source-command-nba-betting

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.

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Repositorio: Farrice/mes-knowledge-base
Última actividad en el origen: 11 de junio de 2026 a las 23:28
Idioma detectado de SKILL.md: inglés
Estrellas: 0
Forks: 1

Opciones de instalación

De forma predeterminada está seleccionado el prompt que primero revisa el origen. Puedes cambiar a un comando directo o descargar una copia local.

Instalar con Codex o Claude Copia este prompt, pégalo en Codex, Claude u otro asistente, y deja que revise la página de la skill y la instale por ti.

Un comando directo omite el prompt de revisión. Revisa el origen antes de ejecutarlo.

npx skills add https://github.com/Farrice/mes-knowledge-base --skill source-command-nba-betting

El comando permanece en una sola línea. Desplázate horizontalmente para revisarlo antes de copiarlo.

¿Prefieres una copia local? Descarga los archivos que SkillsMP tiene disponibles ahora.

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Lee SKILL.md y los archivos complementarios que muestra SkillsMP antes de decidir si quieres instalarlo.

SKILL.md

Instrucciones de origen · Vista previa de solo lectura

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generate

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.

2026-08-06

video-studio

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).

2026-08-06

brad-bonanno-explainer-video-architecture

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.

2026-08-06

Ver todos los de este repositorio

name	source-command-nba-betting
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.

source-command-nba-betting

Use this skill when the user asks to run the migrated source command nba-betting.

Command Template

Load and embody the skill at skills/nba-betting-edge/SKILL.md. Also load skills/nba-betting-edge/genius.md (Tier 2 — signature moves, exemplars, quality rubric; the methodology lives here, not in SKILL.md). Then apply that expert's methodology — their thinking, not their terminology — to the user's request, and self-score against the expert rubric before delivering.

This skill has runnable processes. Its flagship workflow is skills/nba-betting-edge/workflows/game-day-research-picks.md. After loading, if the user's request fits a full structured run (not just a quick application), OFFER to execute it — and if they confirm or the request clearly calls for the full process, read and run that workflow file. See the skill's 'Available Workflows' table for the other processes.

source-command-nba-betting

Datos de origen

Opciones de instalación

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Más de este repositorio

Más de este repositorio

source-command-nba-betting

Command Template

source-command-nba-betting

Command Template