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"Context engineering methodology for production AI agents from Lance Martin (LangChain) and Yichao "Peak" Ji (Manus)"
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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
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| name | Lance Martin & Yichao "Peak" Ji - Context Engineering |
| description | "Context engineering methodology for production AI agents from Lance Martin (LangChain) and Yichao "Peak" Ji (Manus)" |
Production-grade context engineering methodology for building AI agents that maintain performance across hundreds of tool calls without degradation.
| # | Workflow | Produces | Use When |
|---|---|---|---|
| production | Production Agent Infrastructure Blueprint | A comprehensive architectural specification including action-space layers, sandbox configurations, and security guardrails. | You are designing a new agentic system from scratch or migrating a prototype to a production-grade environment. |
| context | Context Management & Efficiency Engine | A technical implementation plan for reversible compaction, structured summarization, and KV-cache optimization. | The agent is suffering from performance degradation due to long-running sessions or high token costs. |
| performance | Performance Discovery & Optimization Protocol | An evaluation report detailing the pre-rot threshold, model routing logic, and a simplified architecture roadmap. | You need to benchmark agent reliability, select the right models for specific tasks, or simplify a bloated system. |