ModLens Image Vision Bridge
liustack/modlens
Gives text-only models sight by running the modlens CLI on an image path or URL and returning structured JSON evidence with transcribed text, layout and semantics.
Guidance for using the MMSP Python SDK (mmsp). An agent skill from Prism-Shadow/model-message-stream-protocol.
$ npx skills add Prism-Shadow/model-message-stream-protocol --skill mmsp-python -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install Prism-Shadow/model-message-stream-protocol mmsp-python --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/Prism-Shadow/model-message-stream-protocol.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/mmsp-python .claude/skills/mmsp-python && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "mmsp-python" agent skill from https://github.com/Prism-Shadow/model-message-stream-protocol/tree/main/skills/mmsp-python into .claude/skills/mmsp-python/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mmsp-python", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/Prism-Shadow/model-message-stream-protocol/tree/main/skills/mmsp-pythonType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add Prism-Shadow/model-message-stream-protocol --skill mmsp-python -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install Prism-Shadow/model-message-stream-protocol mmsp-python --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Prism-Shadow/model-message-stream-protocol.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/mmsp-python .agents/skills/mmsp-python && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "mmsp-python" agent skill from https://github.com/Prism-Shadow/model-message-stream-protocol/tree/main/skills/mmsp-python into .agents/skills/mmsp-python/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mmsp-python", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add Prism-Shadow/model-message-stream-protocol --skill mmsp-python -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install Prism-Shadow/model-message-stream-protocol mmsp-python --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Prism-Shadow/model-message-stream-protocol.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/mmsp-python .cursor/skills/mmsp-python && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "mmsp-python" agent skill from https://github.com/Prism-Shadow/model-message-stream-protocol/tree/main/skills/mmsp-python into .cursor/skills/mmsp-python/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mmsp-python", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/Prism-Shadow/model-message-stream-protocol.git --path skills/mmsp-python--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add Prism-Shadow/model-message-stream-protocol --skill mmsp-python -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install Prism-Shadow/model-message-stream-protocol mmsp-python --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Prism-Shadow/model-message-stream-protocol.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/mmsp-python .gemini/skills/mmsp-python && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "mmsp-python" agent skill from https://github.com/Prism-Shadow/model-message-stream-protocol/tree/main/skills/mmsp-python into .gemini/skills/mmsp-python/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mmsp-python", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install Prism-Shadow/model-message-stream-protocol mmsp-pythonInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add Prism-Shadow/model-message-stream-protocol --skill mmsp-python -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/Prism-Shadow/model-message-stream-protocol.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/mmsp-python .github/skills/mmsp-python && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "mmsp-python" agent skill from https://github.com/Prism-Shadow/model-message-stream-protocol/tree/main/skills/mmsp-python into .github/skills/mmsp-python/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mmsp-python", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add Prism-Shadow/model-message-stream-protocol --skill mmsp-python -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install Prism-Shadow/model-message-stream-protocol mmsp-python --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Prism-Shadow/model-message-stream-protocol.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/mmsp-python .opencode/skills/mmsp-python && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "mmsp-python" agent skill from https://github.com/Prism-Shadow/model-message-stream-protocol/tree/main/skills/mmsp-python into .opencode/skills/mmsp-python/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mmsp-python", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
mmsp-pythonGuidance for using the MMSP Python SDK (mmsp). An agent skill from Prism-Shadow/model-message-stream-protocol.
Mmsp Python is an agent skill from Prism-Shadow/model-message-stream-protocol. Guidance for using the MMSP Python SDK (mmsp). Use when developing agents that call different LLM APIs, need a unified interface for LLM providers, mention MMSP, request mmsp, or already import it.
Its SKILL.md is about 1.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files (for example `reference/api.md`, `reference/data-models.md` and `reference/integrations.md`).
It sits in AI & LLM Engineering, covering LLM API integration. It works with Python, DeepSeek, Google Gemini and OpenAI. The repository describes itself as: One interface for 1,000+ LLMs, with zero-code switching and built-in observability. (GPT-6 / Claude 5 / Gemini 3.8). The licence is Apache-2.0.
Read from SKILL.md and the folder at commit 326dc9b. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Shell commands in SKILL.md call:
uvpipFrom the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md. Its commands use uv and pip, which can reach the network depending on how they are called.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Mmsp Python loads about 1.4k tokens when it runs. Until then it costs about 53 tokens; SKILL.md has 280 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
The full file from Prism-Shadow/model-message-stream-protocol at commit 326dc9b, republished under its Apache-2.0 licence (© Prism-Shadow). 280 words, ~1,370 tokens.
.claude/skills/mmsp-python/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.MMSP is a unified SDK for calling LLMs across providers with shared data models, tool calling, tracing, and playground support.
uv add mmsp
# or
pip install mmspFor model IDs, API keys, and base URLs, see Model selection.
This example asks GPT to call a weather tool, runs the tool, then sends the result back.
import asyncio
from mmsp import AutoLLMClient
def get_weather(location: str) -> str:
return f"Temperature in {location}: 22 C"
# Map tool names to their implementations so calls can be dispatched by name.
TOOLS = {"get_weather": get_weather}
async def main():
weather_function = {
"name": "get_weather",
"description": "Gets the current weather for a given location.",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city name"
}
},
"required": ["location"]
}
}
client = AutoLLMClient(model="gpt-5.5")
config = {"tools": [weather_function]}
tool_call = None
async for event in client.streaming_response_stateful(
message={
"role": "user",
"content_items": [{"type": "text.done", "text": "What's the weather in London?"}]
},
config=config
):
if event["event_type"] == "stop":
# Always the last event, exactly once: the response has finished.
print(event["finish_reason"], event["usage_metadata"])
for item in event["content_items"]:
if item["type"] == "tool_call.done": # the complete call; tool_call.delta items are fragments
tool_call = item
if tool_call:
# Dispatch by tool name instead of hardcoding the function.
result = TOOLS[tool_call["name"]](**tool_call["arguments"])
async for event in client.streaming_response_stateful(
message={
"role": "user",
"content_items": [
{
"type": "tool_result.done",
"text": result,
"tool_call_id": tool_call["tool_call_id"]
}
]
},
config=config
):
print(event)
# Streams the answer as text.delta fragments, closes it with text.done, then one stop event carrying usage:
# {'role': 'assistant', 'event_type': 'delta', 'content_items': [{'type': 'text.delta', 'text': 'The'}], 'usage_metadata': None, 'finish_reason': None}
# {'role': 'assistant', 'event_type': 'delta', 'content_items': [{'type': 'text.delta', 'text': ' weather'}], 'usage_metadata': None, 'finish_reason': None}
# {'role': 'assistant', 'event_type': 'delta', 'content_items': [{'type': 'text.delta', 'text': ' is 22 C.'}], 'usage_metadata': None, 'finish_reason': None}
# {'role': 'assistant', 'event_type': 'delta', 'content_items': [{'type': 'text.done', 'text': 'The weather is 22 C.'}], 'usage_metadata': None, 'finish_reason': None}
# {'role': 'assistant', 'event_type': 'stop', 'content_items': [], 'usage_metadata': {'cached_tokens': 0, 'prompt_tokens': 12, 'thoughts_tokens': 0, 'response_tokens': 8}, 'finish_reason': 'stop'}
asyncio.run(main())Agent loop rules:
tool_call.done items. tool_call.delta items are argument fragments for live display only.tool_call_id from its originating tool_call.done. Do not invent, normalize, or reuse IDs across unrelated tool calls.ToolCallArgumentParseError in place of the tool_call.done. Do not execute the tool from partial arguments; let the agent runtime retry or re-prompt the model.stop event: it is always the last event, arrives exactly once, and always carries both. delta events carry None for both. A thinking-only response raises EmptyResponseError instead of the stop event; its usage_metadata still reports the tokens..done types (text.done, tool_result.done, …). Types without the suffix are still accepted, with a deprecation warning, until 0.6.0.thinking.done and inline_thinking.done items. Do not strip or modify fidelity fields.UniMessage in the messages array produces one embedding vector. Within a single message, all items in content_items are aggregated into a single embedding. Set embedding_config.dimensions in the config to control vector size.UniConfig, UniMessage, UniEvent, the streaming protocol, and errors.© Prism-Shadow, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 4 other files in skills/mmsp-python of Prism-Shadow/model-message-stream-protocol.
Open the folder on GitHubat commit 326dc9b
Mmsp Python next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Mmsp Python this skillPrism-Shadow/model-message-stream-protocol | 113 | — | ~1.4k | Automated safety check: Pass | Apache-2.0 | |
| ModLens Image Vision Bridgeliustack/modlens | 4.1k | — | ~1.3k | Automated safety check: Notes | MIT | |
| Bridgic LLMsbitsky-tech/bridgic | 155 | — | ~839 | Automated safety check: Notes | MIT | |
| Gemini Interactions APIAyuilos/Miffan | 182 | — | ~4.6k | Automated safety check: Pass | AGPL-3.0 | |
| Azure AI Projects Python SDKmicrosoft/skills | 3.1k | 6 repos | ~2.8k | Automated safety check: Pass | MIT | |
| Gemini API Devgoogle-gemini/gemini-skills | 4.3k | — | ~5.1k | Automated safety check: Pass | Apache-2.0 |
liustack/modlens
Gives text-only models sight by running the modlens CLI on an image path or URL and returning structured JSON evidence with transcribed text, layout and semantics.
bitsky-tech/bridgic
LLM provider initialization for bridgic projects. An agent skill from bitsky-tech/bridgic.
Ayuilos/Miffan
A skill your agent uses when writing code that calls the Gemini API for text generation, multi-turn chat, multimodal understanding, image generation, video generation, streaming responses…
microsoft/skills
Reference for building on Microsoft Foundry with the azure-ai-projects Python SDK: project clients, versioned agents, evaluations, connections, datasets and indexes.
google-gemini/gemini-skills
A skill your agent uses when writing code that calls the Gemini API for text generation, multi-turn chat, multimodal understanding, image generation, video generation, speech generation (TTS), voice…
MigoXLab/dingo
A skill your agent uses when the user wants to fact-check an article or verify factual claims in a document.
Prism-Shadow/model-message-stream-protocol
Guidance for using the MMSP TypeScript SDK (@prismshadow/mmsp).
Prism-Shadow/model-message-stream-protocol
Fixed workflow for developing MMSP itself — adding or updating model support, and changing its pages.
Works with
Categories
Guidance for using the MMSP Python SDK (mmsp). An agent skill from Prism-Shadow/model-message-stream-protocol. Mmsp Python is an agent skill from Prism-Shadow/model-message-stream-protocol. Guidance for using the MMSP Python SDK (mmsp).
Mmsp Python fits situations like: developing agents that call different LLM APIs; need a unified interface for LLM providers; already import it.
Run `npx skills add Prism-Shadow/model-message-stream-protocol --skill mmsp-python -a claude-code`. Or copy the skill folder (skills/mmsp-python in Prism-Shadow/model-message-stream-protocol) into .claude/skills/mmsp-python in your project. Claude Code loads it when a task matches its description.
Run `npx skills add Prism-Shadow/model-message-stream-protocol --skill mmsp-python -a codex`. Or copy the skill folder (skills/mmsp-python in Prism-Shadow/model-message-stream-protocol) into .agents/skills/mmsp-python in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add Prism-Shadow/model-message-stream-protocol --skill mmsp-python -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/mmsp-python, .gemini/skills/mmsp-python, .github/skills/mmsp-python and .opencode/skills/mmsp-python in your project.
Going by SKILL.md and its folder, Mmsp Python needs the command-line tools its instructions call (uv and pip). Our summary lists: Python 3.
SKILL.md contains no URLs. Its commands use uv and pip, which can reach the network depending on how they are called. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
Mmsp Python is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 1.4k tokens (SKILL.md is roughly 5.5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Mmsp Python: ModLens Image Vision Bridge (liustack/modlens, 4.1k stars), Bridgic LLMs (bitsky-tech/bridgic, 155 stars), Gemini Interactions API (Ayuilos/Miffan, 182 stars) and Azure AI Projects Python SDK (microsoft/skills, 3.1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Prism-Shadow (a GitHub organization) maintains it in Prism-Shadow/model-message-stream-protocol, which has 113 GitHub stars. The repository holds 3 skills in this directory. The repository was last updated on October 4, 2026.
Source: Prism-Shadow/model-message-stream-protocol on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.