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Tech News Digest · LeoYeAI | DecimalAI

Researchby LeoYeAI

Tech News Digest

Generate tech news digests with unified source model, quality scoring, and multi-format output. Six-source data collection from RSS feeds, Twitter/X KOLs, GitHub releases, GitHub Trending, Reddit, and web search. Pipeline-based scripts with retry mechanisms and deduplication. Supports Discord, email, and markdown templates.

SkillScore

SkillScore57/100

Test results 57

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

4%57%of test cases passed +52 pts

Without skill1 of 23

With skill13 of 23

12 fixed1 passed either way10 still failing0 broke

Measured on gemini-3.6-flash7/27/2026v121 comparable

Track record

Agent runs: 0; All time

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 tech-news-digest
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("tech-news-digest")
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 tech-news-digest

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/tech-news-digest/SKILL.md https://app.decimal.ai/s/tech-news-digest/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/tech-news-digest/SKILL.md

This gets SKILL.md only. The skill's 17 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 tech-news-digest

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("tech-news-digest")
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/tech-news-digest/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/tech-news-digest/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 17 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 tech-news-digest

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("tech-news-digest")
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/tech-news-digest/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/tech-news-digest/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 17 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 tech-news-digest

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("tech-news-digest")
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/tech-news-digest/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/tech-news-digest/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 17 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 tech-news-digest

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("tech-news-digest")
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/tech-news-digest/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/tech-news-digest/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 17 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 tech-news-digest

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("tech-news-digest")
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/tech-news-digest/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/tech-news-digest/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 17 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 tech-news-digest

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("tech-news-digest")
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/tech-news-digest/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/tech-news-digest/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 17 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 tech-news-digest

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("tech-news-digest")
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/tech-news-digest/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 17 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 tech-news-digest

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("tech-news-digest")
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/tech-news-digest/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/tech-news-digest/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 17 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 tech-news-digest

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("tech-news-digest")
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/tech-news-digest/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/tech-news-digest/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 17 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 tech-news-digest

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("tech-news-digest")
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/tech-news-digest/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/tech-news-digest/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 17 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 tech-news-digest

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("tech-news-digest")
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/tech-news-digest/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/tech-news-digest/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 17 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 tech-news-digest

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("tech-news-digest")
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/tech-news-digest/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/tech-news-digest/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 17 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 tech-news-digest

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/tech-news-digest/SKILL.md https://app.decimal.ai/s/tech-news-digest/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/tech-news-digest/SKILL.md

This gets SKILL.md only. The skill's 17 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/tech-news-digest/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/tech-news-digest.json

This gets SKILL.md only. The skill's 17 other files are not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v1, tested 7/27/2026
Source: LeoYeAI/openclaw-master-skills ↗
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

Tech News Digest

Automated tech news digest system with unified data source model, quality scoring pipeline, and template-based output generation.

Quick Start

Configuration Setup: Default configs are in config/defaults/. Copy to workspace for customization:

mkdir -p workspace/config
cp config/defaults/sources.json workspace/config/tech-news-digest-sources.json
cp config/defaults/topics.json workspace/config/tech-news-digest-topics.json

Environment Variables:
- TWITTERAPI_IO_KEY - twitterapi.io API key (optional, preferred)
- X_BEARER_TOKEN - Twitter/X official API bearer token (optional, fallback)
- TAVILY_API_KEY - Tavily Search API key, alternative to Brave (optional)
- WEB_SEARCH_BACKEND - Web search backend: auto|brave|tavily (optional, default: auto)
- BRAVE_API_KEYS - Brave Search API keys, comma-separated for rotation (optional)
- BRAVE_API_KEY - Single Brave key fallback (optional)
- GITHUB_TOKEN - GitHub personal access token (optional, improves rate limits)

Generate Digest:

# Unified pipeline (recommended) — runs all 6 sources in parallel + merge
python3 scripts/run-pipeline.py \
  --defaults config/defaults \
  --config workspace/config \
  --hours 48 --freshness pd \
  --archive-dir workspace/archive/tech-news-digest/ \
  --output /tmp/td-merged.json --verbose --force

Use Templates: Apply Discord, email, or PDF templates to merged output

Configuration Files

`sources.json` - Unified Data Sources

{
  "sources": [
    {
      "id": "openai-rss",
      "type": "rss",
      "name": "OpenAI Blog",
      "url": "https://openai.com/blog/rss.xml",
      "enabled": true,
      "priority": true,
      "topics": ["llm", "ai-agent"],
      "note": "Official OpenAI updates"
    },
    {
      "id": "sama-twitter",
      "type": "twitter", 
      "name": "Sam Altman",
      "handle": "sama",
      "enabled": true,
      "priority": true,
      "topics": ["llm", "frontier-tech"],
      "note": "OpenAI CEO"
    }
  ]
}

`topics.json` - Enhanced Topic Definitions

{
  "topics": [
    {
      "id": "llm",
      "emoji": "🧠",
      "label": "LLM / Large Models",
      "description": "Large Language Models, foundation models, breakthroughs",
      "search": {
        "queries": ["LLM latest news", "large language model breakthroughs"],
        "must_include": ["LLM", "large language model", "foundation model"],
        "exclude": ["tutorial", "beginner guide"]
      },
      "display": {
        "max_items": 8,
        "style": "detailed"
      }
    }
  ]
}

Scripts Pipeline

`run-pipeline.py` - Unified Pipeline (Recommended)

python3 scripts/run-pipeline.py \
  --defaults config/defaults [--config CONFIG_DIR] \
  --hours 48 --freshness pd \
  --archive-dir workspace/archive/tech-news-digest/ \
  --output /tmp/td-merged.json --verbose --force

Features: Runs all 6 fetch steps in parallel, then merges + deduplicates + scores
Output: Final merged JSON ready for report generation (~30s total)
Metadata: Saves per-step timing and counts to *.meta.json
GitHub Auth: Auto-generates GitHub App token if $GITHUB_TOKEN not set
Fallback: If this fails, run individual scripts below

Individual Scripts (Fallback)

`fetch-rss.py` - RSS Feed Fetcher

python3 scripts/fetch-rss.py [--defaults DIR] [--config DIR] [--hours 48] [--output FILE] [--verbose]

Parallel fetching (10 workers), retry with backoff, feedparser + regex fallback
Timeout: 30s per feed, ETag/Last-Modified caching

`fetch-twitter.py` - Twitter/X KOL Monitor

python3 scripts/fetch-twitter.py [--defaults DIR] [--config DIR] [--hours 48] [--output FILE] [--backend auto|official|twitterapiio]

Backend auto-detection: uses twitterapi.io if TWITTERAPI_IO_KEY set, else official X API v2 if X_BEARER_TOKEN set
Rate limit handling, engagement metrics, retry with backoff

`fetch-web.py` - Web Search Engine

python3 scripts/fetch-web.py [--defaults DIR] [--config DIR] [--freshness pd] [--output FILE]

Auto-detects Brave API rate limit: paid plans → parallel queries, free → sequential
Without API: generates search interface for agents

`fetch-github.py` - GitHub Releases Monitor

python3 scripts/fetch-github.py [--defaults DIR] [--config DIR] [--hours 168] [--output FILE]

Parallel fetching (10 workers), 30s timeout
Auth priority: $GITHUB_TOKEN → GitHub App auto-generate → gh CLI → unauthenticated (60 req/hr)

`fetch-github.py --trending` - GitHub Trending Repos

python3 scripts/fetch-github.py --trending [--hours 48] [--output FILE] [--verbose]

Searches GitHub API for trending repos across 4 topics (LLM, AI Agent, Crypto, Frontier Tech)
Quality scoring: base 5 + daily_stars_est / 10, max 15

`fetch-reddit.py` - Reddit Posts Fetcher

python3 scripts/fetch-reddit.py [--defaults DIR] [--config DIR] [--hours 48] [--output FILE]

Parallel fetching (4 workers), public JSON API (no auth required)
13 subreddits with score filtering

`enrich-articles.py` - Article Full-Text Enrichment

python3 scripts/enrich-articles.py --input merged.json --output enriched.json [--min-score 10] [--max-articles 15] [--verbose]

Fetches full article text for high-scoring articles
Cloudflare Markdown for Agents (preferred) → HTML extraction (fallback) → Skip (paywalled/social)
Blog domain whitelist with lower score threshold (≥3)
Parallel fetching (5 workers, 10s timeout)

`merge-sources.py` - Quality Scoring & Deduplication

python3 scripts/merge-sources.py --rss FILE --twitter FILE --web FILE --github FILE --reddit FILE

Quality scoring, title similarity dedup (85%), previous digest penalty
Output: topic-grouped articles sorted by score

`validate-config.py` - Configuration Validator

python3 scripts/validate-config.py [--defaults DIR] [--config DIR] [--verbose]

JSON schema validation, topic reference checks, duplicate ID detection

`generate-pdf.py` - PDF Report Generator

python3 scripts/generate-pdf.py --input report.md --output digest.pdf [--verbose]

Converts markdown digest to styled A4 PDF with Chinese typography (Noto Sans CJK SC)
Emoji icons, page headers/footers, blue accent theme. Requires weasyprint.

`sanitize-html.py` - Safe HTML Email Converter

python3 scripts/sanitize-html.py --input report.md --output email.html [--verbose]

Converts markdown to XSS-safe HTML email with inline CSS
URL whitelist (http/https only), HTML-escaped text content

`source-health.py` - Source Health Monitor

python3 scripts/source-health.py --rss FILE --twitter FILE --github FILE --reddit FILE --web FILE [--verbose]

Tracks per-source success/failure history over 7 days
Reports unhealthy sources (>50% failure rate)

`summarize-merged.py` - Merged Data Summary

python3 scripts/summarize-merged.py --input merged.json [--top N] [--topic TOPIC]

Human-readable summary of merged data for LLM consumption
Shows top articles per topic with scores and metrics

User Customization

Workspace Configuration Override

Place custom configs in workspace/config/ to override defaults:

Sources: Append new sources, disable defaults with "enabled": false
Topics: Override topic definitions, search queries, display settings
Merge Logic:
- Sources with same id → user version takes precedence
- Sources with new id → appended to defaults
- Topics with same id → user version completely replaces default

Example Workspace Override

// workspace/config/tech-news-digest-sources.json
{
  "sources": [
    {
      "id": "simonwillison-rss",
      "enabled": false,
      "note": "Disabled: too noisy for my use case"
    },
    {
      "id": "my-custom-blog", 
      "type": "rss",
      "name": "My Custom Tech Blog",
      "url": "https://myblog.com/rss",
      "enabled": true,
      "priority": true,
      "topics": ["frontier-tech"]
    }
  ]
}

Templates & Output

Discord Template (`references/templates/discord.md`)

Bullet list format with link suppression (<link>)
Mobile-optimized, emoji headers
2000 character limit awareness

Email Template (`references/templates/email.md`)

Rich metadata, technical stats, archive links
Executive summary, top articles section
HTML-compatible formatting

PDF Template (`references/templates/pdf.md`)

A4 layout with Noto Sans CJK SC font for Chinese support
Emoji icons, page headers/footers with page numbers
Generated via scripts/generate-pdf.py (requires weasyprint)

Default Sources (151 total)

RSS Feeds (62): AI labs, tech blogs, crypto news, Chinese tech media
Twitter/X KOLs (48): AI researchers, crypto leaders, tech executives
GitHub Repos (28): Major open-source projects (LangChain, vLLM, DeepSeek, Llama, etc.)
Reddit (13): r/MachineLearning, r/LocalLLaMA, r/CryptoCurrency, r/ChatGPT, r/OpenAI, etc.
Web Search (4 topics): LLM, AI Agent, Crypto, Frontier Tech

All sources pre-configured with appropriate topic tags and priority levels.

Dependencies

pip install -r requirements.txt

Optional but Recommended:

feedparser>=6.0.0 - Better RSS parsing (fallback to regex if unavailable)
jsonschema>=4.0.0 - Configuration validation

All scripts work with Python 3.8+ standard library only.

Monitoring & Operations

Health Checks

# Validate configuration
python3 scripts/validate-config.py --verbose

# Test RSS feeds
python3 scripts/fetch-rss.py --hours 1 --verbose

# Check Twitter API
python3 scripts/fetch-twitter.py --hours 1 --verbose

Archive Management

Digests automatically archived to <workspace>/archive/tech-news-digest/
Previous digest titles used for duplicate detection
Old archives cleaned automatically (90+ days)

Error Handling

Network Failures: Retry with exponential backoff
Rate Limits: Automatic retry with appropriate delays
Invalid Content: Graceful degradation, detailed logging
Configuration Errors: Schema validation with helpful messages

API Keys & Environment

Set in ~/.zshenv or similar:

# Twitter (at least one required for Twitter source)
export TWITTERAPI_IO_KEY="your_key"        # twitterapi.io key (preferred)
export X_BEARER_TOKEN="your_bearer_token"  # Official X API v2 (fallback)
export TWITTER_API_BACKEND="auto"          # auto|twitterapiio|official (default: auto)

# Web Search (optional, enables web search layer)
export WEB_SEARCH_BACKEND="auto"          # auto|brave|tavily (default: auto)
export TAVILY_API_KEY="tvly-xxx"           # Tavily Search API (free 1000/mo)

# Brave Search (alternative)
export BRAVE_API_KEYS="key1,key2,key3"     # Multiple keys, comma-separated rotation
export BRAVE_API_KEY="key1"                # Single key fallback
export BRAVE_PLAN="free"                   # Override rate limit detection: free|pro

# GitHub (optional, improves rate limits)
export GITHUB_TOKEN="ghp_xxx"              # PAT (simplest)
export GH_APP_ID="12345"                   # Or use GitHub App for auto-token
export GH_APP_INSTALL_ID="67890"
export GH_APP_KEY_FILE="/path/to/key.pem"

Twitter: TWITTERAPI_IO_KEY preferred ($3-5/mo); X_BEARER_TOKEN as fallback; auto mode tries twitterapiio first
Web Search: Tavily (preferred in auto mode) or Brave; optional, fallback to agent web_search if unavailable
GitHub: Auto-generates token from GitHub App if PAT not set; unauthenticated fallback (60 req/hr)
Reddit: No API key needed (uses public JSON API)

Cron / Scheduled Task Integration

OpenClaw Cron (Recommended)

The cron prompt should NOT hardcode the pipeline steps. Instead, reference references/digest-prompt.md and only pass configuration parameters. This ensures the pipeline logic stays in the skill repo and is consistent across all installations.

Daily Digest Cron Prompt

Read <SKILL_DIR>/references/digest-prompt.md and follow the complete workflow to generate a daily digest.

Replace placeholders with:
- MODE = daily
- TIME_WINDOW = past 1-2 days
- FRESHNESS = pd
- RSS_HOURS = 48
- ITEMS_PER_SECTION = 3-5
- ENRICH = true
- BLOG_PICKS_COUNT = 3
- EXTRA_SECTIONS = (none)
- SUBJECT = Daily Tech Digest - YYYY-MM-DD
- WORKSPACE = <your workspace path>
- SKILL_DIR = <your skill install path>
- DISCORD_CHANNEL_ID = <your channel id>
- EMAIL = (optional)
- LANGUAGE = English
- TEMPLATE = discord

Follow every step in the prompt template strictly. Do not skip any steps.

Weekly Digest Cron Prompt

Read <SKILL_DIR>/references/digest-prompt.md and follow the complete workflow to generate a weekly digest.

Replace placeholders with:
- MODE = weekly
- TIME_WINDOW = past 7 days
- FRESHNESS = pw
- RSS_HOURS = 168
- ITEMS_PER_SECTION = 10-15
- ENRICH = true
- BLOG_PICKS_COUNT = 3-5
- EXTRA_SECTIONS = 📊 Weekly Trend Summary (2-3 sentences summarizing macro trends)
- SUBJECT = Weekly Tech Digest - YYYY-MM-DD
- WORKSPACE = <your workspace path>
- SKILL_DIR = <your skill install path>
- DISCORD_CHANNEL_ID = <your channel id>
- EMAIL = (optional)
- LANGUAGE = English
- TEMPLATE = discord

Follow every step in the prompt template strictly. Do not skip any steps.

Why This Pattern?

Single source of truth: Pipeline logic lives in digest-prompt.md, not scattered across cron configs
Portable: Same skill on different OpenClaw instances, just change paths and channel IDs
Maintainable: Update the skill → all cron jobs pick up changes automatically
Anti-pattern: Do NOT copy pipeline steps into the cron prompt — it will drift out of sync

Multi-Channel Delivery Limitation

OpenClaw enforces cross-provider isolation: a single session can only send messages to one provider (e.g., Discord OR Telegram, not both). If you need to deliver digests to multiple platforms, create separate cron jobs for each provider:

# Job 1: Discord + Email
- DISCORD_CHANNEL_ID = <your-discord-channel-id>
- EMAIL = [email protected]
- TEMPLATE = discord

# Job 2: Telegram DM
- DISCORD_CHANNEL_ID = (none)
- EMAIL = (none)
- TEMPLATE = telegram

Replace DISCORD_CHANNEL_ID delivery with the target platform's delivery in the second job's prompt.

This is a security feature, not a bug — it prevents accidental cross-context data leakage.

Security Notes

Execution Model

This skill uses a prompt template pattern: the agent reads digest-prompt.md and follows its instructions. This is the standard OpenClaw skill execution model — the agent interprets structured instructions from skill-provided files. All instructions are shipped with the skill bundle and can be audited before installation.

Network Access

The Python scripts make outbound requests to:

RSS feed URLs (configured in tech-news-digest-sources.json)
Twitter/X API (api.x.com or api.twitterapi.io)
Brave Search API (api.search.brave.com)
Tavily Search API (api.tavily.com)
GitHub API (api.github.com)
Reddit JSON API (reddit.com)

No data is sent to any other endpoints. All API keys are read from environment variables declared in the skill metadata.

Shell Safety

Email delivery uses send-email.py which constructs proper MIME multipart messages with HTML body + optional PDF attachment. Subject formats are hardcoded (Daily Tech Digest - YYYY-MM-DD). PDF generation uses generate-pdf.py via weasyprint. The prompt template explicitly prohibits interpolating untrusted content (article titles, tweet text, etc.) into shell arguments. Email addresses and subjects must be static placeholder values only.

File Access

Scripts read from config/ and write to workspace/archive/. No files outside the workspace are accessed.

Support & Troubleshooting

Common Issues

RSS feeds failing: Check network connectivity, use --verbose for details
Twitter rate limits: Reduce sources or increase interval
Configuration errors: Run validate-config.py for specific issues
No articles found: Check time window (--hours) and source enablement

Debug Mode

All scripts support --verbose flag for detailed logging and troubleshooting.

Performance Tuning

Parallel Workers: Adjust MAX_WORKERS in scripts for your system
Timeout Settings: Increase TIMEOUT for slow networks
Article Limits: Adjust MAX_ARTICLES_PER_FEED based on needs

Security Considerations

Shell Execution

The digest prompt instructs agents to run Python scripts via shell commands. All script paths and arguments are skill-defined constants — no user input is interpolated into commands. Two scripts use subprocess:

run-pipeline.py orchestrates child fetch scripts (all within scripts/ directory)
fetch-github.py has two subprocess calls:
1. openssl dgst -sha256 -sign for JWT signing (only if GH_APP_* env vars are set — signs a self-constructed JWT payload, no user content involved)
2. gh auth token CLI fallback (only if gh is installed — reads from gh's own credential store)

No user-supplied or fetched content is ever interpolated into subprocess arguments. Email delivery uses send-email.py which builds MIME messages programmatically — no shell interpolation. PDF generation uses generate-pdf.py via weasyprint. Email subjects are static format strings only — never constructed from fetched data.

Credential & File Access

Scripts do not directly read ~/.config/, ~/.ssh/, or any credential files. All API tokens are read from environment variables declared in the skill metadata. The GitHub auth cascade is:

$GITHUB_TOKEN env var (you control what to provide)
GitHub App token generation (only if you set GH_APP_ID, GH_APP_INSTALL_ID, and GH_APP_KEY_FILE — uses inline JWT signing via openssl CLI, no external scripts involved)
gh auth token CLI (delegates to gh's own secure credential store)
Unauthenticated (60 req/hr, safe fallback)

If you prefer no automatic credential discovery, simply set $GITHUB_TOKEN and the script will use it directly without attempting steps 2-3.

Dependency Installation

This skill does not install any packages. requirements.txt lists optional dependencies (feedparser, jsonschema) for reference only. All scripts work with Python 3.8+ standard library. Users should install optional deps in a virtualenv if desired — the skill never runs pip install.

Input Sanitization

URL resolution rejects non-HTTP(S) schemes (javascript:, data:, etc.)
RSS fallback parsing uses simple, non-backtracking regex patterns (no ReDoS risk)
All fetched content is treated as untrusted data for display only

Network Access

Scripts make outbound HTTP requests to configured RSS feeds, Twitter API, GitHub API, Reddit JSON API, Brave Search API, and Tavily Search API. No inbound connections or listeners are created.

About this skill

Automatic

Marketing & Content pack

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v4 — 7/30/2026

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

LeoYeAI / openclaw-master-skills

skills/dinstein-tech-news-digest/SKILL.md

View on GitHub ↗

License MITImported 8/2/2026Version v1Bundled 17 files · scripts

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

Related skills

Skills that do similar work, with their headline benchmark lift.

From the same source and nearby in the catalogue.

Ready to supercharge your AI agents?

SkillScore

Safety

What it changes

Track record

Use this skill

Tech News Digest

Quick Start

Configuration Files

sources.json - Unified Data Sources

topics.json - Enhanced Topic Definitions

Scripts Pipeline

run-pipeline.py - Unified Pipeline (Recommended)

Individual Scripts (Fallback)

fetch-rss.py - RSS Feed Fetcher

fetch-twitter.py - Twitter/X KOL Monitor

fetch-web.py - Web Search Engine

fetch-github.py - GitHub Releases Monitor

fetch-github.py --trending - GitHub Trending Repos

fetch-reddit.py - Reddit Posts Fetcher

enrich-articles.py - Article Full-Text Enrichment

merge-sources.py - Quality Scoring & Deduplication

validate-config.py - Configuration Validator

generate-pdf.py - PDF Report Generator

sanitize-html.py - Safe HTML Email Converter

source-health.py - Source Health Monitor

summarize-merged.py - Merged Data Summary

User Customization

Workspace Configuration Override

Example Workspace Override

Templates & Output

Discord Template (references/templates/discord.md)

Email Template (references/templates/email.md)

PDF Template (references/templates/pdf.md)

Default Sources (151 total)

Dependencies

Monitoring & Operations

Health Checks

Archive Management

Error Handling

API Keys & Environment

Cron / Scheduled Task Integration

OpenClaw Cron (Recommended)

Daily Digest Cron Prompt

Weekly Digest Cron Prompt

Why This Pattern?

Multi-Channel Delivery Limitation

Security Notes

Execution Model

Network Access

Shell Safety

File Access

Support & Troubleshooting

Common Issues

Debug Mode

Performance Tuning

Security Considerations

Shell Execution

Credential & File Access

Dependency Installation

Input Sanitization

Network Access

SkillSafety

Source

Related skills

More in the registry

`sources.json` - Unified Data Sources

`topics.json` - Enhanced Topic Definitions

`run-pipeline.py` - Unified Pipeline (Recommended)

`fetch-rss.py` - RSS Feed Fetcher

`fetch-twitter.py` - Twitter/X KOL Monitor

`fetch-web.py` - Web Search Engine

`fetch-github.py` - GitHub Releases Monitor

`fetch-github.py --trending` - GitHub Trending Repos

`fetch-reddit.py` - Reddit Posts Fetcher

`enrich-articles.py` - Article Full-Text Enrichment

`merge-sources.py` - Quality Scoring & Deduplication

`validate-config.py` - Configuration Validator

`generate-pdf.py` - PDF Report Generator

`sanitize-html.py` - Safe HTML Email Converter

`source-health.py` - Source Health Monitor

`summarize-merged.py` - Merged Data Summary

Discord Template (`references/templates/discord.md`)

Email Template (`references/templates/email.md`)

PDF Template (`references/templates/pdf.md`)