Results for “equity-score”

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.

Use to compare BPO sites, vendors or partner teams fairly, adjusting for the work mix each is given before concluding anything about performance. Trigger for "compare our BPO sites", "which vendor is performing best", "site A scores lower than site B", outsourcer QBR packs, partner MI reporting, or setting contractual quality targets with a vendor.

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

Score, evaluate, and iteratively improve any content or strategy using an auto-assembled panel of domain experts. Handles copy, sequences, landing pages, strategy docs, titles, charts, recruiting evaluations, or anything else that needs a quality gate. Recursively iterates until all scores hit 90+ (max 3 rounds). Use when asked to: "expert panel this", "score this", "rate these variants", "quality check this", "panel review", "which version is better", "expert score", "evaluate this copy/strategy/page", or when another skill needs a quality gate on its output. Also triggers on: "score this landing page", "expert panel these email variants", "rate this headline", "panel these charts".

Screen US stocks for high-quality dividend opportunities combining value characteristics, attractive yields, and consistent growth using a two-stage screening approach with FINVIZ Elite and FMP APIs.

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.

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

Score and iteratively improve marketing content with an expert panel until 90+. Use for content quality gates, expert panel reviews, editorial scoring, or when another skill needs a content QA loop.

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

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

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.

Screens resumes with skill matching, experience evaluation, and bias-free candidate ranking

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

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.

Evaluate and rank agent results by metric or LLM judge for an AgentHub session. Use when the user runs /hub:eval or asks to score, compare, or pick a winner among completed AgentHub agents.

Analyzes state merit review for non-covered securities offerings, applying NASAA Statements of Policy to cheap stock, promoter equity investment, voting rights, and promoter compensation. Produces examiner-ready comment responses with cap table analysis and negotiation strategy. Use when filing Reg A, Rule 504, intrastate, or direct public offerings in merit review states, responding to Blue Sky examiner comments, structuring offerings to avoid conditioning, or analyzing NASAA SOPs. Also trigger on cheap stock analysis, promoter equity tests, unequal voting rights review, state examiner correspondence, or phrases like "merit review issues" or "the state examiner sent comments."

Systematically evaluates alternatives against weighted criteria, builds a comparison matrix, and recommends the best option with documented rationale and trade-offs.

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.

Synthesize market breadth, regime, and flow signals into a unified capital commitment recommendation with exposure ceiling, bias, and action guidance.

Integrate FIRST's Exploit Prediction Scoring System (EPSS) API to prioritize vulnerability remediation based on real-world exploitation probability within 30 days.

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.

RICE feature prioritization with scoring and capacity planning. Usage: /rice prioritize <features.csv> [options]

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

Takes a curriculum framework and a statutory or accreditation requirement list; produces a coverage table, gap summary, and CSV showing which framework content covers each requirement and where gaps exist.

Aesthetic assessment and design scoring across 6 dimensions. Use for UI critique, design review, visual quality assessment, remix suggestions. Activate on "design critique", "aesthetic review", "UI assessment", "visual quality", "design score", "remix this design". NOT for implementation (use frontend-developer), accessibility-only audits (use color-contrast-auditor), or brand identity creation.

Compute the Equal Error Rate (EER) metric using torchmetrics for binary, multiclass, or multilabel classification tasks, with reference signatures and usage examples.

Stock quotes, history, search, compare, crypto via Yahoo.

Scores an inbound message on budget, fit, and intent, then recommends whether to call, nurture, or skip the lead.

Score the experience a new customer actually has, not just whether they activated. Grades the first 90 days on the moments that make someone feel taken care of, finds where the welcome goes cold, and hands back a scored audit with the specific fixes. Built for any post-sale team, customizable to your product and lifecycle. Trigger on "audit my onboarding", "score our onboarding experience", "why do new customers churn early", "grade our first 90 days", "where does onboarding go cold", "is our welcome any good", or any onboarding-quality question.

Query real-time and historical equity market data—prices, news, financial statements, metrics, analyst estimates, insider and institutional activity, SEC filings, earnings press releases, segmented revenues, stock screening, and macro interest rates. Use when you need broad public-market research from a single AIsa-backed skill. Use when: the user needs market data, stock analysis, dividend research, or read-only financial data workflows.

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.

Use to design or audit a QA dispute and appeal workflow with timeboxes, adjudication standards, and second-level consistency so appeals improve trust instead of rewriting scores without rules. Trigger for "QA appeal process", "agents disputing scores", "who adjudicates QA disputes", "overturn rate too high", second-level review standards, or calibration erosion from ad-hoc score changes.

Cost-benefit analysis. Produces economic NPV and financial NPV side by side, with BCR, optimism bias (with mitigation), Marginal Excess Tax Burden, real-terms rebasing, WELLBY / QALY / VPF wellbeing valuation, sensitivity, switching values, EANC for unequal-life options, validation gate, and a one-line headline verdict (socially worthwhile vs financially self-sustaining). Backed by the greenbook R package (HM Treasury Green Book primitives) when available, with graceful fallback. Supports HMT Green Book, EU Better Regulation, World Bank, ADB, and Victorian HVHR. Reads a longlist markdown file directly via --from.

Design structured evaluation rubrics for scoring LLM and agent outputs — defining quality dimensions, scoring scales, hard gates, score descriptions, and edge cases. Load when the user asks to create an eval rubric, define evaluation criteria, design scoring dimensions, write an eval spec, or says "what should I evaluate", "design a rubric", "create eval criteria", "define quality dimensions", "evaluation rubric for", "how do I measure quality of". Sub-skill of eval-output orchestrator.

Score Analyzer