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Skill: Twitter/X SaaS Opportunity Hunter · valtterimelkko | DecimalAI

Researchby valtterimelkko

Skill: Twitter/X SaaS Opportunity Hunter

Production-quality Twitter/X scraping skill for discovering SaaS opportunities. Navigate Twitter programmatically to identify unaddressed user frustrations, high-intent signals, and emerging market trends. Uses TwitterAPI.io for fast, cost-effective data extraction with simple API key authentication. Provides tactical navigation (user discovery, hashtag analysis), advanced filtering (intent patterns, sentiment scoring, velocity tracking), and idea synthesis (trend persistence, cross-pollination

Never made a case worse

SkillScore

SkillScore95/100

Test results 95

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

73%95%of test cases passed +23 pts

Without skill16 of 22

With skill21 of 22

5 fixed16 passed either way1 still failing0 broke

Measured on gemini-3.6-flash8/4/2026v121 comparable

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskIn the `OpportunitySignal` data model of the SaaS opportunity hunter toolkit, what are the four possible string values for the `priority` field?

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 valtterimelkko-scraping-twitter
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("valtterimelkko-scraping-twitter")
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 valtterimelkko-scraping-twitter

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/valtterimelkko-scraping-twitter/SKILL.md https://app.decimal.ai/s/valtterimelkko-scraping-twitter/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/valtterimelkko-scraping-twitter/SKILL.md

This gets SKILL.md only. The skill's 8 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 valtterimelkko-scraping-twitter

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("valtterimelkko-scraping-twitter")
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/valtterimelkko-scraping-twitter/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/valtterimelkko-scraping-twitter/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 8 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 valtterimelkko-scraping-twitter

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("valtterimelkko-scraping-twitter")
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/valtterimelkko-scraping-twitter/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/valtterimelkko-scraping-twitter/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 8 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 valtterimelkko-scraping-twitter

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("valtterimelkko-scraping-twitter")
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/valtterimelkko-scraping-twitter/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/valtterimelkko-scraping-twitter/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 8 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 valtterimelkko-scraping-twitter

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("valtterimelkko-scraping-twitter")
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/valtterimelkko-scraping-twitter/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/valtterimelkko-scraping-twitter/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 8 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 valtterimelkko-scraping-twitter

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("valtterimelkko-scraping-twitter")
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/valtterimelkko-scraping-twitter/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/valtterimelkko-scraping-twitter/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 8 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 valtterimelkko-scraping-twitter

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("valtterimelkko-scraping-twitter")
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/valtterimelkko-scraping-twitter/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/valtterimelkko-scraping-twitter/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 8 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 valtterimelkko-scraping-twitter

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("valtterimelkko-scraping-twitter")
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/valtterimelkko-scraping-twitter/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 8 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 valtterimelkko-scraping-twitter

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("valtterimelkko-scraping-twitter")
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/valtterimelkko-scraping-twitter/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/valtterimelkko-scraping-twitter/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 8 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 valtterimelkko-scraping-twitter

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("valtterimelkko-scraping-twitter")
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/valtterimelkko-scraping-twitter/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/valtterimelkko-scraping-twitter/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 8 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 valtterimelkko-scraping-twitter

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("valtterimelkko-scraping-twitter")
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/valtterimelkko-scraping-twitter/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/valtterimelkko-scraping-twitter/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 8 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 valtterimelkko-scraping-twitter

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("valtterimelkko-scraping-twitter")
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/valtterimelkko-scraping-twitter/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/valtterimelkko-scraping-twitter/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 8 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 valtterimelkko-scraping-twitter

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("valtterimelkko-scraping-twitter")
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/valtterimelkko-scraping-twitter/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/valtterimelkko-scraping-twitter/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 8 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 valtterimelkko-scraping-twitter

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/valtterimelkko-scraping-twitter/SKILL.md https://app.decimal.ai/s/valtterimelkko-scraping-twitter/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/valtterimelkko-scraping-twitter/SKILL.md

This gets SKILL.md only. The skill's 8 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/valtterimelkko-scraping-twitter/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/valtterimelkko-scraping-twitter.json

This gets SKILL.md only. The skill's 8 other files are not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v1, tested 8/4/2026
Source: valtterimelkko/agent-workflow-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

Skill: Twitter/X SaaS Opportunity Hunter

Production-quality Twitter/X scraping toolkit for discovering unaddressed user frustrations and emerging market trends for software development.

Overview

This skill provides a comprehensive framework for Twitter-based SaaS opportunity discovery through three distinct layers:

Navigation - Where to look (user discovery, hashtag analysis, conversation exploration)
Extraction - What to pull (high-intent patterns, sentiment scoring, velocity tracking)
Synthesis - How to interpret (trend persistence, cross-pollination, opportunity scoring)

Powered by TwitterAPI.io - no Twitter developer account required, simple API key authentication, and cost-effective pricing at ~$0.15 per 1,000 tweets.

Prerequisites

Required Credentials

The skill uses the hierarchical credential loading strategy from saas-idea-finder:

Environment variables (highest priority)
Optional vault/secret store (for service users like openclaw)
Shell startup file such as ~/.bashrc (fallback)

Environment variables:

export TWITTERAPI_KEY="your_twitterapi_key"

Optional vault/secret store setup:

vault kv put secret/skills-apis/twitterapi/api_key value="your_api_key"

Getting a TwitterAPI.io Key

Go to https://twitterapi.io/
Sign up for an account (no credit card required for free tier)
Copy your API key from the dashboard
Add to environment or vault as shown above

Rate Limits & Pricing

User Type	Rate Limit	Cost
Free Trial	1 req / 5 seconds	$0.10 free credit
Paid	20+ QPS	$0.15 per 1,000 tweets
Enterprise	1000+ QPS	Custom pricing

Quick Start

Basic Usage

import asyncio
from scripts import TwitterScraper, OpportunityAnalyzer

async def main():
    # Initialize scraper (auto-loads credentials)
    scraper = TwitterScraper()
    
    # Search tweets
    tweets, pagination = await scraper.search_tweets("#buildinpublic", limit=50)
    
    # Analyze for opportunities
    analyzer = OpportunityAnalyzer()
    for tweet in tweets:
        analysis = analyzer.analyze_tweet(tweet)
        if analysis["intent_signals"]:
            print(f"💡 Opportunity: {tweet.text[:100]}...")
            print(f"   Problem: {analysis['problem_statement']}")
    
    await scraper.close()

asyncio.run(main())

High-Level Synthesis

from scripts import TwitterScraper, IdeaSynthesizer

async def main():
    scraper = TwitterScraper()
    synthesizer = IdeaSynthesizer(scraper)
    
    # Comprehensive analysis across hashtags
    report = await synthesizer.analyze_opportunity(
        hashtags=["buildinpublic", "indiehacker", "SaaS"],
        keywords=["automation", "AI", "workflow"],
        known_competitors=["Zapier", "Notion", "Airtable"],
        min_opportunity_score=60
    )
    
    print(f"Found {report['summary']['high_opportunity_count']} high-opportunity signals")
    print(f"Average score: {report['summary']['average_opportunity_score']}")
    
    for opp in report['opportunities'][:5]:
        signal = opp['opportunity']
        print(f"\n🎯 {signal['opportunity_score']}/100 - {signal['priority'].upper()}")
        print(f"   Problem: {signal['problem_statement'][:100]}...")
        print(f"   Action: {signal['recommended_action']}")
    
    await scraper.close()

Tactical Navigation Functions

Find Relevant Users

from scripts import TwitterScraper, TwitterNavigator

scraper = TwitterScraper()
navigator = TwitterNavigator(scraper)

# Find influencers in your target niche
users = await navigator.find_relevant_users(
    niche_keyword="ecommerce",
    limit=10,
    min_followers=5000
)

for user in users:
    print(f"@{user['user'].username}: {user['user'].followers:,} followers")
    print(f"   Relevance: {user['relevance_score']:.1f}")
    print(f"   Influence: {user['influence_score']:.1f}")

Analyze Hashtags

# Discover relevant hashtags and their metrics
hashtags = await navigator.get_relevant_hashtags(
    niche_keyword="productivity",
    limit=10
)

for hashtag in hashtags:
    print(f"#{hashtag.hashtag}:")
    print(f"   Tweets: {hashtag.tweet_count}")
    print(f"   Unique authors: {hashtag.unique_authors}")
    print(f"   Velocity: {hashtag.velocity} tweets/hour")
    print(f"   Related: {', '.join(hashtag.related_hashtags[:5])}")

Identify Top Contributors

# Find "power users" who spark high-engagement conversations
contributors = await navigator.identify_top_contributors(
    hashtag="buildinpublic",
    limit=15,
    activity_lookback=100
)

for c in contributors[:5]:
    print(f"@{c['username']}: {c['tweet_count']} tweets")
    print(f"   Avg engagement: {c['avg_engagement_per_tweet']}")
    print(f"   Quality score: {c['quality_score']}")

Explore Conversation Threads

# Deep dive into a conversation thread
thread = await navigator.explore_conversation_thread(
    tweet_id="1234567890",
    max_depth=3
)

print(f"Thread tweets: {len(thread.tweets)}")
print(f"Total replies: {len(thread.replies)}")
print(f"Unique authors: {thread.unique_authors}")
print(f"Total engagement: {thread.total_engagement}")

Advanced Filtering Capabilities

Intent Pattern Matching

from scripts.filters import IntentFilter, filter_high_intent_tweets

# Filter for high-intent phrases
intent_filter = IntentFilter()
filtered_tweets = intent_filter.filter(tweets)

# Or use the convenience function
high_intent = filter_high_intent_tweets(tweets, min_confidence=0.3)

for tweet in high_intent:
    matches = tweet.metadata.get("intent_matches", [])
    print(f"🎯 {tweet.text[:80]}...")
    print(f"   Signals: {', '.join(matches)}")

The "Negative Search" (Churn Signals)

from scripts.filters import NegativeSearchFilter, find_churn_signals

# Find people quitting products (ready-to-churn users)
churn_signals = find_churn_signals(
    tweets,
    target_products=["Salesforce", "HubSpot"]  # Optional: focus on specific products
)

for signal in churn_signals:
    print(f"⚠️  {signal['churn_type'].upper()}: {signal['tweet'].text[:80]}...")
    if signal['product']:
        print(f"   Product: {signal['product']}")
    print(f"   Context: {signal['context'][:150]}...")

Velocity Tracking

from scripts.filters import VelocityFilter, find_controversial_tweets

# Find tweets with high engagement velocity
velocity_filter = VelocityFilter(
    min_likes_per_hour=1.0,
    min_replies_per_hour=0.1,
    min_engagement_ratio=0.05
)
hot_tweets = velocity_filter.filter_by_velocity(tweets)

# High reply-to-like ratio = controversial/painful
controversial = find_controversial_tweets(tweets, min_ratio=0.3)

for tweet, ratio in controversial[:5]:
    print(f"🔥 {ratio:.2f} ratio: {tweet.text[:80]}...")
    print(f"   {tweet.reply_count} replies / {tweet.engagement} likes")

Competitor Mention Extraction

from scripts.filters import CompetitorFilter

# Track mentions of known competitors
competitor_filter = CompetitorFilter(
    known_competitors=["Salesforce", "HubSpot", "Zapier"]
)

mentions = competitor_filter.extract_mentions(tweets)
for brand, mentions_list in mentions.items():
    print(f"📊 {brand}: {len(mentions_list)} mentions")
    for m in mentions_list[:3]:
        print(f"   {m['mention_type']} - {m['intent']}")

# Find feature gaps
gaps = competitor_filter.find_competitor_gaps(tweets)
for gap in gaps[:5]:
    print(f"🕳️  Gap: {gap['gap_description']}")
    print(f"   Confidence: {gap['confidence']}")

Hashtag Filtering

from scripts.filters import HashtagFilter, filter_by_hashtags

# Filter tweets by specific hashtags
hashtag_filter = HashtagFilter(["buildinpublic", "indiehacker"])
filtered = hashtag_filter.filter(tweets)

# Or use the convenience function
filtered = filter_by_hashtags(tweets, ["saas", "b2b"])

# Analyze hashtag usage
analysis = hashtag_filter.analyze_hashtags(tweets)
for hashtag, stats in analysis.items():
    print(f"#{hashtag}: {stats['tweet_count']} tweets, "
          f"{stats['unique_authors']} authors, "
          f"avg {stats['avg_engagement']:.1f} engagement")

Idea Synthesis

Trend Persistence Check

from scripts import TrendAnalyzer

trend_analyzer = TrendAnalyzer(scraper)

# Check if a problem is a "fad" or "chronic pain point"
report = await trend_analyzer.check_trend_persistence(
    keyword="inventory management",
    hashtags=["ecommerce", "shopify"],
    days=30
)

print(f"Keyword: {report.keyword}")
print(f"Is persistent: {report.is_persistent}")
print(f"7-day mentions: {report.mention_count_7d}")
print(f"30-day mentions: {report.mention_count_30d}")
print(f"Growth rate: {report.growth_rate:+.1f}%")
print(f"Prediction: {report.prediction}")

Cross-Pollination Detection

from scripts import CrossPollinationAnalyzer

cross_analyzer = CrossPollinationAnalyzer(scraper)

# Find similar problems across different niches
report = await cross_analyzer.find_cross_pollination(
    hashtag_a="EtsySeller",
    hashtag_b="AmazonFBA"
)

print(f"Similarity score: {report['similarity_score']:.2f}")
print(f"Opportunity type: {report['opportunity_type']}")
print(f"Shared keywords: {', '.join(report['shared_keywords'][:10])}")

# Pain points in both communities
for pain in report['pain_points_a'][:5]:
    print(f"  A: {pain}")
for pain in report['pain_points_b'][:5]:
    print(f"  B: {pain}")

Comprehensive Opportunity Analysis

from scripts import IdeaSynthesizer

synthesizer = IdeaSynthesizer(scraper)

# Full synthesis pipeline
report = await synthesizer.analyze_opportunity(
    hashtags=["buildinpublic", "indiehacker", "SaaS"],
    keywords=["automation", "AI", "workflow"],
    known_competitors=["Zapier", "Make", "n8n"],
    min_opportunity_score=65
)

summary = report['summary']
print(f"Priority distribution: {summary['priority_distribution']}")
print(f"Avg opportunity score: {summary['average_opportunity_score']}")
print(f"Avg frustration score: {summary['average_frustration_score']}")

print("\nTop suggested features:")
for feature, count in summary['top_suggested_features'][:5]:
    print(f"  • {feature} ({count} mentions)")

print(f"\nRecommendation: {summary['recommendation']}")

Data Models

ScrapedTweet

@dataclass
class ScrapedTweet:
    id: str
    platform: str = "twitter"
    text: str = ""
    url: str = ""
    author_username: str = ""
    author_name: str = ""
    author_verified: bool = False
    author_followers: int = 0
    engagement: int = 0           # Like count
    reply_count: int = 0
    retweet_count: int = 0
    quote_count: int = 0
    view_count: Optional[int] = None
    created_at: str = ""
    language: str = "en"
    metadata: Dict[str, Any] = field(default_factory=dict)
    
    # Computed fields
    pain_score: float = 0.0
    opportunity_score: float = 0.0
    is_high_opportunity: bool = False
    
    @property
    def total_engagement(self) -> int:
        """Calculate total engagement across all metrics."""
        return self.engagement + self.reply_count + self.retweet_count + self.quote_count

OpportunitySignal

@dataclass
class OpportunitySignal:
    opportunity_score: float      # 0-100
    priority: str                 # high, medium, low, monitor
    intent_score: float
    frustration_score: float
    velocity_score: float
    trend_score: float
    monetization_score: float
    problem_statement: str
    target_audience: str
    suggested_features: List[str]
    tech_stack_hints: List[str]
    competitor_gaps: List[str]
    recommended_action: str

Testing

Run Tests

# All tests
python3 tests/run_tests.py

# Or directly
python3 tests/test_scraper.py

Test Coverage

Tests validate:

Dataclass creation: All models instantiate correctly
Analysis functions: Intent detection, frustration scoring, monetization signals
Filter functions: Intent filtering, churn detection
Credential loading: Hierarchy works correctly

Pro Tips

The "Negative Search"

Don't just search for what people want. Search for "I'm quitting [Product]":

# This finds ready-to-churn users and tells you what NOT to build
churn_signals = find_churn_signals(tweets, target_products=["CompetitorX"])

for signal in churn_signals:
    print(f"Why they're leaving: {signal['context']}")
    # Build the OPPOSITE of these complaints

High Reply-to-Like Ratio

Tweets with high replies relative to likes often indicate:

Controversial topics
Deeply felt problems
Active pain points

controversial = find_controversial_tweets(tweets, min_ratio=0.3)
# These are goldmines for SaaS opportunities

Cross-Hashtag Validation

If the same problem exists in #EtsySeller AND #AmazonFBA:

It's likely a universal pain point
Solution can potentially serve both markets
Higher total addressable market

report = await cross_analyzer.find_cross_pollination("EtsySeller", "AmazonFBA")
if report['similarity_score'] > 0.3:
    print("Cross-niche opportunity detected!")

Advanced Search Operators

TwitterAPI.io supports Twitter's advanced search operators:

# Search by user
tweets, _ = await scraper.search_tweets("from:elonmusk", limit=50)

# Search by mention
tweets, _ = await scraper.search_tweets("@twitter", limit=50)

# Search with minimum engagement
tweets, _ = await scraper.search_tweets("python min_retweets:10", limit=50)

# Exclude replies
tweets, _ = await scraper.search_tweets("saas -filter:replies", limit=50)

# Date range
tweets, _ = await scraper.search_tweets("startup since:2025-01-01", limit=50)

# Language filter
tweets, _ = await scraper.search_tweets("programming lang:en", limit=50)

Error Handling

The scraper implements multiple resilience patterns:

Circuit Breaker: Prevents cascading failures after 3 consecutive errors
Exponential Backoff: 2^attempt seconds between retries
User-Agent Rotation: 5 realistic browser UAs
Rate Limit Handling: Automatic delay adjustment based on 429 responses

Troubleshooting

"No API key configured" Error

Set up your credentials:

# Environment variable
export TWITTERAPI_KEY="your_api_key"

# Or vault
vault kv put secret/skills-apis/twitterapi/api_key value="your_api_key"

Rate Limiting (429 Errors)

The scraper automatically handles 429s with exponential backoff. To reduce frequency:

Upgrade to paid TwitterAPI.io tier for higher QPS
Increase delay between requests (modify _min_delay in scraper)

Empty Results

If you get empty results:

Check if the hashtag/keyword actually exists
Try broader search terms
Check your API key is valid and has credits

Architecture

skills-global/scraping-twitter/
├── scripts/
│   ├── twitter_scraper.py   # Core scraper with TwitterAPI.io
│   ├── analysis.py          # Pain point & opportunity analysis
│   ├── navigation.py        # User/hashtag discovery
│   ├── synthesis.py         # Trend & cross-pollination analysis
│   ├── filters.py           # Semantic filtering
│   ├── credentials.py       # Credential loading (saas-idea-finder pattern)
│   └── utils.py             # Dataclasses & utilities
├── tests/
│   ├── test_scraper.py      # Scraper tests
│   └── run_tests.py         # Test runner
└── SKILL.md                 # This file

References

TwitterAPI.io documentation: https://twitterapi.io/
Credential loading follows saas-idea-finder pattern
Pricing: $0.15 per 1,000 tweets

License

This skill follows the same patterns and robustness standards as saas-idea-finder and scraping-reddit.

About this skill

Automatic

Product Management pack

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v4 — 8/1/2026

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

valtterimelkko / agent-workflow-skills

skills/scraping-twitter/SKILL.md

View on GitHub ↗

License MITImported 8/1/2026Version v1Bundled 8 files · scripts

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

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valtterimelkko/scraping-redditProduction-quality Reddit scraping skill for discovering SaaS opportunities. Navigate Reddit programmatically to identify unaddressed user frustrations, high-intent signals, and emerging market trends. Uses Reddit's legacy .json endpoints with OAuth2 support, circuit breaker patterns, and comprehensive error handling. Provides tactical navigation (subreddit discovery, wiki scanning, contributor analysis), advanced filtering (intent patterns, sentiment scoring, velocity tracking), and idea syntheClose match+41 pts pass-rate lift

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Use this skill

Skill: Twitter/X SaaS Opportunity Hunter

Overview

Prerequisites

Required Credentials

Getting a TwitterAPI.io Key

Rate Limits & Pricing

Quick Start

Basic Usage

High-Level Synthesis

Tactical Navigation Functions

Find Relevant Users

Analyze Hashtags

Identify Top Contributors

Explore Conversation Threads

Advanced Filtering Capabilities

Intent Pattern Matching

The "Negative Search" (Churn Signals)

Velocity Tracking

Competitor Mention Extraction

Hashtag Filtering

Idea Synthesis

Trend Persistence Check

Cross-Pollination Detection

Comprehensive Opportunity Analysis

Data Models

ScrapedTweet

OpportunitySignal

Testing

Run Tests

Test Coverage

Pro Tips

The "Negative Search"

High Reply-to-Like Ratio

Cross-Hashtag Validation

Advanced Search Operators

Error Handling

Troubleshooting

"No API key configured" Error

Rate Limiting (429 Errors)

Empty Results

Architecture

References

License

SkillSafety

Source

Other skills for the same job

Related skills

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