Use when the user wants to learn, be taught, be quizzed, or understand an idea across any domain — science, math, philosophy, code, law, politics, psychology, medicine, arts, or history — with active scaffolding.
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Install, discover, and wire registry blocks and components into HyperFrames compositions. Use when running hyperframes add or hyperframes catalog, installing one item or every block matching a tag, wiring an installed item into index.html, or working with hyperframes.json. Covers discovery, install locations, block sub-composition wiring, component snippet merging, and authoring a new block or component to contribute upstream (idea → scaffold → validate → PR).
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实测评分 未实测
查看安装方法安装前可以查看交给 AI 代理的指令内容。
The registry provides reusable blocks and components installable via hyperframes add <name>.
data-composition-src in a host composition.hyperframes add data-chart # install a block
hyperframes add grain-overlay # install a component
hyperframes add captions # install every block tagged captions
hyperframes add shimmer-sweep --dir . # target a specific project
hyperframes add data-chart --json # machine-readable output
hyperframes add data-chart --no-clipboard # skip clipboard (CI/headless)
After install, the CLI prints which files were written and a snippet to paste into your host composition. The snippet is a starting point — you'll need to add data-composition-id (must match the block's internal composition ID), data-start, and data-track-index attributes when wiring blocks.
The positional value is resolved as an exact item name first. If no item matches and the value is a tag, the command installs every block with that tag. Registry dependencies are installed before the requested item. hyperframes add works only for blocks and components; for examples, use hyperframes init <dir> --example <name> instead.
Blocks install to compositions/<name>.html by default. Components install to compositions/components/<name>.html by default.
These paths are configurable in hyperframes.json:
{
"registry": "https://raw.githubusercontent.com/heygen-com/hyperframes/main/registry",
"paths": {
"blocks": "compositions",
"components": "compositions/components",
"assets": "assets"
}
}
See install-locations.md for full details.
Blocks are standalone compositions — include them via data-composition-src in your host index.html:
<div
data-composition-id="data-chart"
data-composition-src="compositions/data-chart.html"
data-start="2"
data-duration="15"
data-track-index="1"
data-width="1920"
data-height="1080"
></div>
Key attributes:
data-composition-src — path to the block HTML filedata-composition-id — must match the block's internal IDdata-start — when the block appears in the host timeline (seconds)data-duration — how long the block playsdata-width / data-height — block canvas dimensionsdata-track-index — layer ordering (higher = in front)See wiring-blocks.md for full details.
Components are snippets — paste their HTML into your composition's markup, their CSS into your style block, and their JS into your script (if any):
compositions/components/grain-overlay.html)<div data-composition-id="..."><style> block into your composition's styles<script> content into your composition's script (before your timeline code)See wiring-components.md for full details.
Use the CLI as the primary discovery surface. Search by intent before browsing: the registry holds more items than you can scan by eye, so listing them and matching on names or tags is the slow path, and it fails whenever the author's wording differs from yours.
# Rank the whole catalog against what the beat should do
npx hyperframes catalog --query "reveal a headline one line at a time"
npx hyperframes add caption-clip-wipe
Search is local and sends nothing. By default it ranks on vocabulary shared with the item's name, title and description, so it only finds items that reuse your words; --on-device ranks by meaning instead, after a one-time model download. With --json the envelope names which tier answered, so check that rather than assuming a ranking happened.
Installability is applied after ranking, not before it: a name the vectors carry but this registry cannot serve is dropped from the results and counted in dropped, so a non-zero dropped means the two are different generations. See /hyperframes-cli for the offline tier, the consent gates, and how to refresh a stale index.
To browse or filter instead of search:
npx hyperframes catalog
npx hyperframes catalog --type block
npx hyperframes catalog --type component
npx hyperframes catalog --type block --tag social
npx hyperframes catalog --json
npx hyperframes catalog --human-friendly
The normal table and --json modes only list matches; install a selected name with hyperframes add <name>. --human-friendly opens an interactive picker and installs the selected item immediately. In CI or agent workflows, prefer --json followed by an explicit add.
When the search comes back and nothing in it does the job, say so before you hand-author the move:
npx hyperframes feedback --search-miss "<the query you ran>" --wanted "<the move you needed>" --tier on-device
catalog --query prints this line for you, pre-filled, and --json carries it as report_gap — so it is already in hand at the moment you decide nothing fits.
Report whenever nothing in the results does the job, on either tier. Do not wait for the on-device tier to have answered: it needs a consented 33 MB download, so an agent run is on words unless it explicitly opted in, and gating on on-device would silence almost every report. The --tier value rides along so a vocabulary miss stays distinguishable from a meaning miss when these are read. Describe the effect you wanted, not the item name you imagined: what comes back is a list of moves worth building, and a report naming a non-existent item teaches nothing. This is the only path that sends a query anywhere, which is exactly why it is a separate deliberate command rather than something the search does on its own. It carries no rating and never lands in the rating metric.
This is the whole demand signal for the catalog. Skipping it means the gap you hit gets guessed at from install counts instead, which cannot see a move nobody could install.
If the CLI cannot reach the configured registry, inspect the raw manifest as a fallback:
curl -s https://raw.githubusercontent.com/heygen-com/hyperframes/main/registry/registry.json
A registry the CLI cannot reach does not empty the catalog for discovery: a previously fetched manifest keeps serving past its 24h refresh window whenever revalidation fails, so catalog and catalog --query still list and rank against the last copy on disk.
add still needs the network, even for an item you installed yesterday. Only manifests are cached; the item's actual files are fetched on every install. So offline you can search, and you can see what an item is, but installing it fails at the file fetch. Do not promise a user an offline install.
Each item's registry-item.json contains: name, type, title, description, tags, dimensions (blocks only), duration (blocks only), and file list.
See discovery.md for details on filtering by type and tags.
To author a NEW registry item (caption style, VFX block, transition, lower third, or a reusable component) and ship it as an upstream PR — not install an existing one — follow the full idea → scaffold → build → validate → preview → ship workflow in contributing.md. Copy-paste starter templates (caption / VFX / component / registry-item.json) are in templates.md.
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Use when the user wants to learn, be taught, be quizzed, or understand an idea across any domain — science, math, philosophy, code, law, politics, psychology, medicine, arts, or history — with active scaffolding.
中文简介正在准备中。当前显示原文说明。
This skill helps with using the @ax-llm/ax TypeScript library for building LLM applications. Use when the user asks about ax(), ai(), f(), s(), agent(), flow(), AxGen, AxAgent, AxFlow, signatures, streaming, or mentions @ax-llm/ax.
中文简介正在准备中。当前显示原文说明。
This skill helps an LLM generate correct AxAgent code using @ax-llm/ax. Use when the user asks about agent(), child agents, namespaced functions, discovery mode, shared fields, llmQuery(...), RLM code execution, recursionOptions, or agent runtime behavior. For tuning and eval with agent.optimize(...), use ax-agent-optimize.
中文简介正在准备中。当前显示原文说明。
This skill helps an LLM generate correct AxAgent tuning and evaluation code using @ax-llm/ax. Use when the user asks about agent.optimize(...), judgeOptions, eval datasets, optimization targets, saved optimizedProgram artifacts, or recursive optimization guidance.
中文简介正在准备中。当前显示原文说明。
This skill helps an LLM generate correct AI provider setup and configuration code using @ax-llm/ax. Use when the user asks about ai(), providers, models, presets, embeddings, extended thinking, context caching, or mentions OpenAI/Anthropic/Google/Azure/Groq/DeepSeek/Mistral/Cohere/Together/Ollama/HuggingFace/Reka/OpenRouter with @ax-llm/ax.
中文简介正在准备中。当前显示原文说明。
This skill helps an LLM generate correct AxFlow workflow code using @ax-llm/ax. Use when the user asks about flow(), AxFlow, workflow orchestration, parallel execution, DAG workflows, conditional routing, map/reduce patterns, or multi-node AI pipelines.
中文简介正在准备中。当前显示原文说明。