Results for “framingham-score”

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.

Provides a single 0-100 cycle score blending VIX, SPY IV Rank, Put/Call ratio, and valuation percentile to determine offense vs. defense posture, based on Howard Marks' market cycle framework.

Compute the F1Score metric using torchmetrics when predictions and ground-truth labels are available.

Score and rank options contracts for any ticker using a multi-factor model covering liquidity, IV attractiveness, Greeks balance, and risk/reward. Returns scored option chains with the best contracts highlighted.

Evaluate and rank agent results by metric or LLM judge for an AgentHub session.

Analyze Framer's marketing site design system, including color palette, typography hierarchy, spacing, and component patterns, to produce a structured design specification.

Use when you need to characterize score distributions of a profile HMM on random sequences, such as calibration checks, benchmarking, or filter-behavior experiments.

Scores competitors across nine weighted dimensions with explicit 1–5 rubrics and a tension plot, producing comparable profile cards for competitive analysis.

Compares 2-5 stocks or options across GBM Five Pillars scores, options metrics, technicals, and valuations, highlighting winners per category and providing an overall recommendation.

Evaluate and rank agent results by metric or LLM judge for an AgentHub session.

Prioritize product work with explicit criteria, scoring, tradeoffs, and decision rationale.

Benchmark-relative quality gate. Scores the draft against the research dossier's beat spec (depth, consensus coverage, evidence) plus AI-tell and voice signals, runs an adversarial read armed with the SERP benchmark, and emits the verdict that gates the pipeline.

Evaluates LLM performance using BLEU, ROUGE metrics and LLM-as-judge. Use for model testing.

Applies the TAM/SAM/SOM market sizing framework to guide decision-making, structure analysis, and produce actionable recommendations for product research.

贺炜（体育解说）认知与表达框架（压缩蒸馏）：文学比喻嵌入赛况、克制激情、终场金句… 触发：足球诗人解说 等。非煽动球迷对立

Lightweight ContentShake AI self-check the /draft stage can call before saving. Returns just SEO + Quality scores (no full optimization) so the writer knows whether the draft is in winning territory before /quality-check runs. Fails soft when SEMRUSH_API_KEY is unset.

Turn a pile of accounts into a stack-ranked priority list with a reason on every row. A layered score (gates first, then an evidence-weighted base rank over the signals you actually have, then bounded boosts for product usage and buyer intent) that stays fair across channels and never scores a blank field as a zero. Built for B2B GTM teams, customizable to your signals and your ICP. Trigger on "score these accounts", "rank by fit", "composite ICP score", "stack-rank my list", "who should I work first", "prioritize these leads", or any multi-signal account qualification.

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.

Recommends optimal multi-leg option strategies based on market view, with 15+ templates and full P&L profiles.

Score LLM and agent outputs using LLM-as-judge techniques — direct scoring against rubrics or pairwise comparison between two outputs. Includes built-in bias mitigation for position bias, length bias, and self-enhancement bias. Load when the user asks to score an output, judge a response, evaluate against a rubric, compare two outputs, do direct scoring, run pairwise comparison, or says "rate this", "which response is better", "score this against the rubric", "judge this output", "LLM as judge this". Sub-skill of eval-output orchestrator.

Compute TAM/SAM/SOM with top-down and bottoms-up methods, plan survey sample sizes with per-segment minimums, and score candidate segments against Kotler's five criteria.

Scores any US stock ticker through Warren Buffett's four-lens framework (business simplicity, moat, management, valuation) and returns a weighted HOLDABLE/WATCHABLE/AVOID verdict.

Evaluates machine-generated log summaries without human-written references, using LLM judgment and dense embeddings to score relevance, informativeness, and coherence.

Parse RFP/RFI/RFQ documents, score fit per requirement with verifiable proof points, generate win-themes, estimate winrate, and produce a bid/no-bid recommendation using the Shipley method.

The choice is not about scale or sophistication.

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

Use to measure customer effort from behavioural signals instead of CES surveys, and to audit whether a composite effort score is honest. Trigger for "customer effort score", "behavioural CES", effort without survey, repeat contacts and channel switches, transfers and reopens, or "our CES doesn't match operational data".

Diagnose frame-rate bottlenecks (CPU vs GPU bound FIRST), control Unreal Insights traces, sample live frame times, and annotate performance captures. Use when FPS is low/bad, the game is slow, or you need to find what is limiting the frame rate.

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

Create irresistible offers using the Value Equation, bonus stacking, risk-reversing guarantees, and ethical scarcity.

Judges agent outputs against LaRuche's quality standards, returning a structured scorecard with scores, verdict, and actionable corrections.

Flamingo: A Visual Language Model for Few-Shot Learning

Monthly P&L, cashflow forecast, runway calculation, and top movers. Investor-ready Gamma deck plus Slack summary, archived to Notion. Runs monthly on a 1st-of-month cron schedule.

Score and prioritize leads based on firmographic fit and behavioral engagement signals, producing ranked tiers for sales team focus. Use when the user requests lead scoring or provides relevant inputs for this workflow.

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