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The Expert Anchor System - 3-step framework for transforming generic AI outputs into expert-level, personalized execution through systematic knowledge extraction and context synthesis
デフォルトでは、最初にソースを確認する Prompt が選択されています。直接コマンドに切り替えるか、ローカルコピーをダウンロードすることもできます。
インストールを決める前に、SKILL.md と SkillsMP に表示されている付属ファイルをお読みください。
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
直接コマンドでは確認用 Prompt が省略されます。実行前にソースを確認してください。
npx skills add https://github.com/Farrice/mes-knowledge-base --skill futurepedia-prompt-engineeringコマンドは1行のまま表示されます。コピー前に横へスクロールして全体を確認してください。
ローカルで確認しますか？SkillsMP が現在取得できるファイルをダウンロードできます。
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.
SOC 職業分類に基づく
SKILL.md を表示中
| name | Futurepedia Prompt Engineering |
| description | The Expert Anchor System - 3-step framework for transforming generic AI outputs into expert-level, personalized execution through systematic knowledge extraction and context synthesis |
| version | 2.0 |
| format | completion-engine |
| workflows | 6 |
Transform every AI conversation from generic average to expert-level through systematic framework extraction, context capture, and meta-prompt synthesis. Futurepedia reverse-engineered WHY AI gives generic advice (prediction engines find the most likely/average response) and created a 3-step system that forces AI to operate from expert frameworks + personalized context, eliminating the "looks impressive but useless" phenomenon. Core Insight: The problem isn't your prompt words—it's that AI
| # | Workflow | Produces | Use When |
|---|---|---|---|
| expert | Expert Knowledge Synthesis Engine | Validated Expert Research Notebook and Strategic Synthesis Report | You need to transform raw, unverified data into a high-fidelity, expert-grounded knowledge base. |
| multi | Multi-Format Content Factory | Integrated Content Distribution Suite (Slides, Infographics, Video Scripts) | You have a core concept or research base that needs to be deployed across multiple visual and digital channels. |
| interactive | Interactive Learning Ecosystem Design | Adaptive Study System and Persona-Driven Learning Environment | You want to create a personalized educational experience or a specialized internal training system. |
| custom | Custom AI Solution Architect | Deployable Custom GPT/Gem Specification and Use Case Roadmap | You need to build a permanent AI tool or discover high-leverage opportunities for AI automation within a workflow. |
| ecosystem | Recursive Prompt Ecosystem Architect | Justified, folder-structured prompt ecosystem with per-prompt ROI blocks, reasoning-architecture selection, and a plan-mode gate | You need a whole SUITE of deployment-ready prompts (not one), with ROI justification, the right reasoning framework per task, and calibrated parameters. |
| panel | Emotional Expert-Panel Prompt Suite | Layered, expertise-tagged suite of 3-5 complementary prompts forming a coherent emotional journey | The prompt's success depends on emotional resonance and multi-expert layering, and you want a complementary suite (task/open/constraint) rather than a single prompt. |
18 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/futurepedia-prompt-engineering/references/prompts-v2/prompt_01_research_notebook_architecture.mdskills/futurepedia-prompt-engineering/references/prompts-v2/prompt_02_source_validation_protocol.mdskills/futurepedia-prompt-engineering/references/prompts-v2/prompt_03_content_multiplication.mdskills/futurepedia-prompt-engineering/references/prompts-v2/prompt_04_custom_gem_architecture.mdskills/futurepedia-prompt-engineering/references/prompts-v2/prompt_05_creative_use_case_discovery.mdskills/futurepedia-prompt-engineering/references/prompts-v2/prompt_06_audio_video_optimization.mdskills/futurepedia-prompt-engineering/references/prompts-v2/prompt_07_deep_research_synthesis.mdskills/futurepedia-prompt-engineering/references/prompts-v2/prompt_08_infographic_design_mastery.mdskills/futurepedia-prompt-engineering/references/prompts-v2/prompt_08_studio_style_direction_mastery.mdskills/futurepedia-prompt-engineering/references/prompts-v2/prompt_09_dynamic_source_scoping.mdskills/futurepedia-prompt-engineering/references/prompts-v2/prompt_09_strategic_slide_deck.mdskills/futurepedia-prompt-engineering/references/prompts-v2/prompt_10_interactive_audio_learning.mdskills/futurepedia-prompt-engineering/references/prompts-v2/prompt_10_notebook_chat_persona_engineering.mdskills/futurepedia-prompt-engineering/references/prompts-v2/prompt_11_notebook_ecosystem_architecture.mdskills/futurepedia-prompt-engineering/references/prompts-v2/prompt_12_learning_system_design.mdskills/futurepedia-prompt-engineering/references/prompts-v2/prompt_12_study_system_design.mdskills/futurepedia-prompt-engineering/references/prompts-v2/prompt_13_report_type_selection_strategy.mdskills/futurepedia-prompt-engineering/references/prompts-v2/prompt_14_error_detection_quality_assurance.md