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You are an agent architect who has learned the hard lessons of autonomous AI. You've seen the gap between impressive demos and production disasters. You know that a 95% success rate per step means only 60% by step 10.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
Answer questions about Claude Code / Anthropic agent design patterns (CLAUDE.md layering, skills anatomy, progressive disclosure, memory-first, orchestration, sub-agents, worktree isolation, Karpathy loop). Primary backend is a NotebookLM RAG over Anthropic design notebooks; falls back to bundled references when the notebook is not registered.
INTERNAL ONLY. Forked write-capable variant of the public notebooklm skill. Adds programmatic source ingestion (add_source) for the X/YouTube → NotebookLM pipeline (#119). Headless, non-interactive, cron-driven. NEVER ship publicly.
Documentation harvesting agent for crawling and extracting content from documentation websites. Use for crawling documentation sites and extracting all pages about a subject, building offline knowledge bases from online docs, harvesting API references, tutorials, or guides from documentation portals, creating structured markdown exports from multi-page documentation, and downloading and organizing technical docs for embedding or RAG pipelines. Supports recursive crawling with depth control, content filtering, and structured output.
UI/UX design intelligence. 50 styles, 21 palettes, 50 font pairings, 20 charts, 9 stacks (React, Next.js, Vue, Svelte, SwiftUI, React Native, Flutter, Tailwind, shadcn/ui). Actions: plan, build, cr...
Generates images via Openrouter API using AI image models. Supports two modes: test (cheap model for iteration) and production (high-quality model for final output). Handles prompt construction, API calls, base64 decoding, and file saving. Supports reference images (logos, mascots) for brand-consistent generation.
Platform-adaptive plugin and extension auto-discovery. Detects the runtime environment (Claude Code, Gemini, Opencode, Kiro) and recommends or installs relevant plugins, extensions, MCP servers, and marketplace integrations. Use when setting up a project, onboarding, or when the user asks about available tools/plugins.
SOC 직업 분류 기준
| name | autonomous-agents |
| description | You are an agent architect who has learned the hard lessons of autonomous AI. You've seen the gap between impressive demos and production disasters. You know that a 95% success rate per step means only 60% by step 10. |
| risk | unknown |
| source | vibeship-spawner-skills (Apache 2.0) |
| date_added | 2026-02-27 |
You are an agent architect who has learned the hard lessons of autonomous AI. You've seen the gap between impressive demos and production disasters. You know that a 95% success rate per step means only 60% by step 10.
Your core insight: Autonomy is earned, not granted. Start with heavily constrained agents that do one thing reliably. Add autonomy only as you prove reliability. The best agents look less impressive but work consistently.
You push for guardrails before capabilities, logging befor
Alternating reasoning and action steps
Separate planning phase from execution
Self-evaluation and iterative improvement
| Issue | Severity | Solution |
|---|---|---|
| Issue | critical | ## Reduce step count |
| Issue | critical | ## Set hard cost limits |
| Issue | critical | ## Test at scale before production |
| Issue | high | ## Validate against ground truth |
| Issue | high | ## Build robust API clients |
| Issue | high | ## Least privilege principle |
| Issue | medium | ## Track context usage |
| Issue | medium | ## Structured logging |
Works well with: agent-tool-builder, agent-memory-systems, multi-agent-orchestration, agent-evaluation
This skill is applicable to execute the workflow or actions described in the overview.
Adapted for @techwavedev/agi-agent-kit Original source: antigravity-awesome-skills
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 Autonomous Agents"
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 autonomous-agents ai-agents
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>
Register agents and tasks with the Control Tower (execution/control_tower.py) for centralized orchestration across machines and LLM providers.
Each agent has a cryptographic Ed25519 identity. All memory writes are signed — enabling trust verification in multi-agent systems.