio to map existing research / analysis agents and highlight gaps we could fill.”

When to Use

This skill is applicable when you need to discover or compare autonomous AI agents instead of building in a vacuum:

• At the start of a new agent or workflow project.
• When evaluating vendors or tools to integrate.
• When you want inspiration or best practices from existing agent products.

AGI Framework Integration

Adapted for @techwavedev/agi-agent-kit Original source: antigravity-awesome-skills

Memory-First Protocol

Retrieve prior agent configurations, team compositions, and orchestration patterns. Critical for multi-agent system consistency.

# Check for prior AI agent orchestration context before starting
python3 execution/memory_manager.py auto --query "agent patterns and orchestration strategies for Agentfolio"

Storing Results

After completing work, store AI agent orchestration decisions for future sessions:

python3 execution/memory_manager.py store \
  --content "Agent pattern: hierarchical orchestration with Control Tower dispatcher, 3 specialist sub-agents" \
  --type decision --project <project> \
  --tags agentfolio ai-agents

Multi-Agent Collaboration

This skill is inherently multi-agent. Use cross-agent context to coordinate task distribution and avoid duplicate work.

python3 execution/cross_agent_context.py store \
  --agent "<your-agent>" \
  --action "Agent architecture designed — Control Tower + specialist agents with shared Qdrant memory" \
  --project <project>

Control Tower Integration

Register agents and tasks with the Control Tower (execution/control_tower.py) for centralized orchestration across machines and LLM providers.

Blockchain Identity

Each agent has a cryptographic Ed25519 identity. All memory writes are signed — enabling trust verification in multi-agent systems.