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/render-html: Markdown → single-file HTML for human reading · wanshuiyin | DecimalAI

Documentationby wanshuiyin

/render-html: Markdown → single-file HTML for human reading

Render an ARIS Markdown / JSON artifact (IDEA_REPORT, AUTO_REVIEW, KILL_ARGUMENT, PAPER_PLAN, research-wiki state, etc.) into a single-file HTML view designed for human reading.

Maintained

SkillScore

SkillScore68/100

Test results 68

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

18%68%of test cases passed +50 pts

Without skill4 of 22

With skill15 of 22

12 fixed3 passed either way6 still failing1 broke

Model 9/9/2026v6 · latest is v815 comparable

This benchmark measured an earlier version of the skill.

A newer attempt on gemini-3.8-flash on 10/23/2026 did not produce a publishable comparison (graded differently). The snapshot above is the previously measured run.

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskWhere should the raw Codex agent trace be saved when performing an academic HTML rendering review run on 2026-04-12 run 03?

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 wanshuiyin-render-html
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("wanshuiyin-render-html")
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 wanshuiyin-render-html

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/wanshuiyin-render-html/SKILL.md https://app.decimal.ai/s/wanshuiyin-render-html/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/wanshuiyin-render-html/SKILL.md

This gets SKILL.md only. The skill's 1 other file is 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 wanshuiyin-render-html

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("wanshuiyin-render-html")
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/wanshuiyin-render-html/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/wanshuiyin-render-html/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 1 other file is 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 wanshuiyin-render-html

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("wanshuiyin-render-html")
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/wanshuiyin-render-html/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/wanshuiyin-render-html/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 1 other file is 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 wanshuiyin-render-html

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("wanshuiyin-render-html")
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/wanshuiyin-render-html/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/wanshuiyin-render-html/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 1 other file is 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 wanshuiyin-render-html

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("wanshuiyin-render-html")
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/wanshuiyin-render-html/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/wanshuiyin-render-html/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 1 other file is 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 wanshuiyin-render-html

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("wanshuiyin-render-html")
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/wanshuiyin-render-html/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/wanshuiyin-render-html/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 1 other file is 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 wanshuiyin-render-html

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("wanshuiyin-render-html")
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/wanshuiyin-render-html/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/wanshuiyin-render-html/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 1 other file is 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 wanshuiyin-render-html

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("wanshuiyin-render-html")
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/wanshuiyin-render-html/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 1 other file is 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 wanshuiyin-render-html

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("wanshuiyin-render-html")
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/wanshuiyin-render-html/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/wanshuiyin-render-html/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 1 other file is 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 wanshuiyin-render-html

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("wanshuiyin-render-html")
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/wanshuiyin-render-html/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/wanshuiyin-render-html/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 1 other file is 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 wanshuiyin-render-html

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("wanshuiyin-render-html")
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/wanshuiyin-render-html/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/wanshuiyin-render-html/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 1 other file is 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 wanshuiyin-render-html

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("wanshuiyin-render-html")
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/wanshuiyin-render-html/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/wanshuiyin-render-html/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 1 other file is 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 wanshuiyin-render-html

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("wanshuiyin-render-html")
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/wanshuiyin-render-html/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/wanshuiyin-render-html/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 1 other file is 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 wanshuiyin-render-html

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/wanshuiyin-render-html/SKILL.md https://app.decimal.ai/s/wanshuiyin-render-html/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/wanshuiyin-render-html/SKILL.md

This gets SKILL.md only. The skill's 1 other file is 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/wanshuiyin-render-html/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/wanshuiyin-render-html.json

This gets SKILL.md only. The skill's 1 other file is not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v8 (v6 tested 9/9/2026)
Source: wanshuiyin/auto-claude-code-research-in-sleep ↗
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

/render-html: Markdown → single-file HTML for human reading

Before each reviewer slot, follow Selectable Reviewers: resolve invocation → .aris/reviewer.json → Codex default (fresh native reviewer) with reviewer_route.py through the canonical helper resolver, preserving this slot's tier, fresh contexts, source/image inputs and deterministic gates. Codex examples/pins below apply only to backend=codex; other routes use their own first-call/reply contract. Save the route and actual returned identity; unavailable selections are REVIEW_UNAVAILABLE. Classify acceptance from actual families/completion/scope. Forward invocation reviewer/model/effort overrides to reviewer-bearing children. Generation workers are unchanged.

Markdown is for writers. HTML is for readers. ARIS workflow nodes write Markdown (canonical, audit-trail-friendly, machine-parseable). /render-html turns selected artifacts into a polished single-file HTML view for the human who actually has to read them. The Markdown stays the source of truth.

When to use this skill

Use /render-html for ARIS artifacts that have a real human reader:

Artifact	Why HTML helps	Template
`idea-stage/IDEA_REPORT.md`	Ranked ideas + pilot signal + scores feel like a decision dashboard, not a flat list	`academic`
`review-stage/AUTO_REVIEW.md` (+ `REVIEW_STATE.json`)	Round-by-round score progression + weakness status; pass `--state` to embed the JSON	`academic`
`paper/KILL_ARGUMENT.md` (+ `KILL_ARGUMENT.json`)	Per-point attack/defense with `<details>` Q&A cards + red/yellow/green callouts	`academic`
`research-wiki/SUMMARY.md` (or `research-wiki/index.md`)	Cross-entity cockpit: papers / ideas / experiments / claims at a glance	`dashboard`
`PAPER_PLAN.md` (optional)	Claims-evidence matrix renders better as a polished table than raw MD	`academic`
`RESUBMIT_REPORT.{md,json}` (optional)	7-state failure-mode ledger	`academic` or `dashboard`

Do NOT use for:

LaTeX paper output — the final reader-facing artifact is PDF, not HTML.
SKILL.md files — those are internal LLM-facing protocol.
.aris/traces/* review traces — forensic debug, not human display.
Every Markdown file in your project — only artifacts that benefit from sticky TOC, callouts, math, or score progressions.

Core invariants

MD / JSON is canonical, HTML is generated view. Edit the source, then re-render. Do not hand-edit the HTML.
Fresh review at the artifact boundary. Academic-template HTML uses the selected reviewer in a fresh context. With the default native Codex reviewer the result is same-family provisional; a completed positive selected review with known different model families may provide accepted review. Dashboard HTML skips review by default but accepts --review.
Drift detection. Every rendered HTML embeds the source path, SHA256, and generation timestamp in <meta> tags AND in the visible page header. If the HTML and source diverge, the meta tells you which version of the source produced it.
Single-file output. No build system, no separate CSS, no node_modules. Just one .html.
CDN-friendly default, --offline fallback. MathJax 3 and highlight.js load from cdn.jsdelivr.net by default. Pass --offline to skip both — math will appear as raw $x$ , code blocks won't get syntax highlighting, but everything stays readable.
Pure stdlib helper. render_html.py uses only re, html, hashlib, json, datetime, pathlib, argparse, sys. No pip install required.
Defense-in-depth XSS sanitization. The helper strips <script>/<style>/<iframe>/<object>/<embed>/<form>/<input>/<button>/<link>/<meta>/<base> tags, all on* event-handler attributes (onclick, onload, …), and rewrites javascript:/vbscript:/data: href/src/action schemes to #blocked-unsafe-url:. ARIS workflow artifacts should not contain these in the first place, but the sanitizer is the safety net in case an LLM hallucinates one. Markdown text content is HTML-escaped separately and never reaches the sanitizer.

Tool Location

Arch C self-contained: the canonical implementation lives at skills/render-html/scripts/render_html.py (this SKILL's own scripts/ subdirectory), together with its templates at skills/render-html/scripts/templates/{academic,dashboard}.html. The helper is new — no legacy tools/ shim exists.

Resolve $RENDER_HTML with the Codex hybrid chain (managed project install, in-repo checkout, ARIS repo fallback, then copied global Codex skill install). This is Policy A — skill-local gate:

RENDER_HTML=""
cd "$(git rev-parse --show-toplevel 2>/dev/null || pwd)" || exit 1
if [ -z "${ARIS_REPO:-}" ] && [ -f .aris/installed-skills-codex.txt ]; then
    ARIS_REPO=$(awk -F'\t' '$1=="repo_root"{print $2; exit}' .aris/installed-skills-codex.txt 2>/dev/null) || true
fi
[ -f ".agents/skills/render-html/scripts/render_html.py" ] && RENDER_HTML=".agents/skills/render-html/scripts/render_html.py"
[ -z "$RENDER_HTML" ] && [ -f "skills/render-html/scripts/render_html.py" ] && RENDER_HTML="skills/render-html/scripts/render_html.py"
[ -z "$RENDER_HTML" ] && [ -n "${ARIS_REPO:-}" ] && [ -f "$ARIS_REPO/skills/render-html/scripts/render_html.py" ] && RENDER_HTML="$ARIS_REPO/skills/render-html/scripts/render_html.py"
[ -z "$RENDER_HTML" ] && [ -f ~/.codex/skills/render-html/scripts/render_html.py ] && RENDER_HTML="$HOME/.codex/skills/render-html/scripts/render_html.py"
[ -z "$RENDER_HTML" ] && {
  echo "ERROR: render_html.py not resolved at .agents/skills/render-html/scripts/, skills/render-html/scripts/, \$ARIS_REPO/skills/render-html/scripts/, or ~/.codex/skills/render-html/scripts/." >&2
  echo "       /render-html cannot produce HTML output. Fix: rerun install_aris_codex.sh, export ARIS_REPO, or copy the render-html skill into ~/.codex/skills/." >&2
  exit 1
}

Invocation

# Default: academic template, output to <input>.html alongside source
python3 "$RENDER_HTML" idea-stage/IDEA_REPORT.md

# Dashboard template for cockpit-style views
python3 "$RENDER_HTML" research-wiki/SUMMARY.md --template dashboard

# Custom output path + title + eyebrow
python3 "$RENDER_HTML" review-stage/AUTO_REVIEW.md \
  --out review-stage/AUTO_REVIEW.html \
  --title "Auto Review — overnight run" \
  --eyebrow "Workflow 2"

# Embed sidecar state JSON (rendered as a folded <details> JSON block at end)
python3 "$RENDER_HTML" review-stage/AUTO_REVIEW.md \
  --state review-stage/REVIEW_STATE.json

# Embed sidecar JSON (e.g., for KILL_ARGUMENT.md + KILL_ARGUMENT.json)
python3 "$RENDER_HTML" paper/KILL_ARGUMENT.md \
  --json paper/KILL_ARGUMENT.json \
  --title "Kill Argument — adversarial review"

# Offline (no CDN; math + code render as plain text)
python3 "$RENDER_HTML" idea-stage/IDEA_REPORT.md --offline

# JSON-only input (wrapped in a <pre><code class="language-json"> block)
python3 "$RENDER_HTML" review-stage/REVIEW_STATE.json --template dashboard

# Language attr (default zh-CN; set for English-primary artifacts)
python3 "$RENDER_HTML" docs/SKILLS_CATALOG.md --lang en

# Skip review (academic template otherwise reviews by default)
python3 "$RENDER_HTML" idea-stage/IDEA_REPORT.md
# … then the skill skips the spawn_agent review step if --no-review
# was on the command line. Pass --review to a dashboard render to force it.

The --review / --no-review flags are parsed by the SKILL orchestrator (Claude Code), not by render_html.py. The helper itself stays pure stdlib and never calls MCP. See § HTML Review Gate below for the exact resolution and prompt.

Workflow

Step 1: Identify the artifact

From $ARGUMENTS, determine what to render. Common patterns:

"render IDEA_REPORT" → idea-stage/IDEA_REPORT.md, academic template
"make AUTO_REVIEW readable" → review-stage/AUTO_REVIEW.md + --state review-stage/REVIEW_STATE.json
"show the kill argument as HTML" → paper/KILL_ARGUMENT.md + --json paper/KILL_ARGUMENT.json
"research-wiki dashboard" → look for research-wiki/SUMMARY.md or generate from raw entity counts (Phase 2 work; for Phase 1 just render SUMMARY.md if present, else fall back to listing top-level wiki structure)

Step 2: Pick template

academic (default): linear long-form. Sticky TOC sidebar + serif body + callouts + tables + math + Q&A <details>. Use for IDEA_REPORT, AUTO_REVIEW, KILL_ARGUMENT, PAPER_PLAN.
dashboard: grid layout, smaller font, denser metrics cards, no TOC sidebar. Use for research-wiki cockpit, RESUBMIT_REPORT, multi-project overviews.

Step 3: Run the helper

Use the resolver above to get $RENDER_HTML, then invoke. The script writes the HTML alongside the source by default (or wherever --out says) and prints a one-line confirmation including the source SHA256 prefix.

Step 4: HTML Review Gate (same-family provisional by default)

Decide whether to run review. Academic-template HTML is reviewed by the selected reviewer in a fresh context with no author history before being claimed as reviewed. The default native Codex route records review_independence: same-family and acceptance_status: provisional; a completed positive selected review with known different model families records cross-family accepted. Resolution:

should_review = explicit --review present
             or (template == "academic" and --no-review NOT present)

So:

--template academic (default) → review by default. Skip with --no-review.
--template dashboard → no review by default. Force with --review.
Phase 2 workflow auto-emit (planned) follows the same rule.

If should_review is true, fire a fresh spawn_agent call (NEVER send_input; pin model: gpt-6-astra + reasoning_effort: xhigh per ../shared-references/reviewer-routing.md) with the prompt below. The reviewer reads the source MD + generated HTML directly; it does not see this skill's intermediate state.

Scope of review (narrow on purpose). The HTML reviewer audits render fidelity / safety / structure only — not claim truthfulness. Claim audit belongs upstream (/paper-claim-audit, /research-review, /result-to-claim). Specifically the reviewer checks:

Information fidelity — every section, claim, table, code block, list, math snippet, sidecar payload is present in the HTML (no silent drop)
Structural integrity — heading hierarchy / nested lists / table cells / code fences / <details> blocks / math delimiters survive parsing
Callout routing — > 🚨 … got .callout-bad, > 💡 … got .callout-info, etc.
Safety / escaping — no raw <script> / onclick= / javascript: / unreplaced placeholders / template-leak survives
Expected-difference allowance — frontmatter strip, generated header/footer/meta, TOC insert, sanitized unsafe HTML are all expected, not flagged

Codex prompt (mandatory shape). Send this as a fresh reviewer call (spawn_agent, NOT send_input):

You are an independent ARIS HTML render auditor. This is a fresh review thread.

Read these files directly:
- Source artifact: <ABS path to source.md or source.json>
- Generated HTML:  <ABS path to out.html>
- Optional sidecars: <state.json>, <kill_argument.json> (if any)

Task: Audit whether the generated HTML is a faithful, safe, structurally
usable view of the source artifact. Do NOT judge whether the research
claims are true. Judge only rendering fidelity.

Checks:
1. Information fidelity — sections / claims / tables / code blocks /
   lists / math / sidecars not silently dropped or materially altered.
2. Structural integrity — heading hierarchy, tables, nested lists,
   code fences, details/summary, math delimiters preserved.
3. Callout routing — warning/critical/good/info blockquotes map to
   appropriate CSS classes when present.
4. Safety/escaping — no unexpected raw script/style/iframe/form/
   event-handler/javascript/data URL survives from source.
5. Placeholder/template leakage — no unreplaced {{PLACEHOLDER}} or
   parser private placeholder character appears.
6. Expected differences — frontmatter strip, generated header/footer/
   meta, TOC insertion, sanitized unsafe HTML are EXPECTED and not a
   defect.

Return STRICT JSON first, then a short prose note:

{
  "verdict": "PASS|WARN|FAIL|ERROR",
  "review_independence": "<actual family comparison>",
  "acceptance_status": "<actual acceptance status>",
  "checks": {
    "source_hash_match": "pass|warn|fail|unknown",
    "information_fidelity": "pass|warn|fail",
    "structure": "pass|warn|fail",
    "math_code_tables": "pass|warn|fail",
    "callouts": "pass|warn|fail|not_applicable",
    "safety_escaping": "pass|warn|fail",
    "placeholder_leak": "pass|warn|fail"
  },
  "blocking_issues": [
    {"severity": "fail",
     "source_location": "L<n>...",
     "html_location": "<selector or near-text>",
     "issue": "...",
     "suggested_fix": "..."}
  ],
  "warnings": [
    {"severity": "warn", "issue": "...", "suggested_fix": "..."}
  ],
  "summary": "one paragraph"
}

Verdict rules:
- PASS: no material fidelity/safety issue.
- WARN: readable output with minor unsupported-Markdown or cosmetic
  degradation only.
- FAIL: missing/altered meaningful content, broken tables/math/code/
  callouts that change interpretation, unsafe executable HTML, source
  hash mismatch, or placeholder leakage.
- ERROR: files could not be read or audit could not complete.

Save outputs:

Write the JSON verdict to <out_path>.review.json (sibling to the HTML).
Save the raw codex trace to .aris/traces/render-html/<YYYY-MM-DD>_run<NN>/review.{txt,json} per shared-references/review-tracing.md.
Print a one-line summary to the user: verdict, N blocking, N warnings, trace: <path>.

If verdict == FAIL: the HTML is NOT a delivered review-passed view. Tell the user the blocking issues, point them at the source (fix MD or template, not the HTML), and re-render. Do not silently overwrite or mark as complete.

If verdict == WARN: deliver the HTML but surface the warning list. User decides whether to fix or accept.

If the selected reviewer route is unavailable: emit verdict: REVIEW_UNAVAILABLE to the sidecar, do not fabricate PASS, and tell the user the HTML was generated but not independently reviewed. The user can manually invoke /research-review on the source MD or re-run when the selected reviewer is available.

Step 5: (Optional) Verify in browser

open <out.html> on macOS, xdg-open on Linux, start on Windows. Math + code highlighting need internet (CDN); offline mode degrades gracefully to readable text.

What the helper supports

Markdown subset (chosen to match what ARIS workflows actually emit):

Headings #/##/###/#### with auto-generated IDs for TOC
Paragraphs, bold **x**, italic *x* / _x_, inline code `x`, strikethrough ~~x~~, links [t](url), images ![a](url)
Unordered/ordered lists with 2-space nested indentation
Code blocks with optional language (```python) — gets <pre><code class="language-python"> for highlight.js
ASCII-art code blocks (heuristic: many box-drawing chars) → <pre class="diagram"> with a distinct cream-yellow background
Tables with :--- / :---: / ---: alignment
Blockquotes > ... — emoji-prefix detection routes to callout variants:
- ⚠️ → .callout-warn (Warning)
- 💡 / 📝 → .callout-info (Tip / Note)
- ✅ / 🔒 → .callout-good (OK / Guarantee)
- ❌ / 🚨 → .callout-bad (Blocked / Critical)
HTML passthrough for <details>, <summary>, <div>, <figure>, <table>, <section>, etc. (block-level) — used for the existing Round-1/2/3 fix details in KILL_ARGUMENT.md
LaTeX math: inline $x$ and display $$x$$ pass through verbatim to MathJax

What the helper does NOT support (intentional simplifications):

Footnotes ([^1])
Definition lists
Reference-style links [label][ref]
Setext-style headings (=== / --- underline form — ARIS doesn't emit these)

Frontmatter (--- ... ---) at the very top of the file is stripped before rendering (SKILL.md frontmatter style); the body that follows is what gets converted.

Two-phase integration plan (Phase 1 is this skill; Phase 2 is workflow hooks)

Phase 1 (this skill, currently shipped): Opt-in only. Users explicitly call /render-html <artifact.md> after a workflow completes. No existing skill changes. Lets us validate which HTML views are actually useful before automating.

Phase 2 (later, planned): Selectively auto-emit HTML at workflow termination:

/idea-discovery completes → also writes idea-stage/IDEA_REPORT.html (academic)
/auto-review-loop terminates → also writes review-stage/AUTO_REVIEW.html (academic, with --state)
/kill-argument already writes .md + .json → naturally extends to + .html
/research-pipeline final report links to the HTML files above (doesn't re-render)
/research-wiki render subcommand generates research-wiki/index.html dashboard
/paper-writing does not auto-emit HTML (the final reader artifact is PDF)

Phase 2 will be guarded by a — html: true flag in each affected skill, defaulting to false until we have empirical evidence the HTML views are read.

Customizing the templates

The two templates live at skills/render-html/scripts/templates/{academic,dashboard}.html. Each is a single self-contained HTML file with inline CSS using {{PLACEHOLDER}} substitution. To customize:

Copy one of the templates to a new name, e.g., my_brand.html.
Edit the CSS variables in :root { ... } to change colors, the font stack, or layout dimensions.
Add the template name to the --template choices in render_html.py argparse.
Re-run /render-html <input> --template my_brand.

The default templates are derived from the user's own academic-newspaper tutorial style (Source Serif Pro + Songti SC, 3-color palette, sticky TOC, low-flash). Stay close to that idiom for ARIS artifacts unless you have a specific reason to break the visual language.

External alternatives (for richer surfaces)

For deck / poster / Xiaohongshu card / tweet card / data report style outputs, point users to html-anything (Apache-2.0, 3000⭐ at time of writing). It ships 75 SKILL.md templates across 9 surfaces and detects 8 coding-agent CLIs (including Claude Code, Codex, Copilot). ARIS does not depend on it — /render-html covers ARIS-native artifacts; html-anything is the recommended path for richer publishing surfaces.

Key rules

Do not auto-render every Markdown file. Only artifacts on the whitelist above. File proliferation is the main anti-pattern.
Do not hand-edit the generated HTML. Edit the source, then re-render. The embedded SHA256 in the HTML meta tells you if the source has changed since render.
academic-template HTML is a reviewed artifact, not raw output. Base Codex review is fresh but same-family provisional; never call it cross-model acceptance. --no-review exists for fast iteration but should not be the way you ship.
The reviewer audits rendering, not research. Claim truthfulness is owned upstream by /paper-claim-audit, /result-to-claim, /research-review. The HTML reviewer asks: "did the renderer faithfully + safely convert this source?" — nothing more.
CDN dependency is opt-out, not opt-in. Most users have internet; --offline is for air-gapped runs / archival.
The default style is academic-newspaper, not marketing-flashy. Match the existing ARIS tonal voice. If you want decks/posters/social cards, point users to html-anything.
Pure stdlib only. Adding a pip install dependency to render_html.py requires an explicit decision — the helper currently has none. MCP calls live in the skill orchestrator, never in the helper script.

When it activates

Automatically, when a task matches. Use when the user says "渲染 HTML", "出一份 HTML 报告", "render html", "make this readable", "export to html", or wants a polished web-rendered view of a Markdown artifact.

About this skill

Automatic

Doc Ops pack

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v8 — 10/23/2026

View 15 scanner checks

live_secretremote_code_executionreverse_shelldata_exfiltrationssrf_metadataagent_snoopinganti_refusaldestructive_commandsobfuscated_unicodeunicode_homoglyphopaque_blobsuspicious_urltool_overreachtrigger_abuseprompt_injection_phrasing

IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

wanshuiyin / auto-claude-code-research-in-sleep

skills/render-html/SKILL.md

View on GitHub ↗

License MITImported 10/23/2026Version v8Bundled 1 file · scripts

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

Related skills

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From the same source and nearby in the catalogue.

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SkillScore

Safety

What it changes

Track record

Use this skill

/render-html: Markdown → single-file HTML for human reading

When to use this skill

Core invariants

Tool Location

Invocation

Workflow

Step 1: Identify the artifact

Step 2: Pick template

Step 3: Run the helper

Step 4: HTML Review Gate (same-family provisional by default)

Step 5: (Optional) Verify in browser

What the helper supports

Two-phase integration plan (Phase 1 is this skill; Phase 2 is workflow hooks)

Customizing the templates

External alternatives (for richer surfaces)

Key rules

When it activates

SkillSafety

Source

Related skills

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