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Found 7,557 Skills
This skill should be used when the user asks about "snow-flow commands", "CLI commands", "how to start", "swarm", "sparc", "orchestrator", "agent spawn", "memory", "task", or needs guidance on Snow-Flow CLI operations.
Deep web research with parallel investigators, multi-wave exploration, and structured synthesis. Spawns multiple web-researcher agents to explore different facets of a topic simultaneously, launches additional waves when gaps are identified, then synthesizes findings. Use when asked to research, investigate, compare options, find best practices, or gather comprehensive information from the web.\n\nThoroughness: quick for factual lookups | medium for focused topics | thorough for comparisons/evaluations (waves continue while critical gaps remain) | very-thorough for comprehensive research (waves continue until satisficed). Auto-selects if not specified.
Use when dispatching code reviews for tiers with N greater than 1 (max-20x, max-5x) in subagent-driven development, or manually for critical changes over 200 lines or security-sensitive code
Debug problems by investigating multiple hypotheses in parallel. Use when you have a bug, unexpected behaviour, or mystery where the root cause is unclear. Spawns parallel investigator agents each pursuing a different theory, then compares evidence to identify the most likely cause and fix.
Consult an advisory council of three AI personas — Cato (skeptic), Ada (optimist), Marcus (pragmatist) — backed by different frontier LLM agents (Gemini, Claude, Codex). Each persona runs as a separate agent process with full repo context and returns independent feedback. Use when the user says "/council", asks for a second opinion, wants feedback on code changes, needs a premortem, wants to pressure-test a decision, or asks "what do you think about this approach?" Claude may also proactively suggest consulting the council before major architectural decisions, risky deploys, or ambiguous trade-offs (but should ask for user approval first).
Build single-agent and multi-agent systems using Google's Agent Development Kit (ADK) in Python, Java, Go, or TypeScript. Use when creating AI agents with ADK, designing multi-agent architectures, implementing agent tools, configuring agent callbacks, managing agent state, orchestrating sequential/parallel/loop agent workflows, or when the user mentions ADK, google-adk, google agent development kit, agentic AI with Gemini, or agent orchestration with Google tools. Also use when setting up ADK projects, writing agent tests, deploying agents, or integrating MCP tools with ADK.
Analyze recent conversation context and capture learnings to project knowledge files (for project-specific insights) or skills/commands/subagents (for cross-project patterns). Use when the user asks to "capture this learning", "update the docs with this", "remember this for next time", "document this issue", "add this to CLAUDE.md", "save this knowledge", or "update project knowledge". Also triggers after resolving build/setup issues, discovering non-obvious patterns, or completing debugging sessions with valuable insights.
Deploy and manage live trading agents on Hyperliquid. ⚠️ HIGH RISK - REAL CAPITAL AT STAKE ⚠️ Provides deployment_create (launch agent, $0.50), deployment_list (monitor), deployment_start/stop (control), and account tools (credit management). Supports EOA (1 deployment max) and Hyperliquid Vault (200+ USDC required, unlimited deployments). CRITICAL: NEVER deploy without thorough backtesting (6+ months, Sharpe >1.0, drawdown <20%). Start small, monitor daily, define exit criteria before deploying.
Gives the agent the ability to send, receive, search, and manage emails directly from the terminal or via a local HTTP API. Use this skill when the agent needs to handle email tasks: sending messages, reading inbox, replying, forwarding, managing contacts, organizing with tags/folders/filters, scheduling background sync, setting up webhooks for new email events, or automating email workflows. Supports structured output (--format json/markdown/html), field selection (--fields), standardized exit codes, and a local REST API with OpenAPI docs. Works with IMAP/SMTP providers including Gmail, Outlook, QQ Mail, and others. Activates on keywords: send email, check inbox, reply, forward, email automation, contacts, email template, notifications, webhook, http api, format json, field selection, serve, openapi.
Three-phase design review. Chain architect → refiner → critique subagents. Triggers on: 'design review', 'architecture review', '/arc', system design proposals, significant refactoring decisions, new service or module design.
Create narrative lore entries that transform technical work into mythological stories. Use when generating agent memory, documenting changes as narrative, or building persistent knowledge through storytelling.
Multi-Model Collaboration — Invoke gemini-agent and codex-agent for auxiliary analysis **Trigger Scenarios** (Proactive Use): - In-depth code analysis: algorithm understanding, performance bottleneck identification, architecture sorting - Large-scale exploration: 5+ files, module dependency tracking, call chain tracing - Complex reasoning: solution evaluation, logic verification, concurrent security analysis - Multi-perspective decision-making: requiring analysis from different angles before comprehensive judgment **Non-Trigger Scenarios**: - Simple modifications (clear changes in 1-2 files) - File searching (use Explore or Glob/Grep) - Read/write operations on known paths **Core Principle**: You are the decision-maker and executor, while external models are consultants.