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CLIProxyAPI + NewAPI Metering Stack · majiayu000 | DecimalAI

DevOpsby majiayu000

CLIProxyAPI + NewAPI Metering Stack

在已经过独立验证的 CLIProxyAPI upstream 之上部署 NewAPI 计费层，把 Codex/Claude/Gemini/Qwen 等订阅账号包装成可计费的 OpenAI 兼容 API。本 Skill 不负责新建裸 CLIProxyAPI；负责 NewAPI Docker 部署、容器到宿主桥接、模型计费倍率、参数化额度修正、多账号 OAuth 凭据热加载和双路径验证。当用户说“给现有 cliproxy 加 NewAPI”“配置 NewAPI 渠道接已运行的 cliproxy”“NewAPI 价格不对”“给现有部署加账号”“172.17.0.1 容器网络”或“408 冷却放大故障”时触发。

Never made a case worseMaintained

SkillScore

SkillScore64/100

Test results 64

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

18%64%of test cases passed +45 pts

Without skill4 of 22

With skill14 of 22

10 fixed4 passed either way8 still failing0 broke

Measured on gemini-3.6-flash8/24/2026v1 · latest is v219 comparable

This benchmark measured an earlier version of the skill.

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskAfter copying new provider authorization JSON files into ~/.cli-proxy-api/ on the VPS, is a service restart required for CLIProxyAPI?

Without the skillMissed

With the skillPassed

Same model, same task. Answers are capped and redacted.

Use this skill

Your agent runs on

Adds every file the skill has, pinned so you can update it. No account needed.

pip install "decimalai>=0.15"
decimalai skills add majiayu000-cliproxy-newapi-stack
claude --plugin-dir "$(git rev-parse --show-toplevel 2>/dev/null || pwd)/.agents"

The same install from Python, for a notebook or a setup script.

Install

pip install "decimalai>=0.15"

Add the skill

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("majiayu000-cliproxy-newapi-stack")
if result.exit_code:
    raise SystemExit(result.problems)

print("Added. Start Claude Code with the command below.")

Start Claude Code with it

claude --plugin-dir "$(git rev-parse --show-toplevel 2>/dev/null || pwd)/.agents"

Type these in a Claude Code session. No account or API key. It writes the skill and any files it bundles, and asks first when its safety rating isn't Passed.

Add the DecimalAI plugin, once

/plugin marketplace add decimal-labs/decimalai-claude-plugins
/plugin install decimalai@decimalai

Install this skill

/decimalai:install majiayu000-cliproxy-newapi-stack

The plugin needs python3 and curl on your machine.

No Python or account. Claude Code picks it up in its next session.

Save it into your project

curl -fsSL --create-dirs -o .claude/skills/majiayu000-cliproxy-newapi-stack/SKILL.md https://app.decimal.ai/s/majiayu000-cliproxy-newapi-stack/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/majiayu000-cliproxy-newapi-stack/SKILL.md

This gets SKILL.md only. The skill's 9 other files are not included.

Other ways to add it

Claude Code docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[claude-agent-sdk]"
decimalai skills add majiayu000-cliproxy-newapi-stack

Add it to your agent

import asyncio

from claude_agent_sdk import ClaudeAgentOptions
from decimalai.claude_agent_sdk import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

async def main():
    question = "Hello!"
    async with support.run(question) as run:  # one user message, one run
        async for message in run.query(options=ClaudeAgentOptions(max_turns=6)):
            print(message)

# In a notebook or async code: await main()
asyncio.run(main())

Prefer its own loader? ClaudeAgentOptions(plugins=[skills_plugin()], skills="all") reads the same folder. Use one or the other: a skill delivered both ways reaches the prompt twice.

The same install from Python, for a notebook or a setup script. agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[claude-agent-sdk]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("majiayu000-cliproxy-newapi-stack")
if result.exit_code:
    raise SystemExit(result.problems)

import asyncio

from claude_agent_sdk import ClaudeAgentOptions
from decimalai.claude_agent_sdk import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

async def main():
    question = "Hello!"
    async with support.run(question) as run:  # one user message, one run
        async for message in run.query(options=ClaudeAgentOptions(max_turns=6)):
            print(message)

# In a notebook or async code: await main()
asyncio.run(main())

Prefer its own loader? ClaudeAgentOptions(plugins=[skills_plugin()], skills="all") reads the same folder. Use one or the other: a skill delivered both ways reaches the prompt twice.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/majiayu000-cliproxy-newapi-stack/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from claude_agent_sdk import ClaudeAgentOptions, query

skill = Path(".agents/skills/majiayu000-cliproxy-newapi-stack/SKILL.md").read_text()
options = ClaudeAgentOptions(system_prompt=f"You are a helpful agent.\n\n{skill}", max_turns=6)

async def main():
    async for message in query(prompt="Hello!", options=options):
        print(message)

# In a notebook or async code: await main()
asyncio.run(main())

This gets SKILL.md only. The skill's 9 other files are not included.

Other ways to add it

Claude Agent SDK docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[openai-agents]"
decimalai skills add majiayu000-cliproxy-newapi-stack

Add it to your agent

from agents import Agent, Runner
from decimalai.openai_agents import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = Agent(
    name=support.name,
    instructions=support.instructions,  # your prompt, then the skill list
    tools=[*support.tools],             # your tools, then load_skill
)
# In a notebook or async code: result = await Runner.run(agent, "Hello!")
result = Runner.run_sync(agent, "Hello!")

Using SandboxAgent? capabilities=[Shell(), decimalai.openai_agents.sandbox_skills()] points its own loader at the same folder.

Install

pip install "decimalai[openai-agents]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("majiayu000-cliproxy-newapi-stack")
if result.exit_code:
    raise SystemExit(result.problems)

from agents import Agent, Runner
from decimalai.openai_agents import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = Agent(
    name=support.name,
    instructions=support.instructions,  # your prompt, then the skill list
    tools=[*support.tools],             # your tools, then load_skill
)
# In a notebook or async code: result = await Runner.run(agent, "Hello!")
result = Runner.run_sync(agent, "Hello!")

Using SandboxAgent? capabilities=[Shell(), decimalai.openai_agents.sandbox_skills()] points its own loader at the same folder.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/majiayu000-cliproxy-newapi-stack/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/majiayu000-cliproxy-newapi-stack/SKILL.md").read_text()
agent = Agent(name="support", instructions=f"You are a helpful agent.\n\n{skill}")
# In a notebook or async code: result = await Runner.run(agent, "Hello!")
result = Runner.run_sync(agent, "Hello!")

This gets SKILL.md only. The skill's 9 other files are not included.

Other ways to add it

OpenAI Agents docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[openai]"
decimalai skills add majiayu000-cliproxy-newapi-stack

Add it to your agent

import json

from openai import OpenAI
from decimalai.openai import agent_config

client = OpenAI()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")


def lookup_order(order_id: str) -> str:
    return f"Order {order_id} shipped yesterday."


# Your tools, as Chat Completions takes them, and what answers each.
TOOLS = [{"type": "function", "function": {
    "name": "lookup_order",
    "description": "Look up the status of an order.",
    "parameters": {"type": "object", "properties": {"order_id": {"type": "string"}},
                   "required": ["order_id"]},
}}]
FUNCTIONS = {"lookup_order": lookup_order}

question = "Where is order A1001?"
with support.run(question) as run:  # one user message, one run
    # run.system is your prompt, then the skill list.
    messages = [{"role": "system", "content": run.system}, run.message]
    while True:
        reply = client.chat.completions.create(
            model="gpt-5.4-mini",
            messages=messages,
            tools=[*TOOLS, *run.tools],  # your tools, then load_skill
        ).choices[0].message
        messages.append(reply)
        if not reply.tool_calls:
            break
        for call in reply.tool_calls:
            # load_skill is answered by the run; your own tools by FUNCTIONS.
            function = FUNCTIONS.get(call.function.name)
            messages.append(run.tool_result(call) or {
                "role": "tool", "tool_call_id": call.id,
                "content": function(**json.loads(call.function.arguments)) if function
                else f"There is no tool named {call.function.name!r}.",
            })
print(reply.content)

# The Responses API takes run.response_tools instead of run.tools.

Install

pip install "decimalai[openai]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("majiayu000-cliproxy-newapi-stack")
if result.exit_code:
    raise SystemExit(result.problems)

import json

from openai import OpenAI
from decimalai.openai import agent_config

client = OpenAI()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")


def lookup_order(order_id: str) -> str:
    return f"Order {order_id} shipped yesterday."


# Your tools, as Chat Completions takes them, and what answers each.
TOOLS = [{"type": "function", "function": {
    "name": "lookup_order",
    "description": "Look up the status of an order.",
    "parameters": {"type": "object", "properties": {"order_id": {"type": "string"}},
                   "required": ["order_id"]},
}}]
FUNCTIONS = {"lookup_order": lookup_order}

question = "Where is order A1001?"
with support.run(question) as run:  # one user message, one run
    # run.system is your prompt, then the skill list.
    messages = [{"role": "system", "content": run.system}, run.message]
    while True:
        reply = client.chat.completions.create(
            model="gpt-5.4-mini",
            messages=messages,
            tools=[*TOOLS, *run.tools],  # your tools, then load_skill
        ).choices[0].message
        messages.append(reply)
        if not reply.tool_calls:
            break
        for call in reply.tool_calls:
            # load_skill is answered by the run; your own tools by FUNCTIONS.
            function = FUNCTIONS.get(call.function.name)
            messages.append(run.tool_result(call) or {
                "role": "tool", "tool_call_id": call.id,
                "content": function(**json.loads(call.function.arguments)) if function
                else f"There is no tool named {call.function.name!r}.",
            })
print(reply.content)

# The Responses API takes run.response_tools instead of run.tools.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/majiayu000-cliproxy-newapi-stack/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/majiayu000-cliproxy-newapi-stack/SKILL.md").read_text()
reply = client.chat.completions.create(
    model="gpt-5.4-mini",
    messages=[
        {"role": "system", "content": f"You are a helpful agent.\n\n{skill}"},
        {"role": "user", "content": "Hello!"},
    ],
).choices[0].message

This gets SKILL.md only. The skill's 9 other files are not included.

Other ways to add it

OpenAI SDK docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[anthropic]"
decimalai skills add majiayu000-cliproxy-newapi-stack

Add it to your agent

import anthropic
from decimalai.anthropic import agent_config

client = anthropic.Anthropic()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

question = "Hello!"
with support.run(question) as run:  # one user message, one run
    answer = client.beta.messages.tool_runner(
        model="claude-haiku-5-5",
        max_tokens=4096,
        system=run.system,     # your prompt, then the skill list
        tools=[*run.tools],    # your tools, then load_skill
        messages=[{"role": "user", "content": question}],
    ).until_done()

Install

pip install "decimalai[anthropic]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("majiayu000-cliproxy-newapi-stack")
if result.exit_code:
    raise SystemExit(result.problems)

import anthropic
from decimalai.anthropic import agent_config

client = anthropic.Anthropic()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

question = "Hello!"
with support.run(question) as run:  # one user message, one run
    answer = client.beta.messages.tool_runner(
        model="claude-haiku-5-5",
        max_tokens=4096,
        system=run.system,     # your prompt, then the skill list
        tools=[*run.tools],    # your tools, then load_skill
        messages=[{"role": "user", "content": question}],
    ).until_done()

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/majiayu000-cliproxy-newapi-stack/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/majiayu000-cliproxy-newapi-stack/SKILL.md").read_text()
reply = client.messages.create(
    model="claude-haiku-5-5",
    max_tokens=4096,
    system=f"You are a helpful agent.\n\n{skill}",
    messages=[{"role": "user", "content": "Hello!"}],
)

This gets SKILL.md only. The skill's 9 other files are not included.

Other ways to add it

Anthropic docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[google-genai]"
decimalai skills add majiayu000-cliproxy-newapi-stack

Add it to your agent

from google import genai
from google.genai import types
from decimalai.google_genai import agent_config

client = genai.Client()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")


def lookup_order(order_id: str) -> str:
    return f"Order {order_id} shipped yesterday."


# Your functions, as declarations for the model, and what answers each.
TOOLS = [{"function_declarations": [{
    "name": "lookup_order",
    "description": "Look up the status of an order.",
    "parameters_json_schema": {"type": "object", "properties": {"order_id": {"type": "string"}},
                               "required": ["order_id"]},
}]}]
FUNCTIONS = {"lookup_order": lookup_order}

question = "Where is order A1001?"
with support.run(question) as run:  # one user message, one run
    config = types.GenerateContentConfig(
        system_instruction=run.system,     # your prompt, then the skill list
        tools=[*TOOLS, *run.tool_params],  # your functions, then load_skill
    )
    contents = [{"role": "user", "parts": [{"text": run.message}]}]
    while True:
        response = client.models.generate_content(
            model="gemini-3.5-flash", contents=contents, config=config,
        )
        if not response.function_calls:
            break
        contents.append(response.candidates[0].content)
        parts = []
        for call in response.function_calls:
            # load_skill is answered by the run; your own functions by FUNCTIONS.
            # A loop of your own rather than automatic function calling, which
            # raises KeyError when the model asks for a function you did not pass.
            function = FUNCTIONS.get(call.name)
            parts.append(run.tool_result(call) or {"function_response": {
                "name": call.name, "id": call.id,
                "response": {"result": function(**(call.args or {})) if function
                             else f"There is no function named {call.name!r}."},
            }})
        contents.append({"role": "user", "parts": parts})
print(response.text)

Install

pip install "decimalai[google-genai]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("majiayu000-cliproxy-newapi-stack")
if result.exit_code:
    raise SystemExit(result.problems)

from google import genai
from google.genai import types
from decimalai.google_genai import agent_config

client = genai.Client()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")


def lookup_order(order_id: str) -> str:
    return f"Order {order_id} shipped yesterday."


# Your functions, as declarations for the model, and what answers each.
TOOLS = [{"function_declarations": [{
    "name": "lookup_order",
    "description": "Look up the status of an order.",
    "parameters_json_schema": {"type": "object", "properties": {"order_id": {"type": "string"}},
                               "required": ["order_id"]},
}]}]
FUNCTIONS = {"lookup_order": lookup_order}

question = "Where is order A1001?"
with support.run(question) as run:  # one user message, one run
    config = types.GenerateContentConfig(
        system_instruction=run.system,     # your prompt, then the skill list
        tools=[*TOOLS, *run.tool_params],  # your functions, then load_skill
    )
    contents = [{"role": "user", "parts": [{"text": run.message}]}]
    while True:
        response = client.models.generate_content(
            model="gemini-3.5-flash", contents=contents, config=config,
        )
        if not response.function_calls:
            break
        contents.append(response.candidates[0].content)
        parts = []
        for call in response.function_calls:
            # load_skill is answered by the run; your own functions by FUNCTIONS.
            # A loop of your own rather than automatic function calling, which
            # raises KeyError when the model asks for a function you did not pass.
            function = FUNCTIONS.get(call.name)
            parts.append(run.tool_result(call) or {"function_response": {
                "name": call.name, "id": call.id,
                "response": {"result": function(**(call.args or {})) if function
                             else f"There is no function named {call.name!r}."},
            }})
        contents.append({"role": "user", "parts": parts})
print(response.text)

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/majiayu000-cliproxy-newapi-stack/SKILL.md

Give it to your agent

from pathlib import Path
from google import genai
from google.genai import types

client = genai.Client()
skill = Path(".agents/skills/majiayu000-cliproxy-newapi-stack/SKILL.md").read_text()
response = client.models.generate_content(
    model="gemini-3.5-flash",
    contents="Hello!",
    config=types.GenerateContentConfig(system_instruction=f"You are a helpful agent.\n\n{skill}"),
)

This gets SKILL.md only. The skill's 9 other files are not included.

Other ways to add it

Gemini API docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[langchain]" langchain-openai
decimalai skills add majiayu000-cliproxy-newapi-stack

Add it to your agent

from langchain.agents import create_agent
from decimalai.langchain import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = create_agent(
    "openai:gpt-5.4-mini",
    tools=[],                         # your tools; the middleware adds load_skill
    middleware=[support.middleware],  # your prompt, the skill list and load_skill
)
result = agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

Install

pip install "decimalai[langchain]" langchain-openai

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("majiayu000-cliproxy-newapi-stack")
if result.exit_code:
    raise SystemExit(result.problems)

from langchain.agents import create_agent
from decimalai.langchain import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = create_agent(
    "openai:gpt-5.4-mini",
    tools=[],                         # your tools; the middleware adds load_skill
    middleware=[support.middleware],  # your prompt, the skill list and load_skill
)
result = agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/majiayu000-cliproxy-newapi-stack/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/majiayu000-cliproxy-newapi-stack/SKILL.md").read_text()
agent = create_agent("openai:gpt-5.4-mini", tools=[], system_prompt=f"You are a helpful agent.\n\n{skill}")
result = agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

This gets SKILL.md only. The skill's 9 other files are not included.

Other ways to add it

LangChain docs

CrewAI's own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" "crewai>=1.15.27"
decimalai skills add majiayu000-cliproxy-newapi-stack

Add it to your agent

import decimalai.crewai
from crewai import Agent, Crew, Task

agent = Agent(
    role="Support", goal="Help the customer", backstory="A support agent.",
    llm=llm,                               # your own CrewAI LLM
    skills=decimalai.crewai.skills(),      # the installed folder
    tools=decimalai.crewai.skill_tools(),  # reads a skill's files, runs its scripts
)
task = Task(description="Hello!", expected_output="an answer", agent=agent)
Crew(agents=[agent], tasks=[task]).kickoff()

The same install from Python, for a notebook or a setup script. CrewAI's own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" "crewai>=1.15.27"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("majiayu000-cliproxy-newapi-stack")
if result.exit_code:
    raise SystemExit(result.problems)

import decimalai.crewai
from crewai import Agent, Crew, Task

agent = Agent(
    role="Support", goal="Help the customer", backstory="A support agent.",
    llm=llm,                               # your own CrewAI LLM
    skills=decimalai.crewai.skills(),      # the installed folder
    tools=decimalai.crewai.skill_tools(),  # reads a skill's files, runs its scripts
)
task = Task(description="Hello!", expected_output="an answer", agent=agent)
Crew(agents=[agent], tasks=[task]).kickoff()

No DecimalAI package. CrewAI's own loader reads the folder, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/majiayu000-cliproxy-newapi-stack/SKILL.md

Give it to your agent

from pathlib import Path
from crewai import Agent, Crew, Task

agent = Agent(
    role="Support", goal="Help the customer", backstory="A support agent.",
    llm=llm,                         # your own CrewAI LLM
    skills=[Path(".agents/skills")],
)
task = Task(description="Hello!", expected_output="an answer", agent=agent)
Crew(agents=[agent], tasks=[task]).kickoff()

This gets SKILL.md only. The skill's 9 other files are not included.

Other ways to add it

CrewAI docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[adk]"
decimalai skills add majiayu000-cliproxy-newapi-stack

Add it to your agent

from decimalai.adk import DecimalAIPlugin, agent_config
from google.adk.agents import LlmAgent
from google.adk.apps import App

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = LlmAgent(
    name="support",          # ADK needs a Python identifier
    model="gemini-3.5-flash",
    instruction=support.instruction,  # your prompt, then the skill list
    tools=[*support.tools],           # your tools, then load_skill
)
# DecimalAIPlugin makes each invocation of runners built from this App one trace.
app = App(name="support", root_agent=agent, plugins=[DecimalAIPlugin()])

Prefer ADK's own loader? SkillToolset(skills=decimalai.adk.skills(), ...) reads the same folder. Use one or the other: a skill delivered both ways reaches the prompt twice.

Install

pip install "decimalai[adk]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("majiayu000-cliproxy-newapi-stack")
if result.exit_code:
    raise SystemExit(result.problems)

from decimalai.adk import DecimalAIPlugin, agent_config
from google.adk.agents import LlmAgent
from google.adk.apps import App

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = LlmAgent(
    name="support",          # ADK needs a Python identifier
    model="gemini-3.5-flash",
    instruction=support.instruction,  # your prompt, then the skill list
    tools=[*support.tools],           # your tools, then load_skill
)
# DecimalAIPlugin makes each invocation of runners built from this App one trace.
app = App(name="support", root_agent=agent, plugins=[DecimalAIPlugin()])

Prefer ADK's own loader? SkillToolset(skills=decimalai.adk.skills(), ...) reads the same folder. Use one or the other: a skill delivered both ways reaches the prompt twice.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/majiayu000-cliproxy-newapi-stack/SKILL.md

Give it to your agent

from pathlib import Path
from google.adk.agents import LlmAgent

skill = Path(".agents/skills/majiayu000-cliproxy-newapi-stack/SKILL.md").read_text()
agent = LlmAgent(
    name="support",
    model="gemini-3.5-flash",
    instruction=lambda context: f"You are a helpful agent.\n\n{skill}",
)

This gets SKILL.md only. The skill's 9 other files are not included.

Other ways to add it

Google ADK docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[pydantic-ai]"
decimalai skills add majiayu000-cliproxy-newapi-stack

Add it to your agent

from pydantic_ai import Agent
from decimalai.pydantic_ai import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = Agent(
    "openai:gpt-4o-mini",
    name=support.name,
    tools=[],                 # your tools; the capability adds load_skill
    capabilities=[support],   # your prompt, the skill list and load_skill
)
# In a notebook or async code: result = await agent.run("Hello!")
result = agent.run_sync("Hello!")

On the Pydantic AI harness, decimalai.pydantic_ai_harness.skills(project) uses its own loader instead.

Install

pip install "decimalai[pydantic-ai]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("majiayu000-cliproxy-newapi-stack")
if result.exit_code:
    raise SystemExit(result.problems)

from pydantic_ai import Agent
from decimalai.pydantic_ai import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = Agent(
    "openai:gpt-4o-mini",
    name=support.name,
    tools=[],                 # your tools; the capability adds load_skill
    capabilities=[support],   # your prompt, the skill list and load_skill
)
# In a notebook or async code: result = await agent.run("Hello!")
result = agent.run_sync("Hello!")

On the Pydantic AI harness, decimalai.pydantic_ai_harness.skills(project) uses its own loader instead.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/majiayu000-cliproxy-newapi-stack/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/majiayu000-cliproxy-newapi-stack/SKILL.md").read_text()
agent = Agent("openai:gpt-4o-mini", instructions=f"You are a helpful agent.\n\n{skill}")
# In a notebook or async code: result = await agent.run("Hello!")
result = agent.run_sync("Hello!")

This gets SKILL.md only. The skill's 9 other files are not included.

Other ways to add it

Pydantic AI docs

Deep Agents' own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" deepagents
decimalai skills add majiayu000-cliproxy-newapi-stack

Add it to your agent

import decimalai.deepagents
from deepagents import create_deep_agent

agent = create_deep_agent(model=model, **decimalai.deepagents.skills())
agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

The default backend reads files but has no shell. To run a skill's scripts, pass skills(backend=LocalShellBackend(...)).

The same install from Python, for a notebook or a setup script. Deep Agents' own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" deepagents

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("majiayu000-cliproxy-newapi-stack")
if result.exit_code:
    raise SystemExit(result.problems)

import decimalai.deepagents
from deepagents import create_deep_agent

agent = create_deep_agent(model=model, **decimalai.deepagents.skills())
agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

The default backend reads files but has no shell. To run a skill's scripts, pass skills(backend=LocalShellBackend(...)).

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/majiayu000-cliproxy-newapi-stack/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/majiayu000-cliproxy-newapi-stack/SKILL.md").read_text()
agent = create_deep_agent(model=model, system_prompt=f"You are a helpful agent.\n\n{skill}")
agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

This gets SKILL.md only. The skill's 9 other files are not included.

Other ways to add it

Deep Agents docs

Its own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" agent-framework
decimalai skills add majiayu000-cliproxy-newapi-stack

Add it to your agent

import asyncio

from agent_framework import Agent
from decimalai.agent_framework import skills

agent = Agent(client=client, instructions="You are a helpful agent.", context_providers=[skills()])
asyncio.run(agent.run("Hello!"))

It caches skills by default, so restart the process after decimalai skills update.

The same install from Python, for a notebook or a setup script. Its own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" agent-framework

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("majiayu000-cliproxy-newapi-stack")
if result.exit_code:
    raise SystemExit(result.problems)

import asyncio

from agent_framework import Agent
from decimalai.agent_framework import skills

agent = Agent(client=client, instructions="You are a helpful agent.", context_providers=[skills()])
asyncio.run(agent.run("Hello!"))

It caches skills by default, so restart the process after decimalai skills update.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/majiayu000-cliproxy-newapi-stack/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/majiayu000-cliproxy-newapi-stack/SKILL.md").read_text()
agent = Agent(client=client, instructions=f"You are a helpful agent.\n\n{skill}")
asyncio.run(agent.run("Hello!"))

This gets SKILL.md only. The skill's 9 other files are not included.

Other ways to add it

Microsoft Agent Framework docs

Strands' own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" strands-agents
decimalai skills add majiayu000-cliproxy-newapi-stack

Add it to your agent

from decimalai.strands import skills
from strands import Agent
from strands.vended_tools import file_editor, shell

agent = Agent(
    model=model,                # always pass one: the default is Amazon Bedrock
    system_prompt="You are a helpful agent.",
    tools=[file_editor, shell],
    plugins=[skills()],
)
agent("Hello!")

Strands loads an agent's skills once, so build the agent again after decimalai skills update.

The same install from Python, for a notebook or a setup script. Strands' own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" strands-agents

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("majiayu000-cliproxy-newapi-stack")
if result.exit_code:
    raise SystemExit(result.problems)

from decimalai.strands import skills
from strands import Agent
from strands.vended_tools import file_editor, shell

agent = Agent(
    model=model,                # always pass one: the default is Amazon Bedrock
    system_prompt="You are a helpful agent.",
    tools=[file_editor, shell],
    plugins=[skills()],
)
agent("Hello!")

Strands loads an agent's skills once, so build the agent again after decimalai skills update.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/majiayu000-cliproxy-newapi-stack/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/majiayu000-cliproxy-newapi-stack/SKILL.md").read_text()
agent = Agent(model=model, system_prompt=f"You are a helpful agent.\n\n{skill}")
agent("Hello!")

This gets SKILL.md only. The skill's 9 other files are not included.

Other ways to add it

Strands docs

Adds every file the skill has, pinned so you can update it. Cursor, Copilot, Cline and Warp read this folder by themselves.

pip install "decimalai>=0.15"
decimalai skills add majiayu000-cliproxy-newapi-stack

No Python or account. Cursor, Copilot, Cline and Warp read this folder by themselves.

Save it into your project

curl -fsSL --create-dirs -o .agents/skills/majiayu000-cliproxy-newapi-stack/SKILL.md https://app.decimal.ai/s/majiayu000-cliproxy-newapi-stack/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/majiayu000-cliproxy-newapi-stack/SKILL.md

This gets SKILL.md only. The skill's 9 other files are not included.

Other ways to add it

Cursor and other editors docs

Fetch the file and give its text to your model as instructions. No account or package needed.

Get the file

curl -fsSL https://app.decimal.ai/s/majiayu000-cliproxy-newapi-stack/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/majiayu000-cliproxy-newapi-stack.json

This gets SKILL.md only. The skill's 9 other files are not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v2 (v1 tested 8/24/2026)
Source: majiayu000/spellbook ↗
License: MIT

Manage it in DecimalAI

Saved prompts and skills, pinned versions, and traces of this skill in your runs. DecimalAI does not run your agent.

Claude Code reads files from your project, so DecimalAI config does not apply there.

On Claude Agent SDK: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On OpenAI Agents: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On OpenAI SDK: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Anthropic: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Gemini API: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On LangChain: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On CrewAI: set DECIMAL_API_KEY and add decimalai.init(crewai=True) to record runs and skill use. The skill version stays in your repo.

On Google ADK: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Pydantic AI: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Deep Agents: set DECIMAL_API_KEY and add decimalai.init(langchain=True) to record runs and skill use. The skill version stays in your repo.

On Microsoft Agent Framework: set DECIMAL_API_KEY and add decimalai.init(otel=True, agent_name="support") to record runs and skill use. The skill version stays in your repo.

On Strands: set DECIMAL_API_KEY and add decimalai.init(otel=True, agent_name="support") to record runs and skill use. The skill version stays in your repo.

Editors read files from your project, so DecimalAI config does not apply there.

Anywhere else: record a use with decimalai.log_skill_activation() inside a trace.

Use this skill

SKILL.md

CLIProxyAPI + NewAPI Metering Stack

在用户已有并已验证的 CLIProxyAPI upstream 前增加 NewAPI (calciumion/new-api) 计费、限流和多用户 token 层。本 Skill 不安装裸 CLIProxyAPI，也不调用其他部署 Skill 补齐这个前置条件。

所有"完成"结论必须基于本会话命令输出（W-16）。价格和额度变更后必须真发一次请求并查 logs.quota 与本次请求的 token 数和目标价格相符。

Operating Contract

Direct actions: 只读检查、配置备份和本地 dry-run 可直接执行。
Escalate before: 安装容器、修改既有 upstream、重启服务、改防火墙或写生产额度前必须取得本次任务的明确授权。
Evidence-backed pushback: 密钥只从环境变量或密码管理器读取；要求明文落盘或公网暴露原始端口时，展示具体风险并改用 tunnel/TLS。
Feedback loop: 每次变更后重跑 upstream、NewAPI 和精确计费验证；任一步骤无法取得真实状态时停止并报告，不用默认值伪造成功。

0. 前置确认（必问）

SSH 目标：root@HOST 是否能免密
端口分配：CLIPROXY_PORT（默认 8317）、NEWAPI_PORT（默认 8200）
upstream 证据：CLIProxyAPI 已在目标主机运行，当前会话能通过用户批准的 SSH tunnel 或 loopback 请求验证健康、模型列表和认证；证据不足就停止，不猜测、不自动安装裸 upstream
登录账号供应商：codex / claude / qwen / iflow / gemini
价格输入格式：每个虚拟模型给我 input / cached / output 三个 USD per 1M 数字
客户端机器：要在哪些机器上落 BASE_URL 环境变量

安全默认：本地 OAuth + scp 同步；NewAPI 仅绑定 VPS loopback，通过 SSH tunnel 完成首次注册。公网访问必须走已配置 TLS 的反向代理，不能直接开放 NewAPI 原始 HTTP 端口。

Phase 1 — 验证现有 CLIProxyAPI upstream

先记录现有服务的进程、监听地址、健康响应、模型列表和配置备份位置。任何一项无法验证都停止。只有用户在当前消息批准修改这个既有 upstream 时，才执行以下两个补丁：

加稳定性开关到 /root/CLIProxyAPI/config.yaml：
```
disable-cooling: true
```
原因见 references/troubleshooting.md "CLIProxyAPI cooldown 原理"。若漏改，30 并发 5KB payload 会出现混合 ~50% 503。
不要把 CLIProxyAPI 端口暴露到公网（与裸部署不同）：
- 先用 ip -4 addr show docker0 确认 Docker bridge 地址，再把 host 绑定到该地址（常见值为 172.17.0.1）
- 删除已有的公网 ufw allow <CLIPROXY_PORT>/tcp；管理访问统一走 SSH tunnel，不保留公网 admin 后门

Phase 2 — 部署 NewAPI 容器

IMAGE='calciumion/new-api@sha256:<VERIFIED_DIGEST>' \
SSH_TARGET=root@<HOST> SSH_KEY=~/.ssh/id_ed25519 PORT=8200 \
  scripts/deploy_newapi.sh

脚本只绑定远端 127.0.0.1:8200。首次注册前保持防火墙关闭该端口，建立 SSH tunnel：

ssh -N -L 8200:127.0.0.1:8200 -i <KEY> <SSH_TARGET>

仅访问本机 http://127.0.0.1:8200 完成 root 账号注册，并把密码保存到密码管理器。注册和登录验证成功后，再配置带 TLS 的 Caddy/Nginx 反向代理；先验证 HTTPS、认证和来源限制，再按需开放 443/tcp。禁止开放 <NEWAPI_PORT>。

Phase 3 — 渠道 + Token

3a. 创建渠道（CLIProxyAPI as upstream）

NewAPI 后台 → "渠道" → 新建 → OpenAI 类型：

base_url：http://172.17.0.1:<CLIPROXY_PORT> ⚠️ 不能写 127.0.0.1 — 容器里的 127.0.0.1 是容器自己。详见 references/troubleshooting.md "容器网络速记"。
密钥：CLIProxyAPI 的 cpa_xxx key
模型：以逗号分隔填 CLIProxyAPI 暴露的虚拟模型名，例如： gpt-5.4,gpt-5.3-codex,gpt-5.3-codex-spark,gpt-5.4-mini,gpt-5.2
测试按钮应当 200 OK；失败先查 references/troubleshooting.md。

3b. 创建 Token（客户端用）

NewAPI 后台 → "令牌" → 新建：

名称：client-default 之类标识
额度：先放 unlimited 或一个大数（实际计费由 user.quota 控制）
可用模型：勾上你给客户端开放的模型
复制生成的 sk-xxx（这是客户端的 BASE_API_KEY）

Phase 4 — 写价格 + 充额度

4a. 写价格

用 scripts/set_pricing.py（基于实测 USD/1M 自动算出三个倍率）：

SSH_TARGET=root@<HOST> SSH_KEY=~/.ssh/id_ed25519 \
  scripts/set_pricing.py \
    --model gpt-5.4       --input 2.5  --cached 0.25  --output 15 \
    --model gpt-5.3-codex --input 1.75 --cached 0.175 --output 14

脚本会：合并写入 options 表的 ModelRatio / CacheRatio / CompletionRatio，重启容器。

⚠️ 倍率语义见 references/newapi-pricing.md —— CacheRatio / CompletionRatio 是相对输入价的倍数，不是绝对单价。ModelRatio 在不同 fork 里可能除以 2，第一次配置务必发请求看 logs.quota 实际值匹配预期。

4b. 充额度

NewAPI 在线充值通常未配。需要紧急修正额度时，可使用参数化 SQLite helper：

SSH_TARGET=root@<HOST> SSH_KEY=~/.ssh/id_ed25519 \
  scripts/topup.sh <user_id> <quota>
# 例：1 1000000000  → 1B quota ≈ USD 2000 (默认 QuotaPerUnit=500000)

4c. 配置在线充值（可选）

如果要让用户自助充值，NewAPI 后台 → 系统 → 支付：

Stripe / 易支付 / 自定义 → 填 TopUpLink 等字段
不配在线充值时，管理员可使用经验证的 scripts/topup.sh；输入必须为整数，脚本会参数化 SQL 并验证恰好更新一行

Phase 5 — 客户端环境变量

只把非敏感配置写入客户端 shell 配置：

export BASE_URL="https://<NEWAPI_DOMAIN>/v1"
export BASE_MODEL="<虚拟模型名>"       # 如 gpt-5.4

API key 必须存进 Keychain 或其他密码管理器，在当前进程启动前读取；禁止写入 ~/.zshrc、聊天、脚本或命令行参数。例如 macOS 可使用：

key="$(security find-generic-password -s newapi-client -a client-default -w)"
export BASE_API_KEY="$key"

跨多台机器同步时（W-14 文件归属）：单台单台手动 SSH 改各自的 rc 文件，避免并行写覆盖。

Phase 6 — 加新账号（OpenAI/Anthropic/etc）

不需要重启服务，CLIProxyAPI 有 file watcher。

最简单：

PROVIDER=codex \
  SSH_TARGET=root@<HOST> SSH_KEY=~/.ssh/id_ed25519 \
  CLIPROXY_LOCAL=<本地 CLIProxyAPI 仓库路径> \
  scripts/add_codex_account.sh

脚本流程：

本地启 go run ./cmd/server -<provider>-login -config config.yaml
浏览器用新账号登录（先在浏览器登出旧账号或用无痕窗口 — 否则会复用旧 session 把旧凭据覆盖回去）
自动 diff ~/.cli-proxy-api/<provider>-*.json，把新文件 scp 到 VPS
tail VPS 日志确认 auth file changed (CREATE)

详见 references/multi-account.md（含订阅条件、轮询语义、删除账号、验证方法）。

验证（Phase 4/5/6 之后必跑）

SSH_TARGET=root@<HOST> SSH_KEY=~/.ssh/id_ed25519 \
  CLIPROXY_URL=http://127.0.0.1:<TUNNELED_CLIPROXY_PORT> \
  NEWAPI_URL=https://<NEWAPI_DOMAIN> \
  CLIPROXY_KEY="$(security find-generic-password -s cliproxy-admin -w)" \
  NEWAPI_TOKEN=$(security find-generic-password -s newapi-client -a client-default -w) \
  MODEL=<虚拟模型> INPUT_USD_PER_M=<输入价> OUTPUT_USD_PER_M=<输出价> \
  QUOTA_PER_UNIT=500000 \
  scripts/verify_stack.sh

通过判定：

直连 CLIProxyAPI HTTP 200
经 NewAPI HTTP 200
logs 中对应 request_id、模型和 token 数匹配本次请求
实际 quota 与目标输入/输出价格计算结果的误差不超过 5%（且至少允许 1 quota 的整数舍入）

任一不满足都不得声称"部署完成"。

资源索引

文件	用途
`scripts/deploy_newapi.sh`	NewAPI 容器一键部署 + 健康自检
`scripts/set_pricing.py`	用 USD/1M 三参数自动写 NewAPI ratios
`scripts/topup.sh`	直接 SQLite 改 `users.quota`
`scripts/verify_stack.sh`	双路径 + 计费日志验证
`scripts/add_codex_account.sh`	OAuth 登录 + 同步凭据 + watcher 校验
`agents/openai.yaml`	需要独立复核高风险部署或计费方案时的 agent 配置
`references/newapi-pricing.md`	ModelRatio / CacheRatio / CompletionRatio / QuotaPerUnit 完整语义 + 计算示例
`references/troubleshooting.md`	容器网络、cooldown、PUT 不生效、UFW 等踩坑表
`references/multi-account.md`	多账号轮询语义 + 加号 / 删号 / 订阅条件

不要做的事

❌ NewAPI 渠道 base_url 写 http://127.0.0.1:<port>（容器内自指）
❌ 把 CacheRatio 当绝对单价（实际是相对输入的倍数）
❌ 价格只走 /api/option/ PUT 不验证（已知该接口可能静默失败）
❌ 远端 OAuth 隧道折腾（5 分钟窗口 + 隧道配合，已踩过坑，统一用本地登录 + scp）
❌ 把 CLIProxyAPI 公网端口的 cpa_ key 和 NewAPI 的 sk- token 共享给同一类客户端
❌ 跨会话声称"价格生效"——必须本会话发请求 + 看 logs.quota
❌ 在没有 disable-cooling: true 的情况下做高并发压测

About this skill

Automatic

DevOps & SRE pack

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✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v5 — 9/18/2026

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LLM judge · does the skill do what it claims

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LLM judge · policy & harmful-content review

Source

Imported

majiayu000 / spellbook

skills/cliproxy-newapi-stack/SKILL.md

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License MITImported 9/18/2026Version v2Bundled 9 files · scripts

Illustrative Want to see what majiayu000/cliproxy-newapi-stack would put in front of your model? Preview the rendered body and check its trigger phrases — no LLM call, no signup.

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CLIProxyAPI + NewAPI Metering Stack

Operating Contract

0. 前置确认（必问）

Phase 1 — 验证现有 CLIProxyAPI upstream

Phase 2 — 部署 NewAPI 容器

Phase 3 — 渠道 + Token

3a. 创建渠道（CLIProxyAPI as upstream）

3b. 创建 Token（客户端用）

Phase 4 — 写价格 + 充额度

4a. 写价格

4b. 充额度

4c. 配置在线充值（可选）

Phase 5 — 客户端环境变量

Phase 6 — 加新账号（OpenAI/Anthropic/etc）

验证（Phase 4/5/6 之后必跑）

资源索引

不要做的事

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