All Skills

Community-powered growth for builders. Boost amplifies your social posts with authentic community engagement (replies, likes, reposts). Discover finds relevant conversations and generates AI-powered replies at scale. Use Boost when the user has a post URL. Use Discover when the user wants to find and engage in conversations about their product.

This skill should be used when the user asks about "my balance", "portfolio", "token holdings", "check balance", "how much do I have", "wallet balance", "what tokens do I own", "show my holdings", or any balance/portfolio query. Provides guidance on checking balances across chains.

This skill should be used when the user asks about "Polymarket", "prediction markets", "betting odds", "place a bet", "check odds", "market predictions", "what are the odds", "bet on election", "sports betting", or any prediction market operation. Provides guidance on searching markets, placing bets, and managing positions.

Give your agent a budget, a target, and a deadline — it does the rest. Orchestrates DSL + Opportunity Scanner + Emerging Movers into a full autonomous trading loop on Hyperliquid. Race condition prevention, conviction collapse cuts, cross-margin buffer math, speed filter. 3 risk profiles: conservative, moderate, aggressive. Use when setting up autonomous trading, creating a trading strategy, or running a scan-evaluate-trade-protect loop.

4-stage funnel that screens all 500+ Hyperliquid perps down to the top trading opportunities. Scores setups 0-400 across smart money, market structure, technicals, and funding. BTC macro filter, hourly trend gate (counter-trend = hard skip), cross-scan momentum tracking. Near-zero LLM tokens — all computation in Python. Use when scanning for new trading opportunities on Hyperliquid, evaluating setups, or checking market conditions.

This skill should be used when the user asks about "agent profile", "create profile", "update profile", "project profile", "bankr.bot/agents", "profile page", "project updates", or any agent profile management operation.

This skill should be used when the user asks to "swap tokens", "exchange ETH for USDC", "buy DEGEN", "sell tokens", "swap on Base", "trade crypto", "convert ETH to WETH", "exchange tokens", "token swap code", "0x routing", or any token swap operation. Also use for questions about ERC20 approvals, allowanceTarget, swap transaction execution, or building swap transactions with the Bankr SDK.

MoonPay PayBox payment vault for AI agents — official MCP at https://api.paybox.sh/mcp, OAuth 2.1, scoped credentials, and Cheshire mcp-server proxy tools (get_paybox_*).

Entry-point skill for Phoenix perpetuals through Vulcan/Rise SDK inside solana-clawd. Use before answering or acting on Vulcan, Phoenix DEX, Solana perps, paper trading, live trading, margin, TP/SL, TWAP, grid, TA strategies, or perps agent setup.

Discord Actions

Generate spectrograms and feature-panel visualizations from audio with the songsee CLI.

Control Eight Sleep pods (status, temperature, alarms, schedules).

Use this skill for any request involving cryptocurrency market data, coin prices, trading volume, market cap, OHLC charts, historical data, exchanges, derivatives, NFTs, DeFi, on-chain token data, liquidity pools, DEX data, or anything powered by CoinGecko or GeckoTerminal APIs. Trigger this skill whenever the user asks about crypto prices, token data, market trends, or wants to call any CoinGecko or GeckoTerminal endpoint — even if they don't explicitly say "CoinGecko". Also trigger when the user asks about API keys, rate limits, or authentication for these APIs.

Use CodexBar CLI local cost usage to summarize per-model usage for Codex or Claude, including the current (most recent) model or a full model breakdown. Trigger when asked for model-level usage/cost data from codexbar, or when you need a scriptable per-model summary from codexbar cost JSON.

Set up MoonPay as an MCP server for Claude Desktop or Claude Code. Provides all MoonPay CLI tools via the Model Context Protocol.

Run Codex CLI, Claude Code, OpenCode, or Pi Coding Agent via background process for programmatic control.

Make paid API requests to x402-protected endpoints. Automatically handles payment with your local wallet.

Search and analyze your own session logs (older/parent conversations) using jq.

Prediction market arbitrage & alpha scout. Searches Polymarket and Kalshi for the same event, runs cross-platform arb math (including fees), and ranks opportunities by profitability. Use when asked to "find arb", "scout markets", "find edge", or scan a specific topic across prediction markets.

Evaluate Google AI Overview presence and click-cannibalization risk for BID-passing Pleasur.ai Stage 01 keyword candidates using current Ahrefs evidence. Use after BID validation and before prioritization, in either presence-only or human-approved deep-completeness mode.

Bind content to live product proof and an accountable author voice.

Produce an answer-first outline that makes the research decision executable.

Iteratively improves a PR (GitHub), MR (GitLab), or shelved changelist (Perforce) until Greptile gives it a 5/5 confidence score with zero unresolved comments. Triggers Greptile review, fixes all actionable comments, pushes/re-shelves, re-triggers review, and repeats. Use when the user wants to fully optimize a PR/MR/CL against Greptile's code review standards.

Route one Paperclip child stage of the PLE new-content routine, producing hash-verifiable evidence and a fail-closed handoff.

Knowledge base from "Oversubscribed: How to Get People Lining Up to Do Business with You" by Daniel Priestley. Use when applying Priestley's frameworks for demand generation, the Campaign Driven Enterprise (CDE) method, signalling, the 7-Hour Rule, products-for-prospects, becoming oversubscribed, or building a Key Person of Influence brand — or when studying or referencing the book.

Layer 1c of the keyword research pipeline — SKIPPED on the current Semrush MCP (it exposes no AI-visibility / AI-toolkit reports, so there is no multi-engine citation-gap source). Retained as a logged no-op pass-through; never fabricate aio_gap rows. Revisit only if Semrush ships AI-toolkit reports on MCP.

Off-page playbook (Lesson 10, outreach). Turns a vetted link-prospect list into them-focused outreach drafts that earn links without spamming. Picks the RIGHT people (those who LINKED to or MENTIONED our topic — never "people who tweeted it"), gives each a real "excuse" (fresh-angle / new-proof / ego-bait), keeps to ≤1 follow-up, and never makes a pushy link ask. Drafts only — every outward send is operator-gated. Outreach is a tool, not a strategy.

Compare an existing article's section coverage to the current SERP (Semrush phrase_organic + Firecrawl page extraction) and to the related-term / question landscape (phrase_related + phrase_questions). Find topics ranking pages cover that the article doesn't, and propose new sections.

Skeptical pushback on the outline before drafting starts. Asks whether the outline is MECE, BLUF, problem-agitate-solution; whether it covers the topic better than the SERP top-5; whether sections duplicate; whether each section earns its visual. One revision pass on FAIL (BLOG_AGENT_OUTLINE_REVISION_BUDGET, default 1).

Layer 1a of the keyword research pipeline. Generates seed keywords (broad niche terms) and modifiers (best, how-to, vs, calculator, etc.) from brand-config so the keyword multiplier has a real foundation to expand from. Replaces the "AI gives you generic keywords" problem with the structured 10-second hero prompt.

Master orchestrator for the keyword research pipeline. Chains topic-discovery → seed/modifier ideation → competitor + AI-search gap analysis → question mining → BID method → AIO cannibalization check → adversarial redteam → final ranked queue. Same anti-context-bloat pattern as /blog-pipeline (every layer is an Agent dispatch, never a Skill fork).

After the learner demonstrates understanding of a concept, present near-transfer and far-transfer challenges. Use to test whether learning is portable or task-specific — this is what separates understanding from familiarity.

Before any explanation or answer, require the learner to produce a free-recall attempt and confidence rating. Use when a student wants help understanding or reviewing a topic — this skill ensures the AI works from what the learner already knows.

The learner teaches the concept to the AI, which plays a curious novice peer and identifies gaps through authentic questions. Use when the learner wants to test their understanding — teaching forces a different kind of organisation than studying.

Design a multi-round questioning sequence for interrogating AI chatbot answers, tracking how responses shift and distinguishing genuine updates from sycophantic capitulation. Use when teaching students to probe AI critically.

Map a current event below the surface into patterns, structures, and mental models. Use when a class or team needs systemic understanding before action.