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SaaS Idea Finder · valtterimelkko | DecimalAI

Researchby valtterimelkko

SaaS Idea Finder

Uses rotating lenses (7 different daily perspectives) to discover trending topics from HackerNews, HN Algolia, DEV.to, GitHub, arXiv, Stack Exchange, GDELT, SerpAPI, YouTube, Reddit, Twitter/X, and Instagram. Generates actionable Micro-SaaS ideas using deep research with viability scoring (0-100), enhanced templates, and JTBD framework. Daily automated discovery with deduplication.

SkillScore

SkillScore73/100

Test results 73

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

14%73%of test cases passed +59 pts

Without skill3 of 22

With skill16 of 22

13 fixed3 passed either way6 still failing0 broke

Measured on gemini-3.6-flash8/4/2026v119 comparable

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskWhat domain focus and social tags are assigned for Friday scans in the daily rotating lens schedule?

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-saas-idea-finder
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-saas-idea-finder")
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-saas-idea-finder

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-saas-idea-finder/SKILL.md https://app.decimal.ai/s/valtterimelkko-saas-idea-finder/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/valtterimelkko-saas-idea-finder/SKILL.md

This gets SKILL.md only. The skill's 4 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-saas-idea-finder

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-saas-idea-finder")
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-saas-idea-finder/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-saas-idea-finder/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 4 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-saas-idea-finder

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-saas-idea-finder")
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-saas-idea-finder/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/valtterimelkko-saas-idea-finder/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 4 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-saas-idea-finder

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-saas-idea-finder")
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-saas-idea-finder/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/valtterimelkko-saas-idea-finder/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 4 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-saas-idea-finder

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-saas-idea-finder")
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-saas-idea-finder/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/valtterimelkko-saas-idea-finder/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 4 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-saas-idea-finder

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-saas-idea-finder")
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-saas-idea-finder/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-saas-idea-finder/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 4 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-saas-idea-finder

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-saas-idea-finder")
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-saas-idea-finder/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/valtterimelkko-saas-idea-finder/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 4 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-saas-idea-finder

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-saas-idea-finder")
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-saas-idea-finder/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 4 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-saas-idea-finder

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-saas-idea-finder")
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-saas-idea-finder/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/valtterimelkko-saas-idea-finder/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 4 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-saas-idea-finder

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-saas-idea-finder")
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-saas-idea-finder/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/valtterimelkko-saas-idea-finder/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 4 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-saas-idea-finder

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-saas-idea-finder")
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-saas-idea-finder/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/valtterimelkko-saas-idea-finder/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 4 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-saas-idea-finder

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-saas-idea-finder")
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-saas-idea-finder/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/valtterimelkko-saas-idea-finder/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 4 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-saas-idea-finder

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-saas-idea-finder")
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-saas-idea-finder/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/valtterimelkko-saas-idea-finder/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 4 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-saas-idea-finder

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-saas-idea-finder/SKILL.md https://app.decimal.ai/s/valtterimelkko-saas-idea-finder/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/valtterimelkko-saas-idea-finder/SKILL.md

This gets SKILL.md only. The skill's 4 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-saas-idea-finder/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/valtterimelkko-saas-idea-finder.json

This gets SKILL.md only. The skill's 4 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

SaaS Idea Finder

Automatically discover trending topics from multiple sources and generate actionable Micro-SaaS ideas based on real research and market signals.

Overview

This skill implements a 3-stage pipeline that:

Discovers trends from HackerNews, HN Algolia Search, DEV.to, GitHub, arXiv, Stack Exchange, GDELT, SerpAPI, YouTube, Reddit (self-hosted), and social media (Twitter/X, Instagram via Xpoz MCP)
Researches topics using GPT Researcher with comprehensive web analysis
Generates SaaS ideas using AI based on researched pain points and opportunities, with viability scoring (0-100)

Uses a rotating lens system (7 daily perspectives) to ensure fresh, diverse ideas with automatic deduplication.

Pipeline Timing Expectations

Total Pipeline Duration: ~3-6 minutes per topic

Stage	Component	Expected Time	Notes
Stage 1: Discovery		~30-60s
	HackerNews	2-3s	Fast, reliable
	GitHub	3-5s	Depends on API response
	DEV.to	2-3s	Usually fast
	HN Algolia	2-3s	Fast
	NewsAPI	2-3s	Fast
	Product Hunt	2-3s	Fast
	arXiv	2-3s	Fast
	Stack Exchange	2-3s	Fast
	GDELT	2-3s	May timeout occasionally
	SerpAPI	2-3s	Optional, may be skipped
	YouTube	2-3s	Fast
	Reddit	~18-25s	Sequential fetches with rate limiting (4 subreddits × ~5s)
	Twitter/X	~15-25s	8 parallel queries with 15s timeout each
Stage 2: Research	GPT Researcher	~2-4 min	Web scraping + LLM analysis
Stage 3: Synthesis	Idea Generation	~30-60s	LLM idea generation + viability scoring

Factors that increase time:

Slower API responses (TwitterAPI.io, Reddit)
Rate limiting (Reddit: ~6s delay between requests)
Network latency
Complex research topics requiring more web scraping

Factors that decrease time:

Cached/credential failures (skips source quickly)
GDELT timeout (fails fast, ~2s)
SerpAPI not configured (skipped)

Maximum acceptable time: 10 minutes (with 600s timeout buffer)

If pipeline exceeds 10 minutes, it likely indicates:

Network connectivity issues
API service degradation
Hanging connection (should be rare with timeout fixes)

Prerequisites

Required API Keys

This skill uses the shared credential loading system with the following hierarchy:

Environment variables (highest priority)
HashiCorp Vault (for service users like openclaw)
a shell startup file such as ~/.bashrc

Example shell startup file setup:

export GITHUB_TOKEN="ghp_your_token"
export OPENROUTER_API_KEY="sk-or-v1-your_key"
export STACK_KEY="your_stack_key"
export SERPAPI_KEY="your_serpapi_key"
export YOUTUBE_API_KEY="your_youtube_key"
export TWITTERAPI_KEY="your_twitterapi_key"

For openclaw service user (add to vault):

# GitHub token (optional, only for GitHub trend discovery)
vault kv put secret/skills-apis/github token="ghp_your_token"

# OpenRouter API key (required for research and idea generation)
vault kv put secret/skills-apis/openrouter api_key="sk-or-v1-your_key"

# Stack Exchange API (for Stack Overflow pain point discovery)
vault kv put secret/skills-apis/stackexchange value="your_stack_key"

# SerpAPI (for search validation and keyword trends)
vault kv put secret/skills-apis/serpapi value="your_serpapi_key"

# YouTube Data API (for content gap analysis)
vault kv put secret/skills-apis/youtube value="your_youtube_key"

# TwitterAPI.io (for Twitter/X trend discovery)
vault kv put secret/skills-apis/twitterapi/api_key value="your_twitterapi_key"

Environment variables (for testing or CI/CD):

export GITHUB_TOKEN="ghp_your_token"
export OPENROUTER_API_KEY="sk-or-v1-your_key"
export STACK_KEY="your_stack_key"
export SERPAPI_KEY="your_serpapi_key"
export YOUTUBE_API_KEY="your_youtube_key"

Free APIs (No Keys Needed)

HackerNews API (https://hacker-news.firebaseio.com)
HN Algolia Search API (https://hn.algolia.com/api)
DEV.to Forem API (https://developers.forem.com/api)
arXiv API (https://export.arxiv.org/api) - Unlimited requests
GDELT API (https://api.gdeltproject.org) - Unlimited requests

Rate-Limited APIs (Free Tiers)

API	Free Tier	Key Required
Stack Exchange	10,000 requests/day	STACK_KEY
arXiv	Unlimited	No key required
GDELT	Unlimited	No key required
SerpAPI	250 searches/month	SERPAPI_KEY
YouTube Data API	10,000 units/day	YOUTUBE_API_KEY
TwitterAPI.io	$0.10-$1.00 free credits	TWITTERAPI_KEY
Xpoz Social	5,000 credits (one-time)	Xpoz OAuth (Instagram only)

TwitterAPI.io Credit Information:

Free tier: $0.10-$1.00 free credits upon signup (no credit card)
Pricing: $0.15 per 1,000 tweets
Rate limits: 1,000+ requests per second
Get your API key at: https://twitterapi.io

Xpoz Credit Information (Instagram only):

Free tier: 5,000 ONE-TIME credits (NOT monthly, NOT 100K)
Credits do NOT refresh - once used, they're gone
Credit formula: Credits = (Queries × 5) + (Results × 0.005)
Pro tier: $20/month for 30,000 credits/month
Max tier: $200/month for 600,000 credits/month

Note: Reddit and Twitter/X are now self-hosted/managed API and do NOT use Xpoz credits. Only Instagram requires Xpoz.

Social Media Sources

The skill includes social media trend detection from:

Platform	Source	Authentication	Rate Limit
Reddit	Self-hosted JSON API	OAuth2 (recommended) or None	100/min with OAuth2, 10/min without
Twitter/X	TwitterAPI.io	TWITTERAPI_KEY	1,000+ req/sec
Instagram	Xpoz MCP	Xpoz OAuth (optional)	Via Xpoz credits

Reddit: Uses Reddit's public .json endpoints with automatic rate limiting and User-Agent rotation.

Without OAuth2: 10 requests/minute, may be blocked in some environments
With OAuth2: 100 requests/minute, more reliable access

Twitter/X: Uses TwitterAPI.io direct REST API. Fast, cost-effective ($0.15/1k tweets).

Sign up at https://twitterapi.io
Get your API key from the dashboard
No Twitter developer account required

Instagram: Optional Xpoz MCP integration (requires Xpoz credits).

Reddit OAuth2 Setup (Recommended)

For reliable Reddit scraping, set up OAuth2:

Go to https://www.reddit.com/prefs/apps
Click "create another app..."
Select "script" type
Name: SaaSIdeaFinder (or any name)
Redirect URI: http://localhost:8080 (not used but required)
Copy the Client ID (under the app name) and Client Secret

Set environment variables:

export REDDIT_CLIENT_ID="your_client_id"
export REDDIT_CLIENT_SECRET="your_client_secret"

Or add to vault:

vault kv put secret/skills-apis/reddit client_id="your_id" client_secret="your_secret"

TwitterAPI.io Setup (for Twitter/X)

To enable Twitter/X scraping via TwitterAPI.io:

Go to https://twitterapi.io/
Sign up for an account (no credit card required)
Copy your API key from the dashboard
Add to environment:
```
export TWITTERAPI_KEY="your_api_key"
```

Or add to vault:

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

Xpoz Authentication (Optional - for Instagram only)

To enable Instagram scraping via Xpoz:

Go to https://www.xpoz.ai/
Sign in with Google
Copy your API token
Save to ~/.xpoz/token.txt (or vault: secret/skills-apis/xpoz/bearer_token)

See ~/.xpoz/SETUP_INSTRUCTIONS.md for detailed steps.

Note: The skill works without Xpoz - Reddit and Twitter/X are always available via self-hosted/managed APIs.

Social Media Lens Configuration

Each lens includes social platform configurations:

Lens	Reddit	Twitter	Instagram
DevTools & AI	webdev, programming, MachineLearning	#buildinpublic, #AIdev, #devtools	codelife, developer, aitools
Business SaaS	Flipping, Etsy, AmazonFBA	#reseller, #ecommerce, #smallbusiness	etsyshop, smallbusinessowner
AI/ML	MachineLearning, ChatGPT, LocalLLaMA	#AI, #LLM, #ChatGPT	aitools, artificialintelligence
Data & Analytics	dataengineering, datascience	#dataviz, #analytics	datavisualization, analytics
Productivity	devops, nocode, selfhosted	#automation, #nocode	automation, productivity
Design & Frontend	webdev, reactjs, css	#frontend, #webdesign	webdesign, uidesign
Infrastructure	devops, kubernetes, docker	#DevOps, #Kubernetes	devops, cloudcomputing

Quick Start

Run Full Daily Pipeline

cd ./skills/saas-idea-finder
python3 scripts/run_full_pipeline.py

This automatically:

Uses today's lens (Monday = Developer Tools, Tuesday = Business SaaS, etc.)
Discovers trending topics from 9+ sources
Intelligently filters for SaaS opportunities (removes open-source lists, tutorials, etc.)
Deduplicates similar topics across sources
Deep-researches the top topic (~1-2 minutes)
Generates Micro-SaaS ideas with viability scoring (0-100)
Saves outputs to ~/.saas-idea-finder/outputs/

Advanced Options

# Generate more ideas per topic
python3 scripts/run_full_pipeline.py --topics 2

# Include detailed viability analysis
python3 scripts/run_full_pipeline.py --include-viability

# Filter by minimum viability score
python3 scripts/run_full_pipeline.py --min-viability 60

# Filter by pain threshold
python3 scripts/run_full_pipeline.py --pain-threshold 50

### Advanced Options

```bash
# Run with viability scoring (enabled by default)
python3 scripts/run_full_pipeline.py --include-viability

# Filter by pain threshold (minimum pain score 0-100)
python3 scripts/run_full_pipeline.py --pain-threshold 60

# Filter by minimum viability score (0-100)
python3 scripts/run_full_pipeline.py --min-viability 70

# Combine filters for high-quality ideas only
python3 scripts/run_full_pipeline.py --pain-threshold 70 --min-viability 75

How It Works

Smart Topic Discovery

The skill uses an intelligent topic extraction system that:

Filters non-SaaS topics: Automatically removes open-source lists ("awesome-X"), tutorials, courses, free books, and other non-commercial content
Deduplicates: Merges similar topics (e.g., "local-first software" and "software: local first")
Cross-source validation: Prefers topics appearing in multiple sources
Smart scoring: Weights engagement metrics differently per source

Topics are excluded if they contain keywords like:

free, open source, tutorial, course, book, roadmap
awesome-list, cheatsheet, guide, curated
High-star repos without monetization indicators

Rotating Lens System

7 lenses rotate daily (Monday-Sunday), each focusing on different domains:

Day	Lens	Focus
Mon	Developer Tools	CLI tools, dev productivity
Tue	Business SaaS	B2B solutions for SMBs
Wed	AI/ML	AI-powered tools, LLMs
Thu	Data & Analytics	BI, dashboards
Fri	Productivity & Automation	Workflow tools, no-code
Sat	Design & Frontend	UI components, design systems
Sun	Infrastructure & DevOps	Cloud, Kubernetes, monitoring

3-Stage Pipeline

Stage 1: Trend Discovery - Aggregates from:

HackerNews: Top stories, Show HN, Ask HN (55+ posts)
HN Algolia: Keyword-based search for lens-specific topics (15-20 stories)
DEV.to: Trending articles from developer community (20-30 posts)
GitHub: Trending repositories (30 repos, requires token)
arXiv: Research trends and emerging tech papers (recent submissions)
Stack Exchange: Unanswered questions = pain points (high-engagement questions)
GDELT: Global news events and technology trends (breaking tech news)
SerpAPI: Search validation and keyword trends (Google search insights)
YouTube: Content gaps and trending tech videos (tech tutorial analysis)
Reddit (Self-Hosted): Subreddit-specific pain points via public JSON API
- No authentication required
- Automatic rate limiting and User-Agent rotation
- Real pain point detection from discussions
Twitter/X (TwitterAPI.io): Hashtag-based trend discovery
- Direct REST API integration
- Fast, cost-effective data extraction
- Pain point detection from tweets
Instagram (Xpoz) (Optional): Engagement-based trend discovery
- Cross-platform validation for trending topics
- Requires Xpoz credits

Stage 2: Deep Investigation - GPT Researcher analyzes with 40+ sources

Stage 3: SaaS Synthesis - AI generates 3-5 structured Micro-SaaS ideas with:

Viability scoring (0-100)
Pain point severity scoring (0-100)
JTBD (Jobs-to-be-Done) framework analysis
Market sizing (TAM/SAM/SOM estimates)
Competition analysis
Unit economics projections

Viability Scoring (0-100)

Each generated SaaS idea receives a viability score based on 8 weighted categories:

Category	Weight	Description
Market Size	15%	TAM/SAM/SOM analysis and growth potential
Competition	15%	Competitive landscape and differentiation
Technical Feasibility	15%	Implementation complexity and tech stack requirements
Pain Severity	15%	How critical is the problem being solved
Monetization Potential	15%	Revenue model viability and pricing power
Time to MVP	10%	Speed of initial product launch
Network Effects	10%	Potential for viral growth and user lock-in
Regulatory Risk	5%	Compliance requirements and legal barriers

Score Interpretation:

90-100: Exceptional opportunity, low risk
80-89: Strong viability, proceed with confidence
70-79: Good potential, address flagged concerns
60-69: Moderate, requires significant refinement
50-59: Weak viability, major issues to resolve
<50: Not recommended without fundamental changes

Enhanced Idea Template

Each SaaS idea now includes comprehensive business analysis:

### Idea: [Name]

**Viability Score:** XX/100

**One-Liner:** [Clear value proposition]

**Target Users:** [Specific user personas]

**Jobs-to-be-Done (JTBD):**
- When [situation], I want to [motivation], so I can [outcome]

**Pain Point Severity:** XX/100
- Frequency: [How often the pain occurs]
- Intensity: [How severe the pain is]
- Willingness to Pay: [User payment intent]

**Proposed Solution:** [Feature overview]

**Market Size:**
- TAM: $X (Total Addressable Market)
- SAM: $X (Serviceable Addressable Market)
- SOM: $X (Serviceable Obtainable Market)

**Competition:**
| Competitor | Strengths | Weaknesses | Our Differentiation |

**Unit Economics:**
- CAC: $X (Customer Acquisition Cost)
- LTV: $X (Lifetime Value)
- LTV:CAC Ratio: X:1
- Payback Period: X months
- Target MRR: $X

**Red Flags:**
- ⚠️ [Potential risk or concern]

**Success Boosters:**
- ✅ [Factor that increases success probability]

**Validation Steps:**
1. [First validation experiment]
2. [Second validation experiment]

Jobs-to-be-Done Framework

Each idea includes JTBD statements following the Clayton Christensen framework:

"When [situation], I want to [motivation], so I can [outcome]"

Example JTBD for a Developer Tool:

When I'm reviewing PRs with 50+ files, I want to auto-group changes by logic, so I can focus on architectural issues instead of scrolling

Why JTBD Matters:

Focuses on user motivation, not just features
Identifies the "job" users "hire" a product to do
Enables better positioning and messaging
Guides feature prioritization

Pain Point Severity Scoring

Each identified pain point receives a severity score (0-100) based on:

Factor	Weight	Description
Frequency	30%	How often users encounter this pain
Intensity	40%	How severely it impacts their work/life
Willingness to Pay	30%	How much they'd pay to solve it

Severity Levels:

90-100: Critical pain, immediate action required
80-89: High severity, actively seeking solutions
70-79: Moderate-high, frequent complaints
60-69: Moderate, occasional frustration
50-59: Low-moderate, tolerable inconvenience
<50: Minor annoyance, low priority

Red Flags & Success Boosters

Auto-Detection Features

The system automatically identifies factors that impact idea viability:

Red Flags (Risk Indicators):

High competition with low differentiation
Regulatory complexity or compliance barriers
Long sales cycles for B2B ideas
Technical complexity requiring specialized expertise
Low willingness to pay relative to CAC
Network effects required for basic functionality

Success Boosters (Positive Indicators):

Clear, specific target user segment
Existing budget for the problem category
Growing market with tailwinds
Product-led growth potential
Integration with existing workflows
Recurring revenue model fit

Individual Stage Usage

Discover Trends

python3 scripts/discover_trends.py --lens "AI/ML Wednesday" --limit 5

Investigate Topic

python3 scripts/investigate_topic.py --topic "local-first software" --lens "Developer Tools Monday"

Generate SaaS Ideas

python3 scripts/synthesize_saas_ideas.py --research path/to/research.md --num-ideas 5

Output Locations

~/.saas-idea-finder/outputs/
├── 2026-02-08_discover/trends.json
├── 2026-02-08_research/topic.md
└── 2026-02-08_ideas/topic_ideas.md

Error Handling

Missing API Keys: Add to your environment, shell startup file, or secret store
All Sources Failed: Check internet, try --since 14
Already Analyzed: Use --force to re-analyze
Rate Limits Exceeded: Wait and retry, or check API quotas

All errors return JSON: {"success": false, "error": "type", "message": "solution"}

Usage Examples

Daily Automated

# Crontab: Run every day at 9 AM
0 9 * * * cd /path/to/skills/saas-idea-finder && python3 scripts/run_full_pipeline.py

Ad-Hoc Research

python3 scripts/run_full_pipeline.py --topics 2 --lens "Business SaaS Tuesday"

Filter by Viability

# Only show ideas with viability score >= 75
python3 scripts/run_full_pipeline.py --min-viability 75 --include-viability

High-Pain-Point Focus

# Only show ideas addressing pain points >= 70 severity
python3 scripts/run_full_pipeline.py --pain-threshold 70

Comprehensive Analysis

# Full pipeline with deep research and high-quality filtering
python3 scripts/run_full_pipeline.py \
  --breadth 3 \
  --depth 2 \
  --pain-threshold 65 \
  --min-viability 70 \
  --include-viability

Validate Specific Trend

# Research a specific topic and generate ideas
python3 scripts/investigate_topic.py \
  --topic "AI code review tools" \
  --lens "Developer Tools Monday" \
  --output ~/.saas-idea-finder/outputs/custom_research.md

python3 scripts/synthesize_saas_ideas.py \
  --research ~/.saas-idea-finder/outputs/custom_research.md \
  --num-ideas 5

When it activates

Automatically, when a task matches. Use when: (1) User asks for SaaS/startup ideas, (2) User wants to discover trending topics in tech/business, (3) Da

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

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IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

valtterimelkko / agent-workflow-skills

skills/saas-idea-finder/SKILL.md

View on GitHub ↗

License MITImported 8/1/2026Version v1Bundled 4 files · scripts

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SkillScore

Safety

What it changes

Track record

Use this skill

SaaS Idea Finder

Overview

Pipeline Timing Expectations

Prerequisites

Required API Keys

Free APIs (No Keys Needed)

Rate-Limited APIs (Free Tiers)

Social Media Sources

Reddit OAuth2 Setup (Recommended)

TwitterAPI.io Setup (for Twitter/X)

Xpoz Authentication (Optional - for Instagram only)

Social Media Lens Configuration

Quick Start

Run Full Daily Pipeline

Advanced Options

How It Works

Smart Topic Discovery

Rotating Lens System

3-Stage Pipeline

Viability Scoring (0-100)

Enhanced Idea Template

Jobs-to-be-Done Framework

Pain Point Severity Scoring

Red Flags & Success Boosters

Auto-Detection Features

Individual Stage Usage

Discover Trends

Investigate Topic

Generate SaaS Ideas

Output Locations

Error Handling

Usage Examples

Daily Automated

Ad-Hoc Research

Filter by Viability

High-Pain-Point Focus

Comprehensive Analysis

Validate Specific Trend

When it activates

SkillSafety

Source

Related skills

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