Results for “sign”

Analyze S&P 500 market breadth charts and CSV data to assess market health, identify trading signals, and generate positioning recommendations for different trader types.

Analyzes local SEO signals including Google Business Profile optimization, NAP consistency, citation health, review signals, local schema markup, and location page quality, with business type and industry vertical detection.

Search, filter, and browse high-signal academic papers using the Paperzilla CLI.

Process and analyze physiological signals including ECG, EEG, EDA, RSP, PPG, EMG, and EOG using Python.

Create a working customer profile from existing research, market signals, and team knowledge before deeper validation.

Scouts market opportunities, identifies gaps, and surfaces recent demand signals with evidence and next actions via the MSX API.

Registers a trading agent on zHive, fetches crypto signals, posts predictions with conviction, and competes for accuracy rewards.

Filters and ranks trending Hugging Face models, datasets, and spaces by novelty and significance, providing a 'why notable' explanation for each pick.

Filters top Hacker News stories by interest tags and extracts high-signal comments (top, dissenting, expert/builder) with themed clustering.

Rigorous market researcher for Prodig Suites. Use for competitor analysis, demand signal analysis, gap identification, market intelligence. Triggers: 'market research', 'competitor analysis', 'demand validation', 'market gaps', 'opportunity research', 'competitive landscape'.

Tracks crypto and AI narratives with mindshare scores, velocity arrows, phase labels, named drivers, and position calls. Detects phase transitions and reflexivity signals.

Analyze any text to extract a cognitive fingerprint across 12 dimensions, revealing reasoning patterns, decision styles, and thinking signatures.

Access real-time X/Twitter attention intelligence for Base chain tokens via the checkr API, including trending tokens, social spikes, attention/price divergence signals, and narrative summaries.

Research companies, industries, or competitor sets using web search and seven analytical lenses to produce structured intelligence for downstream product management tasks.

Recovers transform chains, hidden media payloads, mobile signing paths, and local trust boundaries in CTF challenges involving crypto, encoding, steganography, APK, IPA, and mobile trust-boundary analysis.

Tracks a named competitor's launches, pricing changes, and positioning shifts, and alerts only on significant moves with source-backed analysis.

Tracks institutional investor ownership changes and portfolio flows using 13F filings data to identify stocks with significant smart money accumulation or distribution.

Runs focused experiments to validate feasibility of an idea, saving artifacts to .planning/spikes/ and supporting both idea and frontier modes.

Cross-platform 30-day social research on a topic — Reddit, X/Twitter, Hacker News, Polymarket, open web. Clusters mentions by narrative, ranks by velocity, and surfaces the contrarian thread with traction.

Generates a three-panel analysis for any ticker following Duan Yongping's seller-only options strategy: Sell Put at a willing-buy price, Covered Call for yield, and a VIX-based panic-buy signal.

Models cascading what-if scenarios across all business functions to prepare for compound adversity, using a structured six-step framework that maps variables, domain impacts, cascade effects, severity levels, early warning signals, and hedging strategies.

Multi-signal trend forecasting for autonomous agents. Combines prediction market odds, Twitter/X social sentiment, news velocity, and stock market data into a unified trend analysis with confidence scoring. Powered by AIsa — one API key, five data streams. Use when: the user needs X/Twitter research, monitoring, posting, or engagement workflows.

Comprehensive results analysis for empirical research: generate publication-quality descriptive statistics and balance tables, interpret regression coefficients with economic magnitude and effect sizes, assess identification assumption diagnostics, and produce structured results memos. Use when asked to create summary statistics, Table 1, balance tests, interpret results, assess economic significance, or write results narratives.

Query the openFDA API for drugs, medical devices, adverse event reports, recalls, regulatory submissions (510k, PMA), and substance identification (UNII). Use when searching FDA safety data, pharmacovigilance and adverse-event signals, device clearances, drug labels, or recall records for regulatory data analysis and safety research. Part of the AlterLab Academic Skills suite.

Analyzes DeFi protocol risk before deposit, decomposes headline APY into sustainable yield, emissions, and drag factors, and flags common rug-risk patterns in tokenomics, liquidity, and governance. Advisory only—no wallet access, no private key handling, no transaction signing, no on-chain execution.

Produces a decision-grade dossier on a specific company, person, nonprofit, or government org by testing the user's hypothesis rather than confirming it. Outputs an editable Word document with verdict, identity facts, 12-month activity timeline, network and reputation signals, and red flags.

Analyzes competitor products by synthesizing data from pricing pages, app store reviews, job postings, SEO signals, and social media into structured competitive intelligence. Produces feature comparison matrices, SWOT analyses, positioning maps, UX audits, pricing breakdowns, action plans, and presentations.

Transforms Chan Theory technical analysis into an executable, reproducible research workflow with strict level, structure, and invalidation gates for A-shares, Hong Kong stocks, ETFs, indices, and futures. For research and study only; never issues stock tips or promises returns.

Quantifies market top probability using O'Neil Distribution Days, Minervini leading stock deterioration, and Monty defensive sector rotation, producing a 0-100 composite score with risk zone classification for tactical 2-8 week timing signals.

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 `trend-forecast`. Ships the packaged AIsa skill with an `openclaw.plugin.json` manifest and a Claude-compatible bundle fallback. Multi-signal trend forecasting for autonomous agents. Combines prediction market odds, Twitter/X social sentiment, news velocity, and stock market data into a unified trend analysis with confidence scoring. Powered by AIsa — one API key, five data streams. Use when: the user needs X/Twitter research, monitoring, posting, or engagement workflows.