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opencli-adapter-author · mxyhi | DecimalAI

Codingby mxyhi

opencli-adapter-author

Use when writing an OpenCLI adapter for a new site or adding a new command to an existing site. Guides end-to-end from first recon through field decoding, adapter coding, and verify. Replaces opencli-oneshot / opencli-explorer. For ad-hoc browser driving (no adapter), see opencli-browser instead; for a top-level orientation to opencli, see opencli-usage.

Never made a case worseMaintained

SkillScore

SkillScore82/100

Test results 82

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

23%82%of test cases passed +59 pts

Without skill5 of 22

With skill18 of 22

13 fixed5 passed either way4 still failing0 broke

Measured on gemini-3.6-flash9/7/2026v220 comparable

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskWhat exact OpenCLI command generates the initial code skeleton for a new browser adapter under `~/.opencli/clis/<site>/<name>.js`?

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 mxyhi-opencli-adapter-author
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("mxyhi-opencli-adapter-author")
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 mxyhi-opencli-adapter-author

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/mxyhi-opencli-adapter-author/SKILL.md https://app.decimal.ai/s/mxyhi-opencli-adapter-author/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/mxyhi-opencli-adapter-author/SKILL.md

This gets SKILL.md only. The skill's 13 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 mxyhi-opencli-adapter-author

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("mxyhi-opencli-adapter-author")
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/mxyhi-opencli-adapter-author/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/mxyhi-opencli-adapter-author/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 13 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 mxyhi-opencli-adapter-author

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("mxyhi-opencli-adapter-author")
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/mxyhi-opencli-adapter-author/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/mxyhi-opencli-adapter-author/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 13 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 mxyhi-opencli-adapter-author

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("mxyhi-opencli-adapter-author")
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/mxyhi-opencli-adapter-author/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/mxyhi-opencli-adapter-author/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 13 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 mxyhi-opencli-adapter-author

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("mxyhi-opencli-adapter-author")
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/mxyhi-opencli-adapter-author/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/mxyhi-opencli-adapter-author/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 13 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 mxyhi-opencli-adapter-author

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("mxyhi-opencli-adapter-author")
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/mxyhi-opencli-adapter-author/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/mxyhi-opencli-adapter-author/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 13 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 mxyhi-opencli-adapter-author

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("mxyhi-opencli-adapter-author")
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/mxyhi-opencli-adapter-author/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/mxyhi-opencli-adapter-author/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 13 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 mxyhi-opencli-adapter-author

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("mxyhi-opencli-adapter-author")
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/mxyhi-opencli-adapter-author/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 13 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 mxyhi-opencli-adapter-author

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("mxyhi-opencli-adapter-author")
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/mxyhi-opencli-adapter-author/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/mxyhi-opencli-adapter-author/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 13 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 mxyhi-opencli-adapter-author

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("mxyhi-opencli-adapter-author")
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/mxyhi-opencli-adapter-author/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/mxyhi-opencli-adapter-author/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 13 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 mxyhi-opencli-adapter-author

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("mxyhi-opencli-adapter-author")
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/mxyhi-opencli-adapter-author/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/mxyhi-opencli-adapter-author/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 13 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 mxyhi-opencli-adapter-author

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("mxyhi-opencli-adapter-author")
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/mxyhi-opencli-adapter-author/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/mxyhi-opencli-adapter-author/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 13 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 mxyhi-opencli-adapter-author

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("mxyhi-opencli-adapter-author")
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/mxyhi-opencli-adapter-author/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/mxyhi-opencli-adapter-author/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 13 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 mxyhi-opencli-adapter-author

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/mxyhi-opencli-adapter-author/SKILL.md https://app.decimal.ai/s/mxyhi-opencli-adapter-author/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/mxyhi-opencli-adapter-author/SKILL.md

This gets SKILL.md only. The skill's 13 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/mxyhi-opencli-adapter-author/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/mxyhi-opencli-adapter-author.json

This gets SKILL.md only. The skill's 13 other files are not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v2, tested 9/7/2026
Source: mxyhi/ok-skills ↗
License: Apache-2.0

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

opencli-adapter-author

你是要给一个站点写 adapter 的 agent。这份 skill 目标：简单站点争取 30 分钟内从零到通过 opencli browser verify；复杂、私有协议或写操作站点以证据完整和安全为先，不为了时限猜接口。

全程用现有工具：opencli browser * / opencli doctor / opencli browser init / opencli browser verify。没有新命令。

调试浏览器型 adapter 时，优先直接带上 --trace on --keep-tab true --window foreground。--trace on 每轮都落 trace artifact，summary.md 是失败/成功复盘入口；--keep-tab true --window foreground 让 tab lease 保留且浏览器窗口在前台，方便核对最终页面状态。

前置：看你落在哪

先拿 coverage-matrix.md 快速自测。三个问题：

数据在浏览器里看得到吗？（否 → 先解决鉴权）
数据是 HTTP/JSON/HTML 吗？（否 → 不在 skill 范围）
需要实时推送吗？（是 → 找同数据 HTTP 接口；没有就放弃）

三个都 yes 继续。

顶层决策树

先定 strategy，再写 adapter。 每次进入 Step 3/4 后、写代码前，必须产出一段 strategy note。没有这段 note，不要开始写 clis/<site>/<name>.js。

核心判断不是 "API 比 DOM 高级"，而是 数据源有没有外部契约。实测维护成本显示：公开/官方接口最稳；UI/DOM 语义通常也有用户可见契约；站内未文档化 XHR/GraphQL/signature endpoint 最容易漂。不要为了 "API-first" 把稳定的 UI/DOM 实现盲目迁到无契约内部接口。

Strategy: PUBLIC_API | COOKIE_API | PAGE_FETCH | INTERCEPT | DOM_STATE | UI_SELECTOR
Contract: stable | visible-ui | internal-unstable
Evidence:
- observed request/state: <endpoint / state global / UI-only signal>
- auth source: <none / browser cookie / csrf from meta / localStorage / page runtime>
- replay result: <status + content-type + non-empty sample shape>

If Strategy is PAGE_FETCH or INTERCEPT:
- why PUBLIC_API / COOKIE_API are unavailable:
- why UI_SELECTOR / DOM_STATE are not safer:
- why the maintenance cost is acceptable:

Strategy classes:

Strategy	契约级别	用在什么时候	证据要求
`PUBLIC_API`	stable	不需要登录，Node-side `fetch` 直接拿到目标数据	200 + JSON/HTML 含目标数据，不是埋点/广告
`COOKIE_API`	stable	Node-side `fetch` + `page.getCookies()` / header helper 能拿数据	cookie/CSRF 来源清楚，replay 非空
`UI_SELECTOR`	visible-ui	publish/upload/click/表单，或页面语义比内部接口更稳	selector 有语义锚点；错误路径是 typed error
`DOM_STATE`	visible-ui	数据在 hydration state / bootstrap JSON / SSR HTML 里	state key / script JSON / HTML 结构明确
`PAGE_FETCH`	internal-unstable	只能在页面上下文 `fetch` 才能复用 same-origin/session/runtime	`opencli browser eval fetch(...)` 非空；必须解释为什么避不开内部接口
`INTERCEPT`	internal-unstable	请求签名复杂，但页面自己能自然发出请求	触发 UI 后能截到目标 response；必须解释为什么 UI/DOM 不够

选择规则：优先 PUBLIC_API / COOKIE_API。如果 UI/DOM 语义稳定，不要强行升级到 PAGE_FETCH / INTERCEPT。只有公开/官方接口不可用、UI/DOM 无法表达目标数据或操作时，才承担无契约内部接口的维护成本。

实测：PAGE_FETCH / INTERCEPT 的 fix 频率约为 PUBLIC_API 的 7-8 倍，UI_SELECTOR 跟 COOKIE_API 同档。详细 ladder 推导、api_candidates 证据怎么填、booking #1680 等反例见 references/strategy-selection.md。

边界：只复用页面自己已经合法获得的数据/能力。不教破解签名、不绕验证码/风控/访问控制；遇到不可复用签名（如必须由页面 runtime 生成且不能安全抽象）就降级到 UI_SELECTOR / DOM_STATE / INTERCEPT。

START
  │
  ▼
┌──────────────────────────┐
│ opencli doctor 通？      │── no ──→ 修桥接（doctor 输出里的提示）
└──────────────────────────┘
  │ yes
  ▼
┌────────────────────────────────────────────────────┐
│ 读站点记忆：                                        │
│   1. ~/.opencli/sites/<site>/endpoints.json         │
│   2. ~/.opencli/sites/<site>/notes.md               │
│   3. references/site-memory/<site>.md               │
└────────────────────────────────────────────────────┘
  │ 命中 endpoint + 字段 → 直接跳到【endpoint 验证】（不跳写 adapter！memory 可能过期）
  │ 没命中 → 继续
  ▼
┌──────────────────────────┐
│ 站点侦察（site-recon）    │  → Pattern A/B/C/D/E
└──────────────────────────┘
  │
  ▼
┌──────────────────────────┐
│ API 发现（api-discovery）│  §1 network → §2 state → §3 bundle → §4 token → §5 intercept
└──────────────────────────┘
  │ 拿到候选 endpoint
  ▼
┌────────────────────────────────────────────┐
│ 需要 Deep Recon？                          │  无文档私有 API / DOM 丢数据 / 写操作 / 证据冲突
│ → references/deep-recon.md                 │  intent matrix → 因果 diff → 候选账本 → contract gate
└────────────────────────────────────────────┘
  │ 候选通过合同证明；不通过则记录拒绝与 lift condition
  ▼
┌────────────────────────────────────────────┐
│ 验证候选合同（memory 命中也要跑）           │── 401/403 ──→ 回到 §4 排 token
│ safe replay；不可 replay 的 read 用自然截获 │── 空/HTML ──→ 回到 site-recon 换 Pattern
│ 数据非空、identity 对、分页/错误语义完整     │── 站点换版 ──→ 标记旧 endpoint，回 api-discovery
└────────────────────────────────────────────┘
  │ OK
  ▼
┌───────────────────────────────────────┐
│ 字段解码（memory 里的 field-map 也要抽查）│  自解释 → 直接 / 已知代号 → field-conventions / 未知 → decode-playbook
│ 比一条已知字段和网页肉眼值，确认没错位     │
└───────────────────────────────────────┘
  │
  ▼
┌──────────────────────────┐
│ 设计 columns (output)    │  对照 output-design.md 的命名 / 类型 / 顺序
└──────────────────────────┘
  │
  ▼
┌──────────────────────────┐
│ opencli browser init      │  生成 ~/.opencli/clis/<site>/<name>.js 骨架
│ 复制最像的邻居 adapter    │
│ 改 name / URL / 映射三处  │
└──────────────────────────┘
  │
  ▼
┌──────────────────────────┐
│ opencli browser verify    │── 失败 ──→ autofix skill，用 --trace retain-on-failure 回对应步骤
└──────────────────────────┘
  │ 成功
  ▼
┌──────────────────────────┐
│ 字段 vs 网页肉眼对一遍   │── 数值不对 ──→ 回字段解码
└──────────────────────────┘
  │ 对得上
  ▼
┌──────────────────────────┐
│ 回写 ~/.opencli/sites/   │  endpoints / field-map / notes / fixtures
└──────────────────────────┘
  │
  ▼
DONE

Runbook（一步一步勾选）

[ ] 1. opencli doctor 返回 "Everything looks good"
[ ] 2. 读站点记忆：
       [ ] ~/.opencli/sites/<site>/endpoints.json 存在？里面有想要的 endpoint？
       [ ] references/site-memory/<site>.md 存在？看"已知 endpoint"节
       [ ] 命中后：**跳到第 5（endpoint 验证） + 第 7（字段核对）**，不能直接跳第 9 写 adapter
       [ ] memory 写入超过 30 天（看 `verified_at`）→ 当作过期，按冷启动走 Step 3 → 4
[ ] 3. 侦察（site-recon.md）：
       [ ] **首选**：`opencli browser analyze <url>` 一步拿 pattern + 反爬 + 最近 adapter + next step
       [ ] `analyze` 结论模糊时再手跑：`open` → `wait time 2` (或 `wait xhr <regex>`) → `network`
       [ ] 定 Pattern（A / B / C / D / E）
[ ] 4. API 发现（api-discovery.md）按 Pattern 选 §：
       [ ] Pattern A → §1 network 精读
       [ ] Pattern B → §2 state 抽取 + §1 深层数据
       [ ] Pattern C → §3 bundle / script src 搜索
       [ ] Pattern D → §4 token 来源 + 降级 §5
       [ ] Pattern E → 找 HTTP 轮询接口；找不到才 §5
       [ ] 无文档 API / DOM 丢数据 / 写操作 / bundle 与 network 冲突 → `deep-recon.md`
           [ ] 写 intent matrix 和明确的 mutation boundary
           [ ] baseline → 单一动作 → 新请求 diff；至少一组 changed-input 对照
           [ ] jsluice 只扩大候选面；候选必须进入 evidence ledger
           [ ] read 候选过 occurrence/replay/completeness/auth/pagination/failure gate
           [ ] write 候选有明确授权、目标绑定、幂等/不确定性与不可自动重试语义
[ ] 5. 候选合同验证（memory 命中也要重跑）：
       [ ] `PUBLIC_API / COOKIE_API / PAGE_FETCH`：safe replay 跨两个输入返回成功
       [ ] `INTERCEPT`：两次自然页面动作都截到属于目标 identity 的完整响应
       [ ] 响应含目标数据（不是 HTML / 广告 / 推荐侧栏），字段与网页对得上
       [ ] 分页达到 exact limit 或证明 upstream exhaustion；失败不返回 partial
       [ ] write 不自动 replay，必须过 `deep-recon.md` 的额外合同门禁
[ ] 6. 写 strategy note（写代码前的强制产物）：
       [ ] 从 `PUBLIC_API / COOKIE_API / PAGE_FETCH / INTERCEPT / DOM_STATE / UI_SELECTOR` 选一个
       [ ] 填 Contract：`stable / visible-ui / internal-unstable`
       [ ] 填 Evidence：observed request/state、auth source、replay result
       [ ] 如果选 `PAGE_FETCH` / `INTERCEPT`，必须解释为什么 `PUBLIC_API` / `COOKIE_API` / `UI_SELECTOR` / `DOM_STATE` 都不适合
       [ ] 如果选 `UI_SELECTOR` / `DOM_STATE`，不需要为 "为什么不是 API" 过度辩护；只要说明语义锚点和 typed error 路径
[ ] 7. 字段解码：
       [ ] 自解释 → 直接用 key
       [ ] 已知代号 → field-conventions.md 查表
       [ ] 未知代号 → field-decode-playbook.md（排序键对比 / 结构差分 / 常量排查）
[ ] 8. 设计 columns（output-design.md）：
       [ ] 命名 camelCase 且对齐邻居 adapter
       [ ] 类型 / 单位 / 百分比格式清楚
       [ ] 顺序：识别列 → 业务数字 → metadata
[ ] 9. 写 adapter（adapter-template.md）：
       [ ] opencli browser init <site>/<name>
       [ ] 找同站点或同类型最像的 adapter，cp 过来
       [ ] 改 name / URL / 字段映射
[ ] 10. opencli browser verify <site>/<name>
        [ ] 首轮通过后立刻 `--write-fixture` 生成 `~/.opencli/sites/<site>/verify/<cmd>.json` 种子
        [ ] 手改种子：加 `patterns`（URL / 日期 / ID 格式）+ `notEmpty`（核心字段）+ 收紧 `rowCount`
        [ ] 再跑一次 `opencli browser verify <site>/<name>`，确认 ✓ matches fixture
[ ] 11. 字段值 vs 网页肉眼比对（别只看 "Adapter works!"）
[ ] 12. 回写站点记忆（**verify 通过 + 肉眼比对对得上之后**，schema 见 `references/site-memory.md`）：
        [ ] `endpoints.json`：以 endpoint 的短名为 key，value = `{url, method, params.{required,optional}, response, verified_at: YYYY-MM-DD, notes}`
        [ ] `field-map.json`：只追加新代号。key = 字段代号，value = `{meaning, verified_at: YYYY-MM-DD, source}`；**已存在的 key 不要覆盖**，有冲突先和网页肉眼值对齐再写
        [ ] `notes.md`：顶部追加一段 `## YYYY-MM-DD by <agent/user>`，写本次写 adapter 时遇到的新坑 / 新结论
        [ ] `verify/<cmd>.json`：**必填。** `opencli browser verify` 的期望值（args / rowCount / columns / types / patterns / notEmpty），Step 10 已经让你生成了，这里只是 checklist
        [ ] `fixtures/<cmd>-<YYYYMMDDHHMM>.json`：仅保存公开数据或可证明完成脱敏的样本；私人邮箱/消息/账号等高敏响应改用合成 fixture，不落盘
        [ ] 原始 dump/capture 只短暂落 `/tmp/` 或受控 cache；安全分级后的长期样本才进 `fixtures/`，任务结束清理原始文件
[ ] 13. repo 贡献收口（私人 adapter 可跳过）：
        [ ] production-path tests，不只测 parser/helper
        [ ] `npm run typecheck` + focused/site tests + `npm run build`
        [ ] `node dist/src/main.js validate <site>`
        [ ] `npm run check:typed-error-lint` + `npm run check:silent-column-drop`
        [ ] adapter 文档；若 sitemap/site memory 有稳定新知识则同步
        [ ] `git diff --check` + 敏感数据扫描 + 删除 raw capture/cache + 释放 browser session
        [ ] 写操作或私有协议请独立 review exact head 后再合入

降级路径（某步卡住跳到哪）

卡在	现象	跳去
Step 4 API 发现	`network` 空，`__INITIAL_STATE__` 也空	§3 bundle 搜 baseURL
	bundle 搜不到 baseURL	§5 intercept
Step 5 endpoint 验证	401 / 403	§4 token 排查
	200 但响应是 HTML	回 Step 3 换 Pattern 判断
	200 但 `data: []` 空	参数传错 / 接口换版，回 §1 看 network 里真实请求头
Step 7 字段解码	排序键对比推不出	field-decode-playbook.md §3 结构差分
	还推不出	先输出 raw，adapter 跑起来再迭代
Step 10 verify 失败	`fltt` 漏了 / 字段映射错	autofix skill；复现命令加 `--trace retain-on-failure`
	某列永远是 `null`	字段路径错了，回 Step 7
Step 10 verify fixture mismatch	`[pattern]` row[i] 报错	先肉眼比对网页值；值对 → 是 fixture pattern 太严，放宽；值不对 → 字段映射错
	`[column] missing column "X"`	实际 response 没这列（站点改版 or args 影响）；重新 `--update-fixture` 或修 adapter
	`[type]` actual null / undefined	字段提取失败，回 Step 7 重抽；临时 fallback 用 union type `string\|null` 只有在语义真的可空时用
Step 11 数值不对	差 10000 倍	单位不统一（"万" vs "元"）
	百分比小 100 倍	响应已是 `0.025`，不要 × 100

参考文件

文件	什么时候翻
`references/coverage-matrix.md`	动手前做"是否在范围内"自测
`references/site-recon.md`	Step 3 定站点类型
`references/api-discovery.md`	Step 4 找 endpoint
`references/deep-recon.md`	复杂无文档站：动作归因、jsluice 候选扩展、合同证明、读写安全与交付净账
`references/strategy-selection.md`	Step 6 填 strategy note 之前：契约模型 + 实测 fix 频率 + `api_candidates` 证据用法 + 反例
`references/field-conventions.md`	Step 7 查已知字段代号
`references/field-decode-playbook.md`	Step 7 字段不在词典时
`references/output-design.md`	Step 8 命名 / 类型 / 顺序
`references/adapter-template.md`	Step 9 文件结构 + 活例子 `convertible.js`
`references/site-memory.md`	总览：in-repo 种子 + 本地 `~/.opencli/sites/` 的两层结构
`references/site-memory/<site>.md`	Step 2 读站点公共知识（eastmoney / xueqiu / bilibili / tonghuashun 已铺）
`references/success-rate-pitfalls.md`	Step 7 / 11 踩坑前翻：11 种"verify 能过但数据是错的"静默失败（含 aria-label locale-dependence）
`references/jsdom-fixture-pattern.md`	当 adapter 走 `page.evaluate` 内 DOM 抽取、且 mocked-evaluate 单测漏 silent bug 时——把 HTML 冻进 `clis/<site>/__fixtures__/` 用 JSDOM 跑（含 fixture 创建 mandatory `awk 'NF>0'` 收紧 + reverse-validate 纪律）
`references/typed-errors.md`	写 `func` 主体之前必读：5 类 typed error 落点表（ArgumentError / EmptyResultError / CommandExecutionError / AuthRequiredError / TimeoutError）+ 三大 silent anti-pattern（silent-clamp / sentinel-row / generic CliError）的反例修法

关键约定

adapter 只引 @jackwener/opencli/registry + @jackwener/opencli/errors，不用第三方
columns 数组和 func 返回对象 keys 完全对齐（含顺序）
中间解析对象 key 不能跟 columns 任一项重叠（否则 silent-column-drop audit 误判，PR #1329 R1 真踩过；改成专属命名 + push row 时 destructure aliasing）
browser: field 决定 func 签名：browser:false → (args)，browser:true → (page, args)。搞反时 args 实际是 debug flag，所有外部参数 silent fallback 到 default（PR #1329 upstream 之前 8 个 non-browser adapter 全踩过这个）
已知失败按 references/typed-errors.md 5-classification 抛对应 typed error；不要 silent return []，不要 silent return [{sentinel}]，不要 Math.max/min silent clamp 外部参数
写私人 adapter 用 ~/.opencli/clis/<site>/<name>.js（免 build）；要提 PR 才 copy 到 clis/<site>/<name>.js
站点记忆每轮回写：没记忆 → 用 skill → 产生记忆 → 下次变 5 分钟
“真实发生过”不等于“可作为 production contract 重放”。私有写请求、一次性风控 token、页面 runtime controller 都必须过 deep-recon.md 的 contract gate；过不了就记录 blocker/lift condition，不生成伪 API 命令。
调试过程中的原始 dump / 抓包 / HTML 样本只能短暂落在系统 /tmp/ 或受控 cache，任务结束删除。只有通过 site-memory.md 数据分级、准备长期保留的公开/合成/已脱敏样本才进入 ~/.opencli/sites/<site>/fixtures/。严禁在 repo 根目录、clis/<site>/ 或当前工作目录留 .dbg-*.html / raw-*.json / sample.*。
JSDOM unit-test fixture（clis/<site>/__fixtures__/<command>.html）是上面那条的例外——它是有意 commit 进 repo 的 review artifact，不是临时 dump。但因此 quality bar 要更高：必须按 references/jsdom-fixture-pattern.md 的 5 步做完（含 mandatory awk 'NF>0' 空白行收紧），并 reverse-validate 一道证明 regression guard 真能挂。

卡住了

诊断类：opencli doctor → 看 notes.md → 搜 autofix skill
字段解码类：field-decode-playbook.md 全三节走完 → 先输出 raw 迭代
endpoint 找不到：api-discovery §5 intercept 兜底

不要猜。猜错了 verify 能通过但数据是错的，用户看到乱码才发现。

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buddhayi/opencli-adapter-authorUse when writing an OpenCLI adapter for a new site or adding a new command to an existing site. Guides end-to-end from first recon through field decoding, adapter coding, and verify. Replaces opencli-oneshot / opencli-explorer. For ad-hoc browser driving (no adapter), see opencli-browser instead; for a top-level orientation to opencli, see opencli-usage.Near-identical description+65 pts pass-rate lift

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