a2ui
Make a reply easier to read and act on with rich blocks inside ordinary Markdown — a choice the user picks from, a form that…
Call model APIs through @prismshadow/mmsp (MMSP) — streaming text generation, image generation, speech synthesis, embeddings and the supported-model registry with one client.
Claude Code
$ npx -y skills add Prism-Shadow/penguin-harness --skill unified-llm-api --agent claude-code
Codex
$ npx skills add Prism-Shadow/penguin-harness --skill unified-llm-api -a codex -y
Cursor
$ npx skills add Prism-Shadow/penguin-harness --skill unified-llm-api -a cursor -y
Gemini CLI
$ npx skills add Prism-Shadow/penguin-harness --skill unified-llm-api -a gemini-cli -y
GitHub Copilot
$ npx skills add Prism-Shadow/penguin-harness --skill unified-llm-api -a github-copilot -y
OpenCode
$ npx skills add Prism-Shadow/penguin-harness --skill unified-llm-api -a opencode -y
Windsurf
$ npx skills add Prism-Shadow/penguin-harness --skill unified-llm-api -a windsurf -y
name: unified-llm-api description: Call model APIs through @prismshadow/mmsp (MMSP) — streaming text generation, image generation, speech synthesis, embeddings and the supported-model registry with one client. version: 2026.10.24.1
`@prismshadow/mmsp` — MMSP, the Model Message Stream Protocol, formerly `@prismshadow/agenthub` — is a unified TypeScript client for model APIs: one message format and one streaming grammar for text, image generation, speech synthesis and embeddings, behind one entry point.
npm install @prismshadow/mmsp
The main entry point is `AutoLLMClient`:
import { AutoLLMClient } from "@prismshadow/mmsp";
const client = new AutoLLMClient({ model: "<model_id>", apiKey: "<key>", baseUrl: "<url>", clientType: "<type>" });`apiKey`, `baseUrl` and `clientType` are optional (see [Routing and credentials](#routing-and-credentials)); `defaultHeaders` adds request headers. The package also exports `listSupportedModels` (the model registry), `normalizeLegacyMessages`, the `ThinkingLevel` / `PromptCaching` enums, the message and event types, and the error classes (see [Errors](#errors)).
If the user's message only invokes this skill (e.g. "use unified-llm-api skill") without a concrete task, ask the user what they want to build. Do not write code until the requirement is clear.
**Important prerequisite — set the key up first, then develop.** When the script is an AI app you are building for the user, have them add the model API key in **this agent's key vault** (gear icon on its card, Agents page → settings → key vault tab) *before* you start, so the credential is in your shell environment. If the app stores its own model config, keep its Penguin data root **inside the CWD workspace** (`--root ./penguin_data`), never `~/.penguin`. Model ids can come from the penguin CLI catalog and the id table below.
Check for a usable API key before writing code — the client needs one for whichever provider you target:
env | grep -oE "(DEEPSEEK|OPENAI|ANTHROPIC|GEMINI|ZAI|MOONSHOT|MINIMAX)_API_KEY" || echo none
Vault keys also appear in your Vault Keys section. **Only two sources count as a usable key**: a vault-injected environment variable (the check above), or — when the app stores its own model config — a key already configured in the app's own data root (`penguin config model list --root <data_dir>`). Keys living in the global `~/.penguin` or any other `.penguin` directory do **not** count — a bare `penguin config model list` (no `--root`) reads the global store, because the CLI defaults to the global root unless `--root` is given, so a key showing up there proves nothing for your script and must never be used or copied.
If neither counted source yields a usable key, **stop immediately and ask the user to configure one — do not write code, and do not keep calling tools to retry**: ask them to add one in the agent's **key vault** (gear icon on the agent's card, Agents page → settings → key vault tab); vault values reach your shell environment on the next task. Re-checking the environment or the vault in a loop just wastes turns — one clear check, then hand back to the user.
Keep model API keys **project-local**: for an app that stores its own model config, write the key into the project under the working directory with the penguin CLI, **always passing `--root <data_dir>` for a directory inside the current working directory** (`penguin config model add --root ./penguin_data --provider <group> --model-id <id> --api-key <key>`) — without `--root` it writes to the global `~/.penguin/data` instead. `--provider` is required alongside `--model-id`: a model entry is the `(provider, model_id)` pair and the group is never inferred (use `custom` for an endpoint outside the built-in groups). Otherwise rely on vault-injected environment variables. Never read, copy or fall back to model keys stored in the user's global `~/.penguin` directory — that config belongs to the person running Penguin, not to your script.
Use exact model ids. If an id is not in the table below and the user has not given one, ask the user to confirm the exact id before writing code. Official ids route on their own; every gateway id needs an explicit `clientType` (see [Routing and credentials](#routing-and-credentials)).
| Family | Official IDs | Gateway variants | | ---------------------- | ------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------- | | Gemini 3.8 / 3.7 / 3.6 | `gemini-3.8-flash`, `gemini-3.7-flash`, `gemini-3.6-flash`, `gemini-3.5-flash-lite` | OpenRouter `google/gemini-3.8-flash`, `google/gemini-3.7-flash` | | Gemini 3 | `gemini-3.1-pro-preview`, `gemini-3.5-flash`, `gemini-3.1-flash-lite` | — | | Gemini image | `gemini-3.1-flash-image`, `gemini-3.1-flash-lite-image`, `gemini-3-pro-image` | — | | Gemini TTS | `gemini-3.8-flash-tts`, `gemini-3.8-flash-lite-tts`, `gemini-3.1-flash-tts-preview` | — | | Gemini embedding | `gemini-embedding-2`
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Ask which skill or flow fits your situation. A router over the skills in this repo.