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geo-sleuth (v2) · oldcircle | DecimalAI

Researchby oldcircle

geo-sleuth (v2)

照片拍摄地点定位（看图找地点 / 网络迷踪 / 图寻 / 推拍摄时间）。给一张或几张照片，先一条命令做完元数据、OCR、以图搜图（intake.py），把线索和候选记到候选盘（board.py）上由脚本排名、给下一步；查表线索用 clues.py；卫星图和街景都是"机器先排序、人只看前几名"（sat_scan.py、match.py）；每个结论都用真实数据核对，输出坐标 + 误差半径、证据图和分档置信度。Geolocate or chronolocate a photo with tool-verified reasoning — EXIF, OCR, reverse image search (Baidu/Yandex), lookup tables (plates, area codes, calling codes, driving side, territories), candidate board with likelihood ranking, sun and shadow math, OSM Overpass, CLIP-ranked satellite scan, D

Maintained

SkillScore

SkillScore77/100

Test results 77

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

18%77%of test cases passed +59 pts

Without skill4 of 22

With skill17 of 22

13 fixed4 passed either way5 still failing0 broke

Model 10/6/2026v1 · latest is v319 comparable

This benchmark measured an earlier version of the skill.

A newer attempt on gemini-3.8-flash on 10/22/2026 did not produce a publishable comparison (graded differently). The snapshot above is the previously measured run.

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskI have a photo showing a distinct four-way street intersection pattern with specific road angles and nearby OSM building geometries. Which tool script scans road network geometries to find matching road intersections?

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 oldcircle-geo-sleuth
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("oldcircle-geo-sleuth")
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 oldcircle-geo-sleuth

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/oldcircle-geo-sleuth/SKILL.md https://app.decimal.ai/s/oldcircle-geo-sleuth/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/oldcircle-geo-sleuth/SKILL.md

This gets SKILL.md only. The skill's 35 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 oldcircle-geo-sleuth

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("oldcircle-geo-sleuth")
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/oldcircle-geo-sleuth/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/oldcircle-geo-sleuth/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 35 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 oldcircle-geo-sleuth

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("oldcircle-geo-sleuth")
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/oldcircle-geo-sleuth/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/oldcircle-geo-sleuth/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 35 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 oldcircle-geo-sleuth

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("oldcircle-geo-sleuth")
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/oldcircle-geo-sleuth/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/oldcircle-geo-sleuth/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 35 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 oldcircle-geo-sleuth

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("oldcircle-geo-sleuth")
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/oldcircle-geo-sleuth/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/oldcircle-geo-sleuth/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 35 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 oldcircle-geo-sleuth

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("oldcircle-geo-sleuth")
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/oldcircle-geo-sleuth/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/oldcircle-geo-sleuth/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 35 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 oldcircle-geo-sleuth

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("oldcircle-geo-sleuth")
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/oldcircle-geo-sleuth/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/oldcircle-geo-sleuth/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 35 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 oldcircle-geo-sleuth

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("oldcircle-geo-sleuth")
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/oldcircle-geo-sleuth/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 35 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 oldcircle-geo-sleuth

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("oldcircle-geo-sleuth")
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/oldcircle-geo-sleuth/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/oldcircle-geo-sleuth/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 35 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 oldcircle-geo-sleuth

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("oldcircle-geo-sleuth")
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/oldcircle-geo-sleuth/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/oldcircle-geo-sleuth/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 35 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 oldcircle-geo-sleuth

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("oldcircle-geo-sleuth")
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/oldcircle-geo-sleuth/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/oldcircle-geo-sleuth/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 35 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 oldcircle-geo-sleuth

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("oldcircle-geo-sleuth")
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/oldcircle-geo-sleuth/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/oldcircle-geo-sleuth/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 35 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 oldcircle-geo-sleuth

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("oldcircle-geo-sleuth")
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/oldcircle-geo-sleuth/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/oldcircle-geo-sleuth/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 35 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 oldcircle-geo-sleuth

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/oldcircle-geo-sleuth/SKILL.md https://app.decimal.ai/s/oldcircle-geo-sleuth/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/oldcircle-geo-sleuth/SKILL.md

This gets SKILL.md only. The skill's 35 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/oldcircle-geo-sleuth/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/oldcircle-geo-sleuth.json

This gets SKILL.md only. The skill's 35 other files are not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v3 (v1 tested 10/6/2026)
Source: oldcircle/geo-sleuth ↗
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

geo-sleuth (v2)

Goal: find a location that holds up under checking. Every conclusion must point to the clue it used, the command it ran and the file it produced.

${CLAUDE_SKILL_DIR} in commands means the directory containing this SKILL.md (the same for commands in references). Claude Code substitutes it automatically; in other agents, first export CLAUDE_SKILL_DIR=<absolute path of this directory> before running (if the shell doesn't keep variables between commands, prefix every command with it), or replace it with that path directly.

The work is split into three layers. Remember this first, then read the flow:

Layer	Who	Tools
Decision: which candidates exist, how evidence is scored, whether a candidate can be excluded, what to scan next	Script (candidate board)	`board.py`
Perception: reading text, table lookups, finding targets in satellite imagery, street-view matching	Scripts compute and rank first; you look only at the top few	`intake.py` `ocr.py` `clues.py` `sat_scan.py` `match.py` `geo.py bearings`
Judgment: pulling clues out of the image, proposing hypotheses when the tables have nothing, deciding among the machine's top few	You	—

The method comes from breaking down 14 photo-geolocation (网络迷踪) creator videos and 22 puzzles, plus several rounds of blind-test comparisons; the breakdown notes are not published with the repo. The lesson of v1: rules written as prose don't get executed, and two runs of the same skill version gave very different results; so v2 puts every rule that can be written as code into board.py.

Hard rules (always in force; ★ = enforced by board.py, just follow it)

If the user says the photo isn't theirs, or the image shows a private residence or minors, ask once what it's for before continuing; when the prompt already states the source or purpose (an evaluation puzzle, a puzzle setter's hint, the user says they took it), don't ask, and go to the finest level as usual.
Don't fabricate verification. "Measured on the map", "matched street view", "±10 m" must correspond to commands actually run and files actually produced in this session. If you didn't run it, write "unverified".
Precision needs a source: an error radius ≤100 m requires the intersection of two independent constraints, or a ground-level match on ≥3 invariant features. Before reporting a camera position, run a self-check: pose.py project --horizon <sea-horizon row> computes what the pitch should be and each feature's "expected row" for the current camera position; reconcile that with the photo (the depression angle to a target ≠ the camera's pitch; look up the camera position's elevation with terrain.py elev — if the height difference doesn't match the depression angles in the image, the camera position is wrong).
Once you have a unique anchor, close the loop: all later search and geometry starts from the anchor; don't go back to generic features like "blue-green lake water" or "tropical park" and pick the most famous place of that kind.
Compute bearings first, then identify structures: which block in satellite imagery is the tower or chimney in the photo is itself an interpretation. First compute its true bearing in the image with sun.py compass or a confirmed landmark, then use geo.py bearings to see which outline around the candidate camera position falls on that bearing; when nothing fits, first try both templates ("original/mirrored", "sunrise/sunset"); if not verified, only lower the tier, don't exclude.
★ List the whole category before inferring: when inferring a region from "hill city", "tropical", "IP in a municipality", use board.py children to add all subordinate admin divisions as candidates, then rank them with evidence. Don't default to the main urban district, don't pick by population.
Metadata and hints are hypotheses: when EXIF, IP location, location tags or the puzzle setter's words conflict with the image, the image wins; don't invent a story to reconcile them. IP says country A but the hint or the regulation designs (plates, signs) point to another continent: intersect with clues.py lookup territories <country A> --continent <continent>.
Self-consistency: redo each key judgment once with a different crop or keyword set; if the two results differ by tens of kilometers or more, lower the tier.
★ If you can't pin a point, give a range + what information is missing; when the candidates are a few discrete ones, don't take the midpoint: the main answer of board.py report is always the top-ranked candidate; the rest go into alternatives with discriminating tests.
★ Exclusion and confirmation use the same standard: board.py exclude only accepts read/computed clues + a computed file (output of geo.py frame, terrain.py, etc.); observed and inferred clues can only down-weight via evidence --against, with the likelihood ratio clamped to 1/3–3 (inferred) or 1/5–5 (observed). Generating candidates from "there's an X nearby" is also filtering, and X must be something confirmed in the image. Fine-level candidates such as campuses, residential compounds and street segments must go on the board too: add a batch at once with board.py add --from <poi.py --out / osm.py geom output> --level area/road, don't hand-pick; when you drop one, write evidence --against with a comparison image, and check lists candidates without a single piece of evidence (meaning nobody looked at them); a finer candidate goes under the one that contains it (a point under its street segment: --parent, or board.py move), so its support counts in that level's ranking, and check fails when it skips a level; labels from third-party data (tree species in a municipal inventory, an exact school-name match) can only down-weight, never justify exclusion (in two cases the ground truth was skipped by hand exactly this way). The excluded extent must be ≤ the evidence extent: for candidates with extent, such as districts, areas and roads, exclude needs --covers lat,lon[:lat,lon] stating which stretch the evidence covers; the script rejects coverage below half (excluding a whole road after looking at one point on it failed in two cases).
★ Population and fame are not evidence; scan order follows "share ÷ pages" (board.py next): finish the small districts first, put large districts last with a page cap.

Flow

You don't have to go through every step: if reverse image search in step 1 hits directly, jump to steps 6 and 7 to confirm. Every step's output goes on the candidate board.

Step 1: steps 0–3 in one command

uv run ${CLAUDE_SKILL_DIR}/scripts/intake.py photo.jpg --out-dir intake/ [--box x0,y0,x1,y1 ...]
uv run ${CLAUDE_SKILL_DIR}/scripts/board.py init --photo photo.jpg

intake.py usually takes 1–2 minutes, longer on the first run because it installs dependencies: give the command a generous timeout (10+ minutes) or run it in the background. If a command timeout interrupts it, the reverse-image-search subprocesses may still be writing into rev/, but intake.md won't be generated; don't treat it as finished.

intake.md contains: metadata, OCR text (lines read from upscaled/tiled passes are marked pass=up/tile and are hypotheses), Baidu similar images (source-site counts + numbered contact sheet), reverse-image-search labels with tiered vote counts, likely residential compound/development names, the list of edge crops, and failed items. Then you do four things:

Look at the image: go through every crop in edges/ (four edges, four corners); run through the checklist in references/observe.md; log each clue with board.py clue "<text>" --kind <kind> --status observed|read|inferred|computed --file <zoomed crop>. Be honest about status: text you read is read; "the building is probably 8 floors", "the road goes uphill" are inferred.
When a computed result actually tests an earlier clue, link it with clue "<result>" --kind <kind> --status computed --file <output> --resolves K1 (repeat --resolves for multiple clues). An unrelated computation does not resolve a pending infrastructure clue; this link records the test, not support for a location.
Lookup tables: calling codes, driving side, overseas territories are global; plates, area codes and other country tables come from region packs → clues.py lookup <kind> <value> [--country <cc>] (without --country, every pack is searched and matches from several countries are all returned). For those that resolve to an admin division, use board.py apply --kind plate --value <plate prefix> --file <zoomed crop> directly (adds candidates and evidence automatically; the other candidates at the same level are only down-weighted, not excluded).
Reverse-image-search results: first open rev/<name>_baidu_similar.jpg (the top-left tile is the query image) and look for near-duplicates of the same object or the same scene; if there are any, go by number to similar[i].from in the JSON to see the source page; compound, development or hotel names in the labels → poi.py "<name>" --city <city> --out pois.json to get coordinates; for same-name hits (several campuses, several branches) put them all on the board with board.py add --from pois.json --level area, then check; always open the screenshots, and once a post hits, look through the rest of its photo set. Choose where to search by object type (references/search.md).
Hints and metadata: log each as an inferred clue and state how credible it is; IP location only says where the person was when posting, and the posting time is not the capture time.
Country-specific material: while the country is still open, check references/clues/global.md (clues that separate countries). Country-specific clues, tables, preferred services and tips are in region packs (regions/<cc>/). Once a country is a candidate, run regions.py show <cc>: it prints the pack's lookups, services and a one-line index of its clue entries with line numbers; read only the entries you need. When the country is settled, board.py country <cc> and stop consulting global.md. regions.py list shows which packs exist; with no pack, use the global tools.

Step 2: list all candidates, score evidence, check the next step

uv run ${CLAUDE_SKILL_DIR}/scripts/board.py children <parent admin area>          # municipality → all districts; country → first-level admin divisions (gazetteer.py queries OSM, with bbox)
uv run ${CLAUDE_SKILL_DIR}/scripts/board.py evidence --clue K1 --for <candidate A>:5 --for <candidate B>:2 --against <candidate C>:0.3 --why "…" --file <comparison image>
uv run ${CLAUDE_SKILL_DIR}/scripts/board.py rank
uv run ${CLAUDE_SKILL_DIR}/scripts/board.py next

next only ever says one of two things:

Can't separate: run discriminating tests from cheap to expensive, each one on all candidates at once — lookup tables → terrain (plain vs. hill city, tiles.py fetch --zoom 13 or terrain.py) → vehicle livery (image search for " bus" in the local language; read route signs from the result images, compare the stripe along the bus rear by district) → street fixtures (street view per candidate built-up area) → river/road-network templates. Still can't separate after all of them: don't stop; scan in the "share ÷ pages" order it gives. next replaces these lines with the region pack's tips when the board's country is known.
Ready to narrow: first narrow the extent to the built-up area with board.py urban <candidate> (or give it by hand with scan-bbox), then write a falsification condition with board.py falsify <candidate> --text "…", then go to step 3.

The coarse environmental-location rules still apply: terrain before river width and building color; phenology must be paired with the month; take the intersection of rare facility combinations; recognizable species are only an exclusion tool; when all you have is a land type, first narrow to that type of land with a land-cover map (references/clues/).

Step 3: pick a branch to narrow down

What you have	Approach	Read
A unique anchor (building, statue, tower, scenic-area building)	Anchor geometry: sight-line intersection, alignment lines, tangent lines, filtering buildings by camera height	`geometry.md`
Distant mountains, skyline	Render candidate camera positions with `terrain.py view` and compare; this fixes only one sight line, then find a second constraint. No EXIF: don't assume the main camera (distant mountains are often zoomed); a best focal on the edge of the searched range means widen and rerun. Near range in front of a far one: score both layers	`geometry.md`
Clear shadows, lit faces, the sun in the frame	`sun.py locate / when / facing / compass`: latitude band, time, heading, true bearing of objects in the image	`sky.md`
Route numbers, railway or power-line specs, a large river	Linear corridor + line-to-point: `osm.py route / crossings / along`	`corridors.md`
Two or three kinds of infrastructure in one frame	`osm.py near --report`; crossings of two linear types `osm.py intersect` (bends are only annotated, not removed)	`corridors.md`
Linear infrastructure + unrecognized mountains, no text	Compute horizons from elevation along the infrastructure and filter a whole region → score skyline + distance to infrastructure numerically → overlay the top 3–5 → fix the camera position from evenly spaced structures: `terrain.py scan --lines … --out hits.json --clusters-out clusters.json` → `terrain.py ridge photo.jpg --x0 … --x1 … --out ridge.json` → `terrain.py fit --hits hits.json --ridge ridge.json [--line … --line-dist …] --sheet top.jpg --photo photo.jpg` (for the fine search switch to `--at lat,lon --radius 800 --grid 100 --zoom 13`) → `imgprep.py piers photo.jpg --rows … --out cols.json --sheet piers.jpg` → `geo.py spacing --cols … --line … --span … --center lat,lon --ridge ridge.json`	`corridors.md` 4.3, `geometry.md` 7.4 / 7.7
Clear street layout, no anchor	`osm.py street-scan` to filter intersections → `tiles.py sheet` → street view	`corridors.md`
No near-duplicates from reverse image search, no text, only a set of scene elements (road spec + adjacent features + landform)	Don't hand-pick spots first: write the scene as one English query and let CLIP rank with `sat_scan.py grid --bbox <coastal strip/corridor> --zoom 17 --cell 360 --query … --neg …`; look only at the top 20–30 thumbnails; `grid` beats `points` because OSM features like `amenity=parking` may be incomplete	`search.md`
≥4 points of known location in the frame (window views, looking down; also level shots of distant towers, bridges, piers — fix the height)	`pose.py solve` solves camera position, heading and height; when several candidate positions all fit, score them one by one with `pose.py check --cands`; only robust Δchi2 > 9 may go to `board.py exclude --computed`	`geometry.md` section 10
Want to exclude by "there's no X in the frame"	`geo.py frame` first computes whether X should be inside the frame, large enough and not occluded; only what it says can exclude goes to `board.py exclude --computed`	`geometry.md` section 11
An unnamed building of a recognizable class, search region narrowed	Enumerate with `osm.py find` → nearby views facing each building with `gsv.py targets` → rank with `match.py`; use bounded batches	`search.md` section 6
Recognizable facility type (grain dryer tower, feed mill, sugar mill, concrete batching plant)	Look up the industry's distribution → enumerate large buildings with `osm.py buildings` → rank with `sat_scan.py points`	`search.md`
Stores of a chain's sub-brand	First search opening press releases for the address; use store locators only as a candidate pool → `osm.py along` + street-view sampling	`search.md`
Plane window, drone shots looking down	Aerial branch	`aerial.md`

Step 4: finding the spot in satellite imagery — the machine ranks first

uv run ${CLAUDE_SKILL_DIR}/scripts/sat_scan.py grid --bbox <scan_bbox> --zoom 17 --preset track --multi-scale --top 30 --out sat.json --sheet sat_top.jpg --heat heat.jpg
uv run ${CLAUDE_SKILL_DIR}/scripts/poi.py "<district> <school, in the local language>" --city <city> --region <cc> --out schools.json      # seeds: --seeds schools.json boosts nearby cells
uv run ${CLAUDE_SKILL_DIR}/scripts/sat_scan.py points --points big.json --preset factory --out r.json --sheet r.jpg   # rank candidate points from osm.py buildings / poi.py

Presets: track (running tracks), stadium, factory, silo, dam, bridge, quarry, solar, greenhouse, port; custom --query. In tests: across 300-odd cells of an urban area, more than half of the OSM-mapped running tracks made the top 30; in an old town in China where OSM is blank, a school's sports field still ranked first. It ranks, it doesn't decide: look at the thumbnails of the top 20–30 cells, then check them against the bearing and shape in the photo.
Translate the image description into top-down features before looking: curved buildings, octagonal pavilion roofs, sports courts, parking spaces perpendicular to a railway; for tall buildings look at the base; imagery has a date.
With many candidates, make a candidate table: one row per point, one column per criterion directly visible in the photo.
Nothing in the top 30 matches: first go back to the "candidates down-weighted by inference" listed by board.py check and to the falsification conditions, then switch preset or go to z18, and only then widen the area.

Step 5: confirmation — street view is ranked first too

uv run ${CLAUDE_SKILL_DIR}/scripts/baidu_pano.py scan <lat,lon> --radius 300 --out panos.json                     # the street-view script the region card names (CN: baidu_pano.py); otherwise gsv.py
uv run ${CLAUDE_SKILL_DIR}/scripts/match.py rank --query photo.jpg --panos panos.json --toward <landmark lat,lon> --spread 15 --refine sift --top 10 --out m.json --sheet m.jpg
uv run ${CLAUDE_SKILL_DIR}/scripts/match.py rank --query photo.jpg --items around.index.json --render gsv --spread-headings -30,0,30 --out m.json --sheet m.jpg

match.py coarse-ranks by DINOv2 global similarity and fine-ranks by SIFT inliers; in tests, ground-truth street views of the same place from different years all made the top 4. Open only the top 10 and compare invariant features (building outline, window positions, balconies, pole positions, curbs, ridgelines), not vehicles, signs or foliage. ≥2 features for road level, ≥3 for building level. Not a single image with ≥15 inliers doesn't mean it's wrong: with a change of season, an old capture, or the photo taken from the sidewalk while street view was shot from the middle of the road, the ground truth had only 5 and 0–8 inliers in tests (two cases); first open the top 10 and compare invariant features, and only if none fit change --spread-headings or widen --within. The global score gets dominated by season (a blossom-season capture ranks high wherever it is), so compare against a historical capture from the same season as the photo when you can (gsv.py sheet --date).
With many panorama points, group by date and road; sheet --road <road name> --spread 60 shows one road only; older captures have a more open view.
No street view doesn't mean you can't confirm: compare ridgelines with terrain.py view --photo; find a name for the candidate facility (OSM name, nearby place name + the facility-type word in the local language) and search news, encyclopedias and official-site photos to compare facade details.
When street view is many years older than the photo, go by old buildings and permanent structures.

Step 6: fix the camera position and write the output

Look back: at the matching street-view point, turn 180° and see what's on the photographer's side.
A camera position needs two independent constraints (geo.py intersect sight-line intersection, geo.py line alignment line, pose.py multi-point solve, camera height, reverse street view); with only one, building-level confidence is at most "medium".
When there's no measurable shadow, use lit faces for the heading: sun.py facing --lit left --shaded camera. For photos taken from a car, boat or train, state the direction of travel.

uv run ${CLAUDE_SKILL_DIR}/scripts/board.py check                      # before concluding: do exclusions have files, does the main answer have verified evidence, which clues went unused
uv run ${CLAUDE_SKILL_DIR}/scripts/board.py report --merge result.json # main answer = top-ranked; alternatives, exclusions and unused clues are written into result.json automatically
uv run ${CLAUDE_SKILL_DIR}/scripts/evidence.py spec.json --out evidence.jpg

Show evidence.jpg to the user together with the answer, without waiting to be asked. A road-level claim from satellite imagery needs a ≥z18 crop, not a z17 glance.

Output:

One-sentence conclusion: place + camera position + heading (+ direction of travel and capture time, where applicable)
Coordinates: WGS84, plus the local datum when the region card names one (geo.py convert), with error radius
Evidence image (satellite image marking the camera position and heading wedge + comparison images)
Reasoning chain: clue → inference → commands actually run and files produced → extent
Tiered confidence (table below). Rate each level separately; the tier you report is the finest level rated "medium" or above: a low building level doesn't drag down a high area level
Excluded candidates and reasons, alternatives and discriminating tests, clues not used or not resolved (generated by board.py report)
When you can't pin a point: which level is settled + what information is still needed

Use English for tool messages, report headings and generated labels. Preserve source text (OCR, place names, service responses) verbatim as evidence; explain or translate it for the reader. Write the agent's final report in the user's language. In Chinese, use 高/中/低 for high/medium/low and 城市/片区/路/楼/楼层 for city/area/road/building/floor.

Level	High	Medium	Low
City	Text, plates, area codes or a confirmed anchor	Several independent weak clues agree	A single weak clue, or only the hint
Area	A unique facility or landmark matches ≥3 top-down features in satellite imagery (whole candidate area checked, no second match)	The most similar spot in the candidate area, with candidates still unchecked	Inferred
Road	Ground-level match on ≥2 unique features	Layout consistent, 1 unique feature; when there is no ground-level imagery at all: area level high + bearing self-check passed	Satellite layout only
Building	Two independent constraints + ground-level match on ≥3 features	One of the two	Inferred
Floor	Two kinds of reference agree	A single reference	Don't give

Budget and stopping conditions

intake.py counts as one call; if reverse image search with 3 different keyword sets finds nothing, stop and go back to the checklist for other clues.
While candidates still can't be separated, don't scan any point one by one (when board.py next says "can't separate", do the discriminating tests first); if they still can't be separated after the cheap tests, scan in the order it gives, the first 3 pages of each candidate's built-up area first.
Scanning and confirmation always rank first: look at the top 30 cells from sat_scan.py and the top 10 images from match.py; widen only when all of the top ones are wrong, instead of paging through.
Run osm.py coverage before enumerating candidates with OSM: districts with sparse data get silently excluded; switch to sat_scan.py grid and say so in the conclusion.
Scan panoramas in batches of at most 500 points per area. When a batch or several areas yield no match, review candidate generation, search coverage and down-weighted candidates; then change the approach or widen the justified search scope. A fixed number of unmatched areas is not a reason to end the investigation.
A failed or unavailable service blocks that route, not the whole investigation. Consider other feasible sources or methods; do not repeat unchanged failing requests.
Before stopping, board.py check: go back through the top 20 candidates, in ranking order, that were down-weighted by inference but not excluded.
Conclude when evidence supports the requested location level, the user asks to stop or a user-set budget is reached, or no remaining feasible action is likely to distinguish the candidates. If unresolved, use board.py report to state the supported level, actual search coverage, attempted routes, blockers and missing evidence; do not invent a coordinate or precision to finish.

Runtime environment

Python 3.10+, uv, and curl. Always use uv run ${CLAUDE_SKILL_DIR}/scripts/xxx.py; each script declares its dependencies. scripts/ in references is relative to this skill directory.
On first setup or after a runtime failure, run uv run ${CLAUDE_SKILL_DIR}/scripts/doctor.py; add --network to check service reachability. Read the English checks and fixes before starting an expensive scan. It uploads no photos and does not load ML models.
Reverse image search uses local Google Chrome, with automatic fallback to Playwright Chromium (uvx playwright install chromium). If neither starts, use intake.py --no-rev and report the skipped search. OCR prefers Apple Vision on macOS and uses RapidOCR elsewhere or as a fallback.
match.py and sat_scan.py install ML dependencies and download model weights on first use. Allow extra time and disk space.
Cache goes to .geo-cache/ in the current directory; the candidate board is board.json in the current directory. Script list and data sources: references/data-sources.md.
macOS has no timeout command; in zsh $var doesn't word-split, so use bash -c or ${=var} in loops. A province-wide Overpass query can take several minutes; run it in the background.

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oldcircle / geo-sleuth

skills/geo-sleuth/SKILL.md

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License MITImported 10/21/2026Version v3Bundled 35 files · scripts

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geo-sleuth (v2)

Hard rules (always in force; ★ = enforced by board.py, just follow it)

Flow

Step 1: steps 0–3 in one command

Step 2: list all candidates, score evidence, check the next step

Step 3: pick a branch to narrow down

Step 4: finding the spot in satellite imagery — the machine ranks first

Step 5: confirmation — street view is ranked first too

Step 6: fix the camera position and write the output

Budget and stopping conditions

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