kunpeng-skill

Kunpeng Skill

A local-first, multi-source distillation Skill that runs inside mainstream AI agent environments such as Codex, Claude Code, WorkBuddy, OpenCode, and Hermes. Install it in the agent you already use, then invoke it with natural-language requests to turn real source material into reusable methods and execution-ready specifications.

Give the host agent a repository, website, product, UI, image collection, video, audio recording, article, document, book, course, or mixed source set, and describe what you want distilled. Kunpeng guides the agent to extract transferable mechanisms, technical decisions, design principles, interaction patterns, writing methods, and specifications that the same or another agent can apply.

The outputs can be saved in a local knowledge library and reused in future websites, apps, mini apps, games, agents, desktop products, brand systems, and content projects. Kunpeng is intended for vibe coders, AI-native builders, researchers, designers, developers, and product teams that want their agents to extract reusable knowledge from real sources instead of starting every task from zero.

Kunpeng does not train, fine-tune, or modify the weights of a model. It is a workflow used by the host agent: the host supplies semantic understanding, judgment, and creation capabilities, while Kunpeng supplies the distillation process, domain playbooks, local evidence tools, data contracts, library tools, and quality gates.

Common Problems Kunpeng Solves

I found a beautiful website. How do I build something with the same level of polish?

If the host can access the live site, Kunpeng guides it to inspect real pages, responsive layouts, task flows, interaction states, motion, and visible assets; source code can also be included when available. It turns that evidence into an implementation-ready UI and interaction specification covering structure, components, states, design rules, motion, and acceptance checks, without cloning the original brand identity.

This video looks high-end and polished. How was it made, and how can I generate something comparable?

Kunpeng breaks down the full timeline, narrative, shot design, camera and subject movement, edit rhythm, continuity, color and light, subtitles, effects, narration, music, and sound. It does not pretend to recover an unknown model or original prompt; it produces a model-independent, shot-by-shot production and generation package that can be adapted to the tools available in the host agent. One video yields a production recipe, while multiple independent videos can support a creator profile.

This app feels effortless to use. How do I turn that experience into a design I can build?

With access to the product, the host agent follows representative user paths and records the state before an action, the action itself, the transition, and the resulting state. Kunpeng converts those observations into information architecture, task flows, state machines, feedback and recovery rules, responsive behavior, motion guidance, and concrete acceptance steps for a new product.

I found an excellent open-source project. Where should I start, and what is actually worth reusing?

Kunpeng inventories the repository without executing untrusted target code, then guides the host through real entry points, call chains, data flow, dependencies, tests, and failure paths. It separates implemented behavior from documentation claims and turns useful architecture, engineering patterns, technology trade-offs, and product ideas into a reusable project record or an implementation plan, instead of copying the original stack blindly.

I like this visual style. Why do prompts such as "premium" or "minimal" fail to reproduce it consistently?

Kunpeng combines measurable image evidence with the host agent's visual review to unpack composition, grid, hierarchy, typography, color roles, light, material, imagery, and cross-format behavior, plus motion when the source set includes video or interactive states. The result is a visual system with concrete rules, suggested parameters, design tokens, do/don't guidance, and generation criteria. A single image produces an image recipe; stable brand or creator patterns require multiple independent samples.

I like how this author writes or this course teaches. How can I use the method on a new topic?

Kunpeng extracts argument structure, narrative distance, pacing, rhetoric, teaching order, concept dependencies, examples, exercises, and applicability boundaries from multiple texts or lessons. It turns those mechanisms into a writing, knowledge, or teaching profile for the new topic while keeping the source's facts, long passages, signature expressions, and stories out of the result.

I have a folder full of great references. How do I turn it into my own product instead of another archive?

Kunpeng can turn repositories, products, screenshots, videos, and documents into reusable records and reviewed profiles, then index those outputs alongside existing profiles and retrieve what is most relevant to a new goal. The host agent uses that material to produce a product brief and a product, visual, technology, implementation, or production plan; when it also creates a candidate, Kunpeng can re-analyze and evaluate the result against the distilled rules.

Four Ways to Use Kunpeng

source material -> agent inspection + local evidence -> reusable methods and profiles
                -> local knowledge library          -> new product or content
                                                     -> re-analysis and evaluation

One installation covers collection, distillation, retrieval, application, and evaluation. Kunpeng's compact SKILL.md directs the host agent to load only the domain guidance and scripts needed for the current request.

What You Can Distill