菜单
Systematic NBA player prop analysis using behavioral finance principles and statistical edge detection. Research-driven predictions with honest confidence scoring, correlation-aware parlay construction, and Kelly criterion bankroll discipline.
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/Farrice/mes-knowledge-base --skill nba-betting-edge-player-prop-parlay-prediction-system命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
正在显示 SKILL.md
The /generate front door — in-house, pay-as-you-go creative generation (Higgsfield replacement). One command for image, video, and audio: routes each request to the right model recipe (skills/generate/models/*.json), defers to binding creative_router lanes (people → fal-people, style-family → fantastic-posters; Higgsfield fully retired 2026-08-06), quotes paid video before running, honors prompt-level budgets ("total budget $3"), runs comparison batches across models, logs every prompt beside its asset, and auto-refreshes the Asset Command Center (/assets-board). Use when the user says "generate", "make an image/video/voiceover", "create ads/assets/creative", names a model (recraft, kling, seedance, nano banana, gpt image), or asks for a multi-model comparison. Source: RoboNuggets 9C4TRbucmhQ, rebuilt on our fal-first stack.
The Edit Bay — in-house conversational video studio (VOX-style explainers, documentaries, shorts, LinkedIn video). Transcript-driven cutting, B-roll ladder, motion graphics as code, captions, music, publish-ready packages — all agent-operated, free-first. Use when Farrice says "edit this video/footage", "make an explainer/documentary/short", "cut this recording", "add B-roll/captions/music", "turn this essay/post into a video", or any video production ask. Front door for three modes: talking-head, VO-only, zero-camera (his recorded VO + assembled visuals — NO voice clone, NO TTS narration, his ruling 2026-08-06). Source: Brad Bonanno agentic edit pipeline (extractions/brad-bonanno-edit-bay/), rebuilt on the house stack: WhisperX local + edit_bay.py ffmpeg assembly + HyperFrames/Remotion graphics + /generate (fal) — NO Higgsfield (retired 2026-08-06).
Architects technical explainer videos using Brad Bonanno's structural patterns — survives short attention spans, communicates complete systems (not just teasers), and functions as a channel-building artifact (one video pulls subscribers, not just views). Use when planning a long-form technical explainer (5-20 min) for YouTube, structuring an explanation of a complex system the user knows but can't teach yet, balancing depth vs accessibility for a technical audience, or building an evergreen video meant to compound subscriber growth over years rather than spike then die. Trigger proactively whenever the user says "explainer video", "technical explainer", "how do I explain X", "long-form YouTube", "evergreen video", or has a complex system that needs to land with a non-expert. For Instagram-format shareworthy content use brock-johnson-shareworthy-content; for short-form video patterns use a TikTok/Reels-specific skill.
基于 SOC 职业分类
| name | NBA Betting Edge: Player Prop & Parlay Prediction System |
| description | Systematic NBA player prop analysis using behavioral finance principles and statistical edge detection. Research-driven predictions with honest confidence scoring, correlation-aware parlay construction, and Kelly criterion bankroll discipline. |
| version | 2.0 |
| format | completion-engine |
| workflows | 3 |
A research-driven prediction system that finds exploitable edges in NBA player prop betting markets. Built on Jim O'Shaughnessy's behavioral finance framework — the core insight is that betting markets, like financial markets, are distorted by predictable human biases (recency bias, name recognition, emotional betting). The system identifies where posted lines diverge from statistical reality, scores confidence based on contextual alignment, and constructs correlation-aware parlays with disciplined bankroll sizing.
Core Genius: Edge detection through the Context Stack (5-variable analysis) + Recency Bias Arbitrage (weighted averaging that exploits the market's overreaction to recent games) + behavioral bias auditing (Four Horsemen Defense adapted for sports betting).
Honest caveat: This is a research-informed decision tool, not a guaranteed edge. Sports betting markets are efficient. The system helps make better decisions — it does not promise profitability.
| # | Workflow | Produces | Use When |
|---|---|---|---|
| research | Game Day Research & Picks | Pick slate with confidence scores, parlay suggestions, and bankroll sizing for tonight's NBA games | Running daily analysis before game time — pull stats, detect edges, build picks |
| review | Performance Review & Calibration | Win/loss report with ROI calculation, confidence calibration analysis, and pattern effectiveness review | After results are in — log outcomes, calibrate the confidence model, track what's working |
| bankroll | Bankroll Strategy & Position Sizing | Bankroll setup, Kelly criterion sizing guide, exposure limits, and drawdown protocols | Setting up initial bankroll, reviewing position sizing strategy, or after a significant drawdown |
.agent/workflows/betting-edge.md — invoke via /betting-edgeexecution/bet_tracker.py — CLI for logging bets and results.agent/bet-tracking.json — prediction history and bankroll3 deterministic practitioner prompts — each carries an Output Contract, Output Skeleton, and Quality Gate. When a deliverable matches one, Read it and honor its contract instead of improvising the output shape.
skills/nba-betting-edge/references/prompts-v2/bankroll-strategy-document.mdskills/nba-betting-edge/references/prompts-v2/game-day-pick-slate.mdskills/nba-betting-edge/references/prompts-v2/performance-review-report.md