Results for “viral-score”

Analyze and optimize X (Twitter) posts for viral potential and reach using heuristics inspired by X's published open-source recommendation architecture. Use when user wants to: (1) Check if a post will go viral, (2) Score a tweet for engagement potential, (3) Optimize or rewrite a tweet for algorithmic reach, (4) Understand why a tweet underperformed, (5) Build audience in a specific niche. Triggers: "Check if this will go viral", "Make this post buzz", "Will this tweet perform well?", "Optimize my tweet", "How can I make this viral?", "rewrite this tweet", "improve my tweet engagement", "twitter algorithm", "tweet reach", "debug underperforming tweet", "バズるかチェックして", "Xでバズる投稿にして", "伸びるかチェックして", "この投稿を伸ばして", "投稿を改善して", "ツイートを最適化して"

Calculate CVSS scores, interpret vector strings, and prioritize vulnerabilities using CVSS alongside EPSS and CISA KEV for effective risk-based remediation.

Score a clinical ASR manifest against a chosen NIM, produce a five-section KER leaderboard, and route the user via a post-eval decision tree.

Score a UI file's design quality 0-100 against StyleSeed's design language with per-category breakdown, worst offenders, and prioritized fix list.

Vendor privacy risk tiering methodology for processor management. Covers scoring factors including data volume, sensitivity, transfer locations, certifications, breach history, and control maturity with weighted risk calculation and tier assignment.

Structured visual QA verdict for screenshot-to-reference comparisons

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".

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.

Audits trending open-source repos for exploitable vulnerabilities using Semgrep, TruffleHog, osv-scanner, and Slither, then routes findings to responsible disclosure channels (PVR for code flaws, public PRs for dependency CVEs).

Pre-send email quality review and scoring across 5 dimensions (subject line, copy quality, technical/HTML, deliverability, compliance). Analyzes subject lines, body copy, HTML structure, spam triggers, and CAN-SPAM compliance. Scores 0-100 with detailed recommendations for improvement. Use when user wants to review an email before sending, check email quality, or validate marketing email best practices.

Build a multi-factor asset criticality scoring model to weight vulnerability prioritization based on business impact, data sensitivity, and operational importance.

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

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.

Expert en croissance virale (K-factor, network effects, referral loops, embedded virality)

Use to design or audit the support contribution to a customer health score, so the score predicts something instead of averaging weakly-related signals into a colour. Trigger for "build a customer health score", "add support signal to health scoring", "our health scores don't predict churn", red/amber/green account scoring, or a health score nobody trusts.

Structured visual QA verdict for screenshot-to-reference comparisons

Score raw leads as HOT/WARM/COOL based on config-driven weights from agency.config.json

Score Analyzer

Guardian of V.A.I.R.E. quality standards (Value/Agency/Identity/Resilience/Echo). Handles pre-release evaluation, scorecard assessment, and pass/fail determination. Use when UX quality gates are needed. Does not write code.

Scan for viral app opportunities from trends, gaps, and user pain points

Evaluates image captions by converting them into scene graphs and computing an F-score over semantic propositions, measuring how well a generated caption captures the meaning of an image compared to human references.

Operate the VSS alert pipeline for real-time monitoring, Alert-Bridge subscriptions, Slack notifications, incident queries, and camera onboarding.

Calculates CRAP (Change Risk Anti-Patterns) scores for .NET methods by combining cyclomatic complexity with code coverage data from Cobertura XML reports.

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.

Engineer word-of-mouth and virality using the STEPPS framework (Social Currency, Triggers, Emotion, Public, Practical Value, Stories). Use when the user mentions "go viral", "word of mouth", "shareable content", "social currency", "why people share", "viral loop", "referral program", or "organic growth". Also trigger when designing shareable features, crafting social media campaigns, or building products that spread through peer recommendation. Covers environmental triggers and high-arousal emotional content. For sticky messaging, see made-to-stick. For persuasion tactics, see influence-psychology.

Evaluates text-to-image models on spatial relationship accuracy using the VISOR metric, separating object detection from spatial correctness to reveal biases like object priority and merging.

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.

Engineer word-of-mouth and virality using the STEPPS framework (Social Currency, Triggers, Emotion, Public, Practical Value, Stories).

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.

WOLVERINE v2.0 — HYPE alpha hunter. Entry-only scanner, DSL-exit-only architecture. v1.1 lost -22.7% because the scanner's thesis exit chopped 25/27 trades before DSL could manage them. v2.0 removes thesis exit entirely. Scanner decides entries (score 8+, 4H/1H aligned, SM consensus). DSL manages all exits (wide Phase 1 for HYPE volatility, trailing tiers starting at +15% ROE). Leverage lowered to 7x. Max 4 entries/day. 3-hour cooldown between entries. DSL exit managed by plugin runtime via runtime.yaml.

Diagnosing and fixing bimodal matching score distributions in MaxFuse

WordPress Report Card

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.

Daily customer health scoring across the active book of business. Pulls usage signals from PostHog and subscription state from Stripe, scores each active customer per the brand's customer-success playbook, updates the Notion customer DB, and Slack-alerts on at-risk transitions. Runs daily on cron schedule.

Runs bottom-up type-chain validation across database, hooks, and UI layers, then fixes errors at the failing layer for project health checks before large changes.