Loading...
Loading...
Found 6,567 Skills
Use when a user wants an agent to convert USDT BEP20 into UAH payout instructions through a verified exchange flow with approval, expiry, AML screening, and payment monitoring.
Save a live webpage as a high-fidelity PDF that preserves the original layout AND every image (including lazy-loaded ones) using the agent-browser CLI. Use this whenever the user asks to "download this page as PDF", "save this article", "archive this URL", "fetch this page for reference", or otherwise wants a local PDF of a web page that looks like the browser version. Especially important on modern JS-heavy sites (engineering blogs, Next.js sites, anything with IntersectionObserver lazy loading) where naive `chrome --headless --print-to-pdf` or a bare `agent-browser pdf` produces blank rectangles or broken-image placeholders. Trigger this skill even when the user does not name the tool - any request to capture a webpage's full visual content as a PDF on disk should pull this in. For reader-mode/article-only output (no nav, no footer, no manual trimming) prefer percollate instead - see "When NOT to use this".
This skill should be used when the user wants to check whether an agent skill is portable across providers. Common triggers include "is this skill cross-provider safe", "will my skill work in cursor", "audit skill compatibility", "check if this loads in codex", and "which providers support this skill". Spawns one agent per provider in parallel using bundled provider-doc snapshots (refreshed on cadence — never fetched at runtime) and produces a compatibility matrix plus a COMPAT.md report. Skip when authoring a new skill (use skill-creator) or rerunning baselines (use skill-eval).
This skill should be used when the user wants to create a new agent skill, scaffold a SKILL.md, validate an existing skill against repo rules, or refactor a skill to match this monorepo's conventions. Common triggers include "build a skill for X", "create a new skill", "scaffold a skill", "add a skill that does Y", "make me a skill", "audit this skill against our rules", and "refactor this skill to match repo conventions". Enforces kebab-case naming, verbatim trigger phrases, selective XML for example boundaries, and a RED→GREEN→REFACTOR cycle. Skip when modifying source code, debugging an existing skill, or writing non-skill markdown.
Search, install, list, remove, update, or scaffold AI agents with the `agentshq` CLI across many coding CLIs and IDEs. Use when the user wants to discover agents, install them into specific clients, or manage an existing agent catalog.
Use this skill when managing multiple TON wallets, importing agentic wallets, or rotating operator keys. It switches between wallets, lists all wallets in the registry, imports existing wallets, and rotates operator keys. Also use when the user mentions wallet management, switching accounts, or key rotation on TON.
Read a plan document, decompose it into steps, design a per-step agent chain from the ECC catalogue, and emit ready-to-paste /orchestrate custom prompts. Generative only — never invokes /orchestrate itself. Use when the user has a multi-step plan and wants to drive it through orchestrate without composing chains by hand.
Use this skill for Fabric.so CLI workflows with the `fabric` terminal command: diagnose/install/login, search or browse a Fabric library, save notes/links/files, create folders, ask the Fabric AI assistant, manage tasks/workspaces, generate shell completion, check subscription usage, produce JSON output, and use Fabric as persistent agent memory. Do not use for Microsoft Fabric/Azure/Power BI `fab`, Daniel Miessler's Fabric framework, Python Fabric SSH, Fabric.js, or textile/fashion fabric.
Install and configure LLMem for an agent harness. Handles CLI install, plugin deployment, skill registration, and provider setup. Triggers on: "install llmem", "set up memory", "configure memory", "add llmem to harness", "memory setup".
AI autonomous research agent for LLM training optimization using opencode as the agent. The agent autonomously modifies train.py, runs experiments, evaluates val_bpb, and iterates to find the best model. Use when: "run autoresearch", "start experiment", "train model", "autonomous research", "optimize LLM training".
RWA Alpha v1.1 — Real World Asset Intelligence Trading Skill. NewsNow macro event detection + Polymarket probability confirmation + on-chain price action → auto-trade tokenized treasury/gold/yield/governance tokens via OKX DEX (onchainos CLI). Three modes: Yield Optimizer (conservative) / Macro Trader (balanced) / Full Alpha (aggressive). Multi-chain: Ethereum + Solana via Agentic Wallet TEE signing. Trigger: RWA, real world asset, tokenized treasury, gold token, USDY, OUSG, PAXG, ONDO, CFG, PENDLE, PLUME, OM, GFI, TRU, bIB01, yield rotation, macro trading, macro event, NAV premium, NAV discount, credit expansion, credit tightening.
Take the Clawvard entrance exam, report the result, and optionally save the agent identity token with explicit user confirmation.