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Hinge Profile Optimizer · leoyeai | DecimalAI

Marketingby leoyeai

Hinge Profile Optimizer

Comprehensive, research-backed Hinge dating profile optimization.

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

68%64%of test cases passed -5 pts

Without skill15 of 22

With skill14 of 22

1 fixed13 passed either way6 still failing2 broke

Measured on gemini-3.6-flash8/8/2026v1

Track record

Agent runs: 0; All time

Use this skill

Your agent runs on

Adds every file the skill has, pinned so you can update it. No account needed.

pip install "decimalai>=0.15"
decimalai skills add leoyeai-hinge-profile-optimizer
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("leoyeai-hinge-profile-optimizer")
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 leoyeai-hinge-profile-optimizer

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/leoyeai-hinge-profile-optimizer/SKILL.md https://app.decimal.ai/s/leoyeai-hinge-profile-optimizer/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/leoyeai-hinge-profile-optimizer/SKILL.md

This gets SKILL.md only. The skill's 7 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 leoyeai-hinge-profile-optimizer

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("leoyeai-hinge-profile-optimizer")
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/leoyeai-hinge-profile-optimizer/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/leoyeai-hinge-profile-optimizer/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 7 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 leoyeai-hinge-profile-optimizer

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("leoyeai-hinge-profile-optimizer")
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/leoyeai-hinge-profile-optimizer/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/leoyeai-hinge-profile-optimizer/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 7 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 leoyeai-hinge-profile-optimizer

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("leoyeai-hinge-profile-optimizer")
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/leoyeai-hinge-profile-optimizer/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/leoyeai-hinge-profile-optimizer/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 7 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 leoyeai-hinge-profile-optimizer

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("leoyeai-hinge-profile-optimizer")
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/leoyeai-hinge-profile-optimizer/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/leoyeai-hinge-profile-optimizer/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 7 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 leoyeai-hinge-profile-optimizer

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("leoyeai-hinge-profile-optimizer")
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/leoyeai-hinge-profile-optimizer/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/leoyeai-hinge-profile-optimizer/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 7 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 leoyeai-hinge-profile-optimizer

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("leoyeai-hinge-profile-optimizer")
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/leoyeai-hinge-profile-optimizer/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/leoyeai-hinge-profile-optimizer/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 7 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 leoyeai-hinge-profile-optimizer

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("leoyeai-hinge-profile-optimizer")
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/leoyeai-hinge-profile-optimizer/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 7 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 leoyeai-hinge-profile-optimizer

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("leoyeai-hinge-profile-optimizer")
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/leoyeai-hinge-profile-optimizer/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/leoyeai-hinge-profile-optimizer/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 7 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 leoyeai-hinge-profile-optimizer

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("leoyeai-hinge-profile-optimizer")
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/leoyeai-hinge-profile-optimizer/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/leoyeai-hinge-profile-optimizer/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 7 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 leoyeai-hinge-profile-optimizer

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("leoyeai-hinge-profile-optimizer")
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/leoyeai-hinge-profile-optimizer/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/leoyeai-hinge-profile-optimizer/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 7 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 leoyeai-hinge-profile-optimizer

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("leoyeai-hinge-profile-optimizer")
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/leoyeai-hinge-profile-optimizer/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/leoyeai-hinge-profile-optimizer/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 7 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 leoyeai-hinge-profile-optimizer

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("leoyeai-hinge-profile-optimizer")
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/leoyeai-hinge-profile-optimizer/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/leoyeai-hinge-profile-optimizer/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 7 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 leoyeai-hinge-profile-optimizer

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/leoyeai-hinge-profile-optimizer/SKILL.md https://app.decimal.ai/s/leoyeai-hinge-profile-optimizer/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/leoyeai-hinge-profile-optimizer/SKILL.md

This gets SKILL.md only. The skill's 7 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/leoyeai-hinge-profile-optimizer/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/leoyeai-hinge-profile-optimizer.json

This gets SKILL.md only. The skill's 7 other files are not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v1, tested 8/8/2026
Source: LeoYeAI/openclaw-master-skills ↗
License: MIT

Manage it in DecimalAI

Saved prompts and skills, pinned versions, and traces of this skill in your runs. DecimalAI does not run your agent.

Claude Code reads files from your project, so DecimalAI config does not apply there.

On Claude Agent SDK: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On OpenAI Agents: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On OpenAI SDK: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Anthropic: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Gemini API: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On LangChain: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On CrewAI: set DECIMAL_API_KEY and add decimalai.init(crewai=True) to record runs and skill use. The skill version stays in your repo.

On Google ADK: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Pydantic AI: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Deep Agents: set DECIMAL_API_KEY and add decimalai.init(langchain=True) to record runs and skill use. The skill version stays in your repo.

On Microsoft Agent Framework: set DECIMAL_API_KEY and add decimalai.init(otel=True, agent_name="support") to record runs and skill use. The skill version stays in your repo.

On Strands: set DECIMAL_API_KEY and add decimalai.init(otel=True, agent_name="support") to record runs and skill use. The skill version stays in your repo.

Editors read files from your project, so DecimalAI config does not apply there.

Anywhere else: record a use with decimalai.log_skill_activation() inside a trace.

Use this skill

SKILL.md

Hinge Profile Optimizer

The Core Philosophy

Your job isn't to make someone more appealing - it's to make them visible.

The interesting stuff is already there. Everyone has something - the way they think, what they care about, their weird specific interests, how they show up for people, what makes them laugh. Most profiles bury this under generic prompts and bad photo choices.

You're finding what makes this specific person unique and putting it where people can see it. Their character, their humor, their interests, their values, what it would actually be like to date them. That's it.

This is status affirming, not status fixing. You're not here to make them "better" — you're here to show who they already are to the people who'd appreciate that person. Research backs this up: Toma (2015) found that writing a genuine, compelling dating profile actually changes how people see themselves. The process of articulating what makes you interesting reinforces those qualities. This isn't just profile optimisation — it's an act of self-understanding.

There's someone for everyone. They just can't find each other when every profile says "love to laugh, looking for my partner in crime."

Your Role

You're a strategic collaborator helping someone show who they actually are. You're gathering ingredients to cook with, not auditing their flaws. The person sharing their profile and life details is being vulnerable - meet that with warmth and genuine curiosity.

Principles:

Read the room - adapt tone, pace, depth to how they're responding
Chase interesting threads - if something unique emerges, follow it
Skip what's not needed - phases are a framework, not a mandate
Principles over rules - use judgment, not checklists
Honest but kind - reality checks delivered with care
Status affirming - find what's good, not what's wrong

Process Overview

Eight phases, used flexibly:

Setup - Frame the process, establish context
Audit - Score current profile (skip if starting fresh)
Discovery - The big interview - find the real person
Reality Check - Market math, settings review
Photos - Evaluate, order, identify gaps
Copy - Write prompts and captions
Settings - Optimize visibility, reduce clutter
Implementation - Put it live together
Algorithm - Post-launch strategy

Not everyone needs every phase. Someone starting fresh skips audit. Someone who just wants copy help gets lighter discovery. Be flexible.

Phase 0: Setup & Framing

Start here. Set expectations, reduce defensiveness.

Say something like:

"Here's how this works: I'll look at your current profile (if you have one), ask a bunch of questions to understand who you actually are, then we'll build something better together.

The questions might seem random - we won't use everything. I'm just gathering ingredients to see where we can lean in. Nothing is too much, anything can be skipped.

If typing feels like a chore, just dictate - more natural anyway."

Establish:

Do they have a current profile or starting fresh?
Rough target: who are they hoping to attract?
Any specific frustrations? ("I only get X types", "No one responds to my likes")

Phase 1: Profile Audit

If they have an existing profile, audit it. If starting fresh, skip to Phase 2.

Request: Screenshots of current profile - all photos, prompts, settings.

Evaluate against:

First photo (dominates swipe decisions — clear face, good lighting, genuine expression?)
Photo variety (solo, social, full body, context/activity?)
Red flags (mirror selfies, group confusion, sunglasses hiding face, bathroom pics)
Prompt specificity (specific details vs generic statements)
Conversation hooks (can someone easily start a chat from this?)
Overall signal (what type of person does this attract?)

Scoring framework: See references/audit-criteria.md

Deliver: "Here's what's working... here are the opportunities." Lead with positives. Frame gaps as fixable, not failures.

Phase 2: Discovery Interview

The big interview. Find who they actually are — the unique hooks, costly signals, personality markers that make them them.

Framing throughout:

"This might seem random but trust me"
"Looking for the stuff that makes you memorable"
"Skip anything you want"

Approach: Conversational batches, 3-4 questions max per round. Follow interesting threads. Don't just run through a checklist.

Question Areas

Work & Status

What do you do? (dig for the interesting angle)
What would you never put in a bio but is actually impressive?
Any cool projects, clients, achievements, side things?

Personality & Opinions

What did you actually do last weekend?
What do you irrationally love? Irrationally hate?
What would your friends say is your "thing"?
Guilty pleasures? Hate-watches? Weird rituals?
Strong opinions on anything? (food, music, places, people)

Social & Warmth

Who are you closest to?
Any unusual relationships? (elderly relatives, unlikely friendships)
Pets?

Lifestyle & Context

Homebody or always out?
Neighborhoods/venues you're always at?
How do you spend Sundays?
Do you walk, cycle, drive everywhere?

Dating Specifics

What are you actually looking for?
What's gone wrong with past matches? What's the pattern?
What would a great first date look like?

Full question bank: See references/discovery-questions.md

What you're looking for (see references/discovery-questions.md for the full framework):

Costly signals — things that are specific, hard to fake, and demonstrate actual qualities (Donath, 2007). Jazz Cafe > "live music." The exhibition catalogue. The great uncle.
Strong opinions — these are gold because they filter. Chalamet hate, Saturday Kitchen hate-watch, hill-you'll-die-on takes. They attract compatible people and repel incompatible ones.
Unique hooks — anything no one else could claim. 92-year-old great uncle pub quiz partner. These are memorable because of the von Restorff effect: distinctive items stand out in a sea of generic.
Warmth markers — family, friends, pets, genuine care for others. Profiles need warmth alongside edge (Whitty, 2008).
Cultural specificity — venues, scenes, niche references their target market would recognise. Homophily research (Fiore & Donath, 2005) shows people seek similarity — specific cultural markers sort for compatible matches.

Phase 3: Reality Check

Gently align expectations with market reality.

The research context: Bruch & Newman (2018, Science Advances) analyzed 200,000 dating app users and found that attention follows a power law — top profiles receive 10-100x the messages of median ones. Most people pursue partners roughly 25% more desirable than themselves. The people your user wants to attract have abundant options and are more selective about profile quality (Hitsch et al., 2010). This isn't discouraging — it's strategically useful. It means volume is the wrong approach and differentiation is the right one.

Review:

Their target criteria (age range, distance, type)
Current settings
Whether their settings accidentally shrink the pool

Consider:

Is the age range realistic for their age and market?
Is distance too narrow (missing good people) or too wide (weird logistics)?
Are dealbreakers filtering out good matches unnecessarily?

If needed, do the math with them:

"Let's think about the actual pool here. Men 40-45 in London who are creative, have their life together, want something serious, and are on Hinge — that's maybe a few hundred people. And they have options — they can date women 28-48. So the strategy isn't volume, it's being memorable to the right 30-50 people."

Tone: Honest, not brutal. Frame as strategy, not criticism of their hopes. The power-law data is sobering but the implication is empowering: a great profile makes a disproportionate difference precisely because the market is unequal.

Output: Agreed target market, realistic settings, shared understanding that this is quality over quantity.

Phase 4: Photo Strategy

Evaluate what they have, identify gaps, set order.

Request: All available photos (not just current profile ones).

Evaluate each:

Face clearly visible? (no sunglasses, not too distant)
Lighting quality?
What context/story does it tell?
Solo or group? (if group, are they obviously identifiable?)
What does it signal about lifestyle/personality?

First photo matters most — research consistently shows photos dominate swipe decisions (Tyson et al., 2016). Must be: clear face, good lighting, genuine expression, solo.

Ideal mix:

Strong opener (clear face, warmth)
Context/lifestyle (what their world looks like)
Social proof (with friends, clear who they are)
Full body (builds trust)
Personality/interest (activity, venue, something they love)
Wildcard (humor, conversation starter, meme if fits their vibe)

Identify gaps: "You need a workspace photo" / "Need something showing you with friends where your face is clear"

If gaps are critical: Give specific guidance on what to shoot. Frame as "just taking some pictures" not "dating profile photoshoot."

Photo guidelines: See references/photo-guidelines.md

Phase 5: Copy Creation

Write the actual prompts and captions using discovery material.

First: Confirm current Hinge prompt options. They change. Ask user what's available or check references/hinge-prompts-current.md and verify.

Copy Principles

Each of these is grounded in research — see references/copy-principles.md for the evidence behind each one and references/research-findings.md for the full citations.

Specificity > Generic — Specific language signals honesty (Toma & Hancock, 2012) and creates psychological closeness (Construal Level Theory). Generic language signals evasion.

"Jazz Cafe on a weeknight" not "live music"
"Brutalist architecture" not "cool buildings"
"Saturday Kitchen hate-watch" not "cooking shows"

Every element = conversation hook — Specific profile content gets 30-40% more responses than generic content (OkCupid data). A prompt no one can respond to is wasted.

Can someone easily respond to this?
Does it invite a question or shared opinion?

Filter in AND filter out — Homophily research shows people seek similarity. Niche references attract compatible matches and repel incompatible ones. In a power-law market, this is the right strategy.

The right people should light up
The wrong people should self-select out
Niche references are features, not bugs

Balance edge with warmth — Humor signals intelligence (McGee & Shevlin, 2009) but excessive self-deprecation signals insecurity. Whitty (2008) found the best profiles balance self-promotion with warmth.

Dark humor needs a soft landing (family, friends, genuine care)
Pure edge reads as bitter
Pure warmth reads as bland

Show, don't tell — Donath (2007): demonstrated qualities are costly signals (hard to fake, credible). Claimed qualities are cheap signals (easy to fake, ignored).

"Being nice about Timothée Chalamet" shows dark humor
"I have dark humor" tells it (and everyone says this)

150 character limit — be concise, every word earns its place.

Annotated Example

Prompt: "Together we can be terrible at"
Answer: "Being nice about Timothée Chalamet."

WHY IT WORKS:
- Specific opinion (not generic)
- Polarizing = filters (fans swipe left, haters engage)
- Implies dark humor without stating it
- Instant conversation hook (everyone has a take)
- "Together" = collaborative, not solo bitterness

More examples: See references/copy-principles.md

Output: Complete copy doc - every prompt, every caption, copy-paste ready.

Phase 6: Settings & Setup

Optimize settings, reduce clutter.

Walk through:

Distance: Adjusted based on Phase 3 discussion
Age range: Adjusted based on Phase 3 discussion
Dealbreakers: Which actually matter vs performative?
Visible info: Hide clutter that adds nothing (star signs, politics if not crucial, height if not relevant)
Profile order: What do they see first? Lead with strength.

Premium features: If they have Hinge+/HingeX, discuss Roses strategy, seeing who liked them, etc.

Output: Settings checklist completed, clutter removed.

Phase 7: Implementation

Don't just deliver a doc. Help them put it live.

Offer:

"Want to do this now while we're here? Usually easier than coming back to it later."

Walk through:

Photo upload in correct order
Copy-paste each prompt/caption
Settings adjustments
Preview check - how does it look?
Go live

If they want to do it later: Give clear, numbered implementation checklist.

Phase 8: Algorithm Strategy

Post-launch guidance for first 2-4 weeks.

Key points:

Daily activity matters (10-15 mins)
Always comment when liking — never empty likes (Hinge data shows comments significantly outperform bare likes; signaling theory explains why — comments are costly signals of genuine interest)
Respond same day when possible
Be somewhat selective — quality signals (the specific "10-20%" figure is conventional wisdom, not verified data, but the principle holds: being too eager dilutes your signal)
"Most Compatible" suggestions are worth attention
Don't panic in week 1 - algorithm is recalibrating

Expectations:

Week 1-2: Algorithm learning new profile
Week 3-4: Quality matches should appear
Review at week 4: What's working? What needs adjustment?

Flexible Execution

Adapt to what they need:

Situation	Approach
Starting fresh, no profile	Skip Phase 1
Just wants copy help	Light Phase 2, focus on Phase 5
Has good photos, bad prompts	Light Phase 4, focus on Phase 5
Profile fine, no matches	Focus on Phase 3 (reality check) and Phase 6 (settings)
Already implemented, wants strategy	Jump to Phase 8

Reference Files

references/research-findings.md - The research base: 29 peer-reviewed studies, platform data, signaling theory, self-disclosure, competition dynamics. Evidence tiers for everything. Start here to understand why the skill works.
references/audit-criteria.md - Scoring framework with research-backed weighting, signaling analysis (costly vs cheap signals), competitive position assessment
references/discovery-questions.md - Full question bank with research framing: why we ask what we ask, what we're mining for, and how it maps to self-disclosure and signaling theory
references/copy-principles.md - What makes copy work, why it works (research basis for each principle), and annotated examples
references/photo-guidelines.md - Photo evaluation, ordering logic, caption strategy, and red flags — with research context
references/hinge-prompts-current.md - Current Hinge prompt options and selection strategy (verify with user — prompts change)
references/hinge-settings.md - Settings walkthrough, algorithm mechanics, evidence tiers for each claim

Remember

This is someone's dating life — it matters to them.

Most people come in feeling like their profile sucks because they suck. That's almost never true. They're just invisible — the good stuff is there but buried under generic language that reads as evasive (Toma & Hancock, 2012) and cheap signals that everyone else is sending too (Donath, 2007).

Your job is to find it, pull it out, and put it where the right people can see it. Character, humor, interests, values, what makes them them.

The research says this process works at every level: specific profiles get more matches, better conversations, and better first dates (Sharabi & Caughlin, 2017). And the act of writing a genuine, compelling profile changes how people see themselves (Toma, 2015). You're not just optimising a profile — you're helping someone see what's interesting about them.

Be thorough. Be honest. Be kind. There's someone for everyone — help them find each other.

When it activates

Automatically, when a task matches. Use when someone wants to improve their Hinge profile, audit an existing profile, write better prompts/captions, select and order photos strategically, or understand why they're not getting quality matches. This is the thorough process (~45 mins) - discovery interview, honest market math, photo strategy, copy creation, settings cleanup, and implementation support. Grounded in peer-reviewed behavioral research, platform data, and s

About this skill

Automatic

Personal Assistant pack

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v4 — 8/2/2026

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IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

LeoYeAI / openclaw-master-skills

skills/hinge-profile-optimizer/SKILL.md

View on GitHub ↗

License MITImported 8/2/2026Version v1Bundled 7 files

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

Related skills

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

From the same source and nearby in the catalogue.

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What it changes

Track record

Use this skill

Hinge Profile Optimizer

The Core Philosophy

Your Role

Process Overview

Phase 0: Setup & Framing

Phase 1: Profile Audit

Phase 2: Discovery Interview

Question Areas

Phase 3: Reality Check

Phase 4: Photo Strategy

Phase 5: Copy Creation

Copy Principles

Annotated Example

Phase 6: Settings & Setup

Phase 7: Implementation

Phase 8: Algorithm Strategy

Flexible Execution

Reference Files

Remember

When it activates

SkillSafety

Source

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