Results for “onesignal”

Train, evaluate, export, quantize, and run inference for a TAO OneFormer model that performs panoptic, instance, and semantic segmentation using task-conditioned queries.

One-shot lifecycle command that chains init → baseline → spawn → eval → merge in a single invocation.

One-shot lifecycle command that chains init → baseline → spawn → eval → merge in a single invocation.

Transcribe audio files to text with optional speaker diarization and known-speaker hints using OpenAI models.

Use this skill when conditioning, loading, preparing, or labeling signal data for analysis or ML training. Covers: cleaning a single signal (fill gaps, remove drift, deoutlier, denoise, resample/align a time base) BEFORE analysis; building a `signalDatastore` pipeline; creating a `labeledSignalSet` for Signal Labeler; deriving labels (filename, folder, in-file, ROI, time-frequency ROI); stratified train/val/test splits; framing long signals; parallel processing; and shaping datastore output for `trainnet`. Triggers include "clean up this signal", "remove drift / detrend", "fill gaps", "remove spikes / outliers", "denoise", "resample to a uniform rate", "align channels", "labels from filenames", "stratified split", "prepare for Signal Labeler", and function names like `fillgaps`, `fillmissing`, `detrend`, `filloutliers`, `smoothdata`, `resample`, `synchronize`, `signalDatastore`, `labeledSignalSet`, `filenames2labels`, `folders2labels`, `splitlabels`, `framesig`, `framelbl`, `createDatastores`.

Reverse shell generation and listener management. Generates platform-specific reverse shell payloads (bash, python, php, powershell, java, ruby, perl, netcat, socat, msfvenom) and sets up listeners in the Kali container. Supports one-liner generation, encoded payloads for WAF/filter bypass, listener setup with session capture, and shell stabilization. Chains from /pentester, /metasploit, or /post-exploit when command execution is confirmed.

One-shot lifecycle command that chains init → baseline → spawn → eval → merge in a single invocation. Use when the user runs /hub:run or asks to execute a full AgentHub competition end-to-end.

One-shot lifecycle command that chains init → baseline → spawn → eval → merge in a single invocation.

Generates images, videos, and audio using fal.ai models via MCP, covering text-to-image, text/image-to-video, text-to-speech, and video-to-audio.

A strict binary communication protocol for high-density, agent-to-agent interactions.

Generates images, videos, and audio using fal.ai models via MCP, covering text-to-image, text/image-to-video, text-to-speech, and video-to-audio.

Monitors a watchlist of Kalshi prediction markets for price moves, volume spikes, resolution proximity, and kill-criterion triggers, with cross-venue arbitrage detection against paired Polymarket markets adjusted for fees and slippage.

Language-independent tokenizer treating text as raw Unicode. Supports BPE and Unigram algorithms. Fast (50k sentences/sec), lightweight (6MB memory), deterministic vocabulary. Used by T5, ALBERT, XLNet, mBART. Train on raw text without pre-tokenization. Use when you need multilingual support, CJK languages, or reproducible tokenization.

Generate text-to-video with HappyHorse 1.0 on RunComfy. Documents HappyHorse 1.0's strengths (#1 on Artificial Analysis Video Arena, native 1080p with in-pass synchronized audio, multi-shot character consistency, 6-language prompt support), the duration / aspect-ratio / resolution schema, and when to route to Wan 2.7 / Seedance 2 / LTX 2 instead. Calls `runcomfy run happyhorse/happyhorse-1-0/text-to-video` through the local RunComfy CLI. Triggers on "happyhorse", "happy horse", "happyhorse 1.0", "happyhorse video", or any explicit ask to generate video with this model.

Multi-project signal isolation with cascading recipe resolution

Zero-shot univariate time-series forecasting with Google's TimesFM foundation model, producing point forecasts and prediction intervals from CSV/DataFrame/array inputs, with a preflight system checker for RAM/GPU. Use to forecast any univariate series (sales, sensors, energy, vitals, weather) without training a custom model. Part of the AlterLab Academic Skills suite.

Generate, inpaint, and outpaint music with ACE Step on RunComfy via the `runcomfy` CLI. ACE Step is StepFun-AI's open-weights music foundation model — tag-driven composition (genre, mood, instruments), multilingual lyrics with section markers, 5 s to 4 min stereo output, $0.0002–0.0003 per second (≈ 27× cheaper than ElevenLabs Music). Four endpoints: ACE Step text-to-audio (the default), ACE Step 1.5 text-to-audio (50+ language lyrics, refined structured-lyric handling), ACE Step audio-inpaint (regenerate a time range inside an existing track), ACE Step audio-outpaint (extend an existing track before or after). Triggers on "ace step", "ace-step", "acestep", "ACE music", "open music model", "cheap AI music", "inpaint audio", "audio inpaint", "extend music", "audio outpaint", "lengthen track", "music with tags", or any explicit ask to generate or edit music with ACE Step.

JACKAL — First Jump pyramider. FOX v1.6's exact five-layer entry gauntlet combined with RHINO's pyramiding mechanic. Enter at 30% on a qualifying First Jump, add 40% at +10% ROE and final 30% at +20% ROE — but only after re-validating 4h trend, SM alignment, and volume. Failed scouts cost $15 instead of FOX's $50. Full pyramids capture the same upside. DSL High Water Mode (mandatory).

Calculates a per-ticker panic index (0-100) from six weighted signals including VIX, IV Rank, RSI-14, volume anomaly, put/call ratio, and consecutive down days, triggering Bull Put Spread entry signals at scores ≥60.

Generates images, videos, and audio using fal.ai models via MCP, covering text-to-image, text/image-to-video, text-to-speech, and video-to-audio.

Generate, inpaint, and outpaint music with ACE Step on RunComfy via the `runcomfy` CLI. ACE Step is StepFun-AI's open-weights music foundation model — tag-driven composition (genre, mood, instruments), multilingual lyrics with section markers, 5 s to 4 min stereo output, $0.0002–0.0003 per second (≈ 27× cheaper than ElevenLabs Music). Four endpoints: ACE Step text-to-audio (the default), ACE Step 1.5 text-to-audio (50+ language lyrics, refined structured-lyric handling), ACE Step audio-inpaint (regenerate a time range inside an existing track), ACE Step audio-outpaint (extend an existing track before or after). Triggers on "ace step", "ace-step", "acestep", "ACE music", "open music model", "cheap AI music", "inpaint audio", "audio inpaint", "extend music", "audio outpaint", "lengthen track", "music with tags", or any explicit ask to generate or edit music with ACE Step.

Execute the full AgentHub competition lifecycle in a single command: initialize, capture baseline, spawn agents, evaluate results, and merge the winner.

Generate text-to-video with HappyHorse 1.0 on RunComfy. Documents HappyHorse 1.0's strengths (#1 on Artificial Analysis Video Arena, native 1080p with in-pass synchronized audio, multi-shot character consistency, 6-language prompt support), the duration / aspect-ratio / resolution schema, and when to route to Wan 2.7 / Seedance 2 / LTX 2 instead. Calls `runcomfy run happyhorse/happyhorse-1-0/text-to-video` through the local RunComfy CLI. Triggers on "happyhorse", "happy horse", "happyhorse 1.0", "happyhorse video", or any explicit ask to generate video with this model.

Use this skill for on-chain DEX operations: token search, swap quotes, DEX trading, wallet portfolio/balance queries, gas estimation, and transaction broadcasting across 20+ blockchains (Ethereum, Solana, Base, BSC, Arbitrum, Polygon, etc.). Use when user says: 'swap ETH for USDC', 'buy token on-chain', 'DEX swap', 'token search on-chain', 'wallet balance', 'portfolio value', 'gas price', 'broadcast transaction', 'trending on-chain tokens', 'hot tokens', 'token holders', 'token liquidity', 'smart money signal', 'whale signal', 'K-line on-chain', '链上交易', '链上swap', 'DEX交易', '买币', '链上行情', '钱包余额', '持仓', 'gas费', '广播交易', '链上热门币', '聪明钱', '巨鲸信号'. Powered by OKX Web3 DEX API with 500+ liquidity sources. MUST run node scripts — NEVER fabricate on-chain data. For CEX trading (Binance/OKX spot/futures), use aicoin-trading. For CEX market data (funding rates, OI, liquidation), use aicoin-market.

VIXEN v1.0 — Dual-mode emerging movers scanner. Built from FOX v1.6 live trading data (+34.5% ROI). Two entry modes: STALKER (steady accumulation, score 6+, catches the SM buildup BEFORE the explosion) and STRIKER (violent FIRST_JUMP, score 9+, with raw volume confirmation to filter blow-off tops). 2-hour per-asset cooldown after Phase 1 exits. DSL High Water Mode mandatory.

SENTINEL v2.0 — Quality Trader Convergence Scanner. Inverted pipeline: find ELITE/RELIABLE traders, see where they converge. When 5+ quality traders hold the same asset in the same direction, enter.

v5.6.0 joint multi-TF model: single model per symbol with broadcast 1Hour context replaces dual 15Min/1Hour models. Trigger: (1) replacing weighted-voting model aggregation, (2) adding broadcast features to vectorized env, (3) limited training data + worried about overfitting from doubling obs_dim, (4) backtest builder mismatch with newer feature counts.

LLaVA-OneVision: Easy Visual Task Transfer

Queries and monitors UniFi network devices, clients, health, DPI traffic, and alerts via the local UniFi OS gateway API.

Draft an anonymous one-page teaser for a sell-side M&A process — enough to spark buyer interest without revealing the target's identity before an NDA is signed.

Query and monitor UniFi network devices, clients, health, top applications, and alerts via the local UniFi OS gateway API.

Search the web for companies showing buying signals matching the agency ICP. Enforces 75/25 geo split and 20 gig + 5 company daily targets.

Language-independent tokenizer treating text as raw Unicode. Supports BPE and Unigram algorithms. Fast (50k sentences/sec), lightweight (6MB memory), deterministic vocabulary. Used by T5, ALBERT, XLNet, mBART. Train on raw text without pre-tokenization. Use when you need multilingual support, CJK languages, or reproducible tokenization.

Generate text-to-video with HappyHorse 1.0 on RunComfy. Documents HappyHorse 1.0's strengths (#1 on Artificial Analysis Video Arena, native 1080p with in-pass synchronized audio, multi-shot character consistency, 6-language prompt support), the duration / aspect-ratio / resolution schema, and when to route to Wan 2.7 / Seedance 2 / LTX 2 instead. Calls `runcomfy run happyhorse/happyhorse-1-0/text-to-video` through the local RunComfy CLI. Triggers on "happyhorse", "happy horse", "happyhorse 1.0", "happyhorse video", or any explicit ask to generate video with this model.

Call 100+ LLMs through a single OpenAI-compatible interface with LiteLLM — use completion/acompletion/embedding with any provider (Anthropic, OpenAI, Google, Groq, Ollama, etc.), run a proxy server for team rate-limiting and cost tracking, load-balance across providers.