Results for “fear-greed-index”

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.

Detects whether current market conditions match David Tepper's historic panic-buy signal by combining VIX, FearScore, and a quality filter.

Maps the current VIX value to a 5-tier fear-thermometer classification with strategy hints for options sellers, including 1-year percentile and distribution data.

Performs STRIDE-A threat model analysis of repositories and systems, producing architecture overviews, DFD diagrams, prioritized findings, and executive assessments. Supports both single analysis and incremental updates with change tracking.

Interprets Culture Index survey data, behavioral profiles, and personality assessments from JSON or PDF. Supports individual profile interpretation, team composition analysis, burnout detection, hiring profiles, manager coaching, interview transcript analysis, and conflict mediation.

Provides a structured risk scoring methodology for Data Protection Impact Assessments aligned with ENISA threat taxonomy and ISO 29134. Covers likelihood and severity assessment, risk matrix construction, inherent vs residual risk calculation, and risk appetite thresholds per EDPB WP248rev.01 guidance. Keywords: risk scoring, DPIA risk matrix, likelihood, severity, ENISA, ISO 29134, residual risk, risk appetite.

Guides database index selection, creation, and maintenance with SQL examples for PostgreSQL and MySQL.

Threat modeling workflow for software systems: scope, data flow diagrams, STRIDE analysis, risk scoring, and turning mitigations into backlog and tests. Use when designing new features, reviewing architecture changes, handling sensitive data, or hardening auth/payment/multi-tenant flows.

Record buy rationale and structured sell conditions for stock positions, with automatic monitoring to detect when exit triggers fire.

Read-only OWASP LLM Top 10 audit of app-facing AI features: prompt injection, data leak, supply chain, poisoning, unsafe output, excessive agency, system-prompt leak, RAG/embedding risks, misinformation, unbounded consumption. Use when "audit LLM security", "prompt injection", "jailbreak my chatbot", "is my AI safe".

Runs a side-by-side walk-forward backtest of a Bull Put Spread strategy with and without a FearScore entry signal, comparing performance metrics over ~8 years of daily data.

Auto-mirror top Discovery traders on Hyperliquid. Scans top 50 traders, scores on PnL rank (35%), win rate (25%), consistency (20%), hold time (10%), drawdown (10%). Creates 2-5 mirror strategies with overlap checks. Daily rebalance with 2-day watch period before swaps. Use when setting up trader mirroring, copy trading, or portfolio auto-rebalancing based on Discovery leaderboard performance.

Map product assumptions by risk, confidence, evidence, and next validation step.

Confidence Pipeline Fix (v5.4.1)

Provides access to Greip API endpoints for geolocation, IP lookup, threat intelligence, payment scoring, email and phone validation, profanity detection, and account management.

Queries the FRED (Federal Reserve Economic Data) API for 800,000+ economic time series from 100+ sources, covering GDP, unemployment, inflation, interest rates, exchange rates, housing, and regional data. Use for macroeconomic analysis, financial research, policy studies, economic forecasting, fetching U.S. or international economic indicators by FRED series ID, and academic research requiring historical economic time series. Part of the AlterLab Academic Skills suite.

当面对新任务或目标，因过度思考“如何开始”或“需要多久”而陷入拖延、焦虑，迟迟无法行动时

Codebase Memory — Knowledge Graph Tools

Detect insider threat behavioral indicators including unusual data access, off-hours activity, mass file downloads, privilege abuse, and resignation-correlated data theft.

Layer 4 of the keyword research pipeline. Spawns a "skeptical SEO" adversarial sub-agent to argue against every survivor of Layers 1-3. Catches mechanical-classifier blind spots — wrong SERP intent, hidden link-graph gauntlets, AIO trajectory shifts, vanity-rank metrics. Same pattern as quality-check's adversarial draft read, applied to keyword selection.

Chief Security Officer mode. Infrastructure-first security audit: secrets archaeology, dependency supply chain, CI/CD pipeline security, LLM/AI security, skill supply chain scanning, plus OWASP Top 10, STRIDE threat modeling, and active verification. Two modes: daily (zero-noise, 8/10 confidence gate) and comprehensive (monthly deep scan, 2/10 bar). Trend tracking across audit runs. Use when: "security audit", "threat model", "pentest review", "OWASP", "CSO review". (gstack) Voice triggers (speech-to-text aliases): "see-so", "see so", "security review", "security check", "vulnerability scan", "run security".

STRIDE Threat Model

Build quiz and assessment funnels that generate qualified leads through interactive scorecards, covering concept hooks, question design, dynamic results, and automated follow-up sequences.

当意识到自身金钱观念存在限制，并希望采取建设性态度改善财务状况时

Honestly evaluate AI work quality using a two-axis scoring system with mandatory devil's advocate reasoning and cross-session anti-inflation detection.

Validate Pleasur.ai Stage 01 blog-keyword candidates with the Business potential, Intent, and Difficulty method using current Ahrefs evidence. Use before AIO vetting and prioritization to reject poor business fits, mismatched search intent, and SERPs the brand cannot realistically win.

Route empirical-research requests through the Auto-Empirical Research Skills catalog when this whole repository is installed as one skill in Codex, CodeBuddy, Claude Code, or another IDE. Use to choose and load the right vendored AERS skill for causal inference, econometrics, replication, data acquisition, manuscript writing, peer review and referee responses, citation checking, de-AIGC editing, or full empirical-paper workflows without reading the entire repository at once.

Analyzes global market conditions across US, European, and Asian indices, forex, commodities, and economic indicators to produce structured market reports with risk sentiment assessment.

Perform reconnaissance, persistence, privilege escalation, and data pillaging on Microsoft 365/Entra ID tenants via the Microsoft Graph API using the GraphRunner PowerShell toolset.

Triages security incidents by classifying type, assigning severity based on business impact, enriching with threat intelligence, and routing to appropriate response teams using NIST SP 800-61r3 and SANS PICERL frameworks.

Scores real estate leads based on engagement, budget, timeline, and property preference matching

Analyzes the volatility smile and skew for a single options expiration, providing implied volatility curves, skew metrics, and shape classification to reveal market pricing of tail risk and directional fear.

Detect insider threats by modeling normal user and entity behavior with Elasticsearch, computing anomaly scores, and correlating low-confidence indicators into high-confidence alerts.

Track investment theses across their lifecycle — from screening idea to closed position with postmortem. Register theses from screener outputs, manage state transitions, attach position sizing, review due dates, and generate postmortem reports with P&L and MAE/MFE analysis.

根据研究者提供的**研究计划书（Research Proposal）**执行基于**外国（美国/欧盟/英国/日本/跨国）制度环境**的公司金融类实证研究全流程。**启动后第一件事：根据计划书的主题、识别策略、贡献边际与样本范围，从外国 CF 顶刊池（AER/QJE/JPE/JF/JFE/RFS/JFQA/JAR/JAE/MS/JCF/JBF 等 25+ 期刊）中推荐 5 本最匹配的目标期刊（[J1]–[J5]），等待研究者明确选定一本；该期刊决定 main.tex 的 bibliographystyle、Section 骨架、Introduction 风格与表注规范**。然后用 Python 完成数据清洗、描述性统计、基准回归、内生性检验（IV/2SLS、DML）、平行趋势、异质性、机制、稳健性检验与图表绘制。LaTeX 表格和图像严格遵循 template/ 示例格式，研究逻辑与排版严格遵循 rule/ 下的《通用实证研究逻辑与规范总结》与《回归表写作规范总结》。数据集与政策集从 asset/ 中按计划书中的关键词检索（WRDS / NBER/Fed releases / FRED / 全球宏观库）。**当计划书预期的实证结果无法实现时（系数不显著、平行趋势不通过、IV 弱工具、机制不成立等），skill 自动切换备选方案直至完成研究项目**。最终交付物：Python 代码 + LaTeX 表格 + 图像（.pdf/.png）。触发条件：研究者提交研究计划书（含 X→Y 假设、识别策略、样本、政策冲击等）。