Results for “momentum-scan”

Screen US stocks for Stockbee-style short-term momentum burst setups using 4% breakout, dollar breakout, range expansion, volume expansion, prior range contraction, close-location, failure filters, and risk-distance scoring.

nuScenes: A Multimodal Dataset for Autonomous Driving

Provides a standardized interface for ingesting raw data across domains such as genomics, network analysis, document review, and spatial mapping, converting it into semantic vectors for agent use.

Import object detection models from HuggingFace or NVIDIA NGC into a DeepStream pipeline with automated ONNX download, TensorRT engine build, custom parser, multi-stream benchmark, and PDF report generation.

Train, evaluate, export, and run inference for Masked Auto-Encoder (MAE) models for self-supervised pretraining and fine-tuning of visual representations.

Trains and fine-tunes vision models for object detection, image classification, and segmentation using Hugging Face Transformers on cloud GPUs, with automatic dataset validation and Hub persistence.

Configure and run Nessus vulnerability scans, analyze results, and integrate scanning into continuous vulnerability management workflows.

Scan for OSS tool opportunities from npm gaps, GitHub trends, and developer pain points

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.

Run STORM phase 1 — multi-perspective research. This skill should be used when the user asks to "research a topic for an article", "find sources on X from multiple perspectives", "do storm research on X", or invokes /storm:research. Discovers personas, runs simulated Q&A per persona in parallel, and produces an information table + deduplicated sources.

Screen US stocks using William O'Neil's CANSLIM growth stock methodology with composite scoring and weighted components.

DashScope (Alibaba Cloud Bailian / 阿里云百炼) integration — image generation (qwen-image-2.0-pro), text-to-speech (qwen3-tts-flash), and ASR with word-level timestamps (qwen3-asr-flash-filetrans). Use when generating images via Qwen-Image, narrating via Qwen-TTS, or transcribing with word-level timestamps via Qwen-ASR.

Establishes recurring vulnerability scanning workflows using Nessus, Qualys, or OpenVAS, prioritizes findings with risk scoring and CISA KEV data, integrates with SIEM for exploitation detection, and tracks remediation via SLA-based dashboards and automated ticketing.

Screen US stocks for Stockbee-style selling-exhaustion hammer setups using prior momentum, pullback depth, undercut/reclaim, long lower-wick geometry, close-location, volume confirmation, quality/liquidity gates, and risk-distance scoring.

Build and maintain a model book for Stockbee-style Momentum Burst setups by ingesting screener candidates, updating 3-day and 5-day forward outcomes with MFE/MAE and stop-hit status, and summarizing cohort statistics to improve setup recognition.

Scans CoinGecko for top movers, losers, and trending coins, enriched with pump-risk flags for pre-trade screening.

Scan a codebase, live app, or site and find the features that obviously should exist but don't — missing exports, bulk actions, search/filter, undo, history, notifications, dead-end workflows, data collected but never surfaced, backend endpoints with no UI, and half-built features that stopped partway. Produces an evidence-backed, ranked punch list (top 10 max) with a portable gap_packet JSON. Use whenever the user asks "what features are missing", "what's this app lacking", "find gaps", "run gap-scan", "feature audit", or wants proposal ammo from a prospect's app. Also use it to recheck an existing gap_packet — "what's left to do", "recheck the gaps", "what got built since" — re-verifying findings against current code and marking stale ones superseded. Flags ABSENCE only — broken code goes to code-audit, confusing flows to ux-audit, weak persuasion to conversion-audit, underexploited assets to a WWBD-style analysis. If something exists but is bad, it's not a gap — hand it off.

Requires python3, and AISA_API_KEY. Uses the supplied AISA_API_KEY to send requests to https://api.aisa.one. Native-first ClawHub plugin for `stock-hot`. Ships the packaged AIsa skill with an `openclaw.plugin.json` manifest and a Claude-compatible bundle fallback. Hot Scanner — find the most trending and high-momentum stocks and crypto right now via AIsa API. Top gainers, losers, most active by volume, crypto highlights, news catalysts, and top 5 watchlist picks. Use when the user asks about trending stocks, what's hot, market movers, or momentum plays.

Generate driving scenes, scenarios, road surfaces, and 3D content from scenariobuilder.* sensor data (GPS, camera, lidar, actor tracks) using Scenario Builder for Automated Driving Toolbox. BUILD, EXPORT, or AUGMENT a virtual scenario/scene/map: ego or actor trajectories, trajectory smoothing, OpenCRG road-surface extraction, 3D asset generation, static-object placement, point-cloud georeferencing + elevation, lane-based ego localization, sensor-fusion tracking, scenario-event extraction (cut-ins, hard brakes, near-misses, ADAS disengagements), or export to RoadRunner, drivingScenario, OpenDRIVE, OpenCRG, OpenSCENARIO, or Unreal Engine. Also: log-to-scenario, scenario harvesting, accident/near-miss reconstruction, SOTIF (ISO 21448) and ISO 26262 scenario coverage, USGS-aerial-lidar augmentation, traffic-sign placement, vision-based vehicle classification for actor assets. NOT for raw-data import or multi-sensor sync/crop/offset/timestamp normalization — route those to matlab-import-driving-data.

Computer vision engineering skill for object detection, image segmentation, and visual AI systems. Covers CNN and Vision Transformer architectures, YOLO/Faster R-CNN/DETR detection, Mask R-CNN/SAM segmentation, and production deployment with ONNX/TensorRT. Includes PyTorch, torchvision, Ultralytics, Detectron2, and MMDetection frameworks. Use when building detection pipelines, training custom models, optimizing inference, or deploying vision systems.

Scan agent skills for security issues before adoption. Detects prompt injection, malicious code, excessive permissions, secret exposure, and supply chain risks.

Detect adversary lateral movement across networks using Splunk SPL queries against Windows authentication logs, SMB traffic, and remote service abuse.

Configure, simulate, debug, and analyze radarDataGenerator within radarScenario. Use for: interactively building radar detection scenarios from datasheets or performance requirements; diagnosing missed detections and configuration errors; interpreting sensor spherical, body, and scenario-frame outputs; deriving ReferenceRange from hardware specs via link budget; scan mode configuration (mechanical, electronic/AESA, hybrid); and validating simulation results against analytical predictions.

Run and analyze molecular dynamics simulations with OpenMM and MDAnalysis. Set up protein/small molecule systems, define force fields, run energy minimization and production MD, analyze trajectories (RMSD, RMSF, contact maps, free energy surfaces).

Screens recent post-earnings stocks using a 5-factor scoring system (Gap Size, Pre-Earnings Trend, Volume Trend, MA200 Position, MA50 Position) and assigns A/B/C/D grades to identify momentum trade candidates.

Detect authentication attacks, privilege escalation, persistence mechanisms, and lateral movement by analyzing Windows Security, System, and Sysmon event logs in Splunk using SPL queries mapped to MITRE ATT&CK techniques.

Routes requests into the correct obsidian-mind mode: install, daily session loop, capture, review, maintenance, multi-agent wiring, or semantic search, using a ready-made Obsidian vault template for persistent agent memory.

SHARK v3.0 — SM Conviction + Liquidation Cascade Hunter. Consolidated from v1.0's 8-cron pipeline into a single scanner. 4-gate entry: SM concentration (30+ traders, 5%+) → top 5 trader alignment → price momentum → funding structure. Score 8+ to enter. DSL manages all exits. No thesis exit. DSL exit managed by plugin runtime via runtime.yaml.

A funnel and account-scoring diagnostic engine for any sales org. Connect a CRM and a product-analytics tool (plus optional enrichment, community, and meeting tools). Three modes. (1) FUNNEL DIAGNOSIS finds leaky conversion gates by channel with per-stage leakage, dollarized leverage points, and cohort velocity. (2) SPRINT PLANNING enriches qualified accounts into a ranked backlog with verified buying committees. (3) SCORING AUDIT finds where your scoring model is missing real ICPs. Trigger on 'funnel diagnosis', 'diagnose the funnel', 'where are we leaking', 'why is [channel] underperforming', 'conversion by channel', 'sprint planning', 'score these accounts', 'find missed ICPs', 'audit the scoring model', or any channel-level cohort-conversion, account-prioritization, or scoring-gap question.

KOMODO v1.0 — Momentum Event Consensus. Uses leaderboard_get_momentum_events (real-time threshold crossings) to detect when 2+ quality SM traders cross momentum thresholds on the same asset/direction within 60 minutes. Confirmed by market concentration + volume. Enters with the momentum. Replaces MANTIS v1.0 and SCORPION v1.1 (both used stale position data).

Lightweight scanner tracking Smart Money market concentration across all Hyperliquid assets. Flags assets accelerating up the ranks before they become crowded top-3 plays. IMMEDIATE_MOVER signal fires on 10+ rank jumps with quality filters (erratic history, velocity gate, trader count floor, max leverage check). One API call per scan, runs every 60 seconds. Use when detecting SM rotations, finding emerging opportunities early, or monitoring rank acceleration patterns.

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

Evaluates machine learning classifiers on their ability to distinguish signal from background in particle physics simulations, measuring how well algorithms rank signal events above background ones using the AUC metric.