Logicmso logo

Logicmso

Organization
BrownFineSecurity
logicmso

Analyze digital and analog captures from Saleae Logic MSO devices. Decode protocols like UART, SPI, I2C from exported binary files. Use when analyzing logic analyzer captures for CTF challenges, hardware reverse engineering, or protocol decoding.

Overview

PublisherBrownFineSecurity
Repositoryiothackbot
Skill namelogicmso
Stars
858
Forks
134
Bundled files
2
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by BrownFineSecurity on GitHub. Read the source before you install it.

Installation

Install the Logicmso AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/BrownFineSecurity/iothackbot/tree/master/skills/logicmso
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/BrownFineSecurity/iothackbot.git /tmp/iothackbot
mkdir -p .claude/skills
cp -r /tmp/iothackbot/skills/logicmso .claude/skills/logicmso
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Logicmso in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Logicmso on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Logicmso is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Saleae Logic MSO Analysis

This skill enables analysis of captured signals from Saleae Logic MSO devices using the saleae-mso-api Python library. It supports loading binary exports, analyzing signal transitions, and decoding common protocols.

Prerequisites

  • saleae-mso-api Python package — Do NOT blindly pip install. First check if it's already installed:
    bash
    python3 -c "from saleae.mso_api.binary_files import read_file; print('saleae-mso-api is available')"
    Only if that fails, install it: pip install saleae-mso-api
  • Binary export files from Saleae Logic software (.bin format)

Quick Reference

Loading Binary Files

python
from saleae.mso_api.binary_files import read_file
from pathlib import Path

file_path = Path("capture.bin")
saleae_file = read_file(file_path)

# Access metadata
print(f"Version: {saleae_file.version}")
print(f"Type: {saleae_file.type}")

# Access data
contents = saleae_file.contents

Digital Capture Structure

Digital exports contain DigitalExport_V1 with chunks:

python
chunk = saleae_file.contents.chunks[0]

# Key attributes:
chunk.initial_state      # Starting logic level (0 or 1)
chunk.transition_times   # numpy array of transition timestamps (seconds)
chunk.sample_rate        # Capture rate in Hz
chunk.begin_time         # Capture start time
chunk.end_time           # Capture end time

Calculating Pulse Durations

python
import numpy as np

times = np.array(chunk.transition_times)
durations_ms = np.diff(times) * 1000  # Convert to milliseconds

# If initial_state is 0 (LOW):
#   - Even indices (0, 2, 4...) = HIGH pulse durations
#   - Odd indices (1, 3, 5...) = LOW gap durations
# If initial_state is 1 (HIGH):
#   - Even indices = LOW gap durations
#   - Odd indices = HIGH pulse durations

Helper Scripts

This skill includes helper scripts for common analysis tasks:

Protocol Analyzer

bash
# Analyze signal characteristics
python3 skills/logicmso/analyze_protocol.py capture.bin

# Show detailed timing histogram
python3 skills/logicmso/analyze_protocol.py capture.bin --histogram

# Show detected timing clusters
python3 skills/logicmso/analyze_protocol.py capture.bin --clusters

# Export transitions to CSV
python3 skills/logicmso/analyze_protocol.py capture.bin --export transitions.csv

# Show raw transition values
python3 skills/logicmso/analyze_protocol.py capture.bin --raw -n 50

See examples.md for full worked end-to-end captures: unknown-protocol triage, and UART, SPI, I2C, and 1-Wire decoding with runnable Python.

Common Protocol Patterns

UART (Asynchronous Serial)

  • Idle state: HIGH
  • Start bit: LOW (1 bit period)
  • Data bits: 8 bits, LSB first
  • Stop bit: HIGH (1-2 bit periods)
  • Common baud rates: 9600, 19200, 38400, 57600, 115200
  • Bit period calculation: 1/baud_rate seconds
  • Identifying features: Consistent bit periods, durations are multiples of base period

SPI (Serial Peripheral Interface)

  • 4 signals: SCLK (clock), MOSI (master out), MISO (master in), CS (chip select)
  • Clock polarity (CPOL): Idle clock state (0=LOW, 1=HIGH)
  • Clock phase (CPHA): Sample edge (0=leading, 1=trailing)
  • Data: Sampled on clock edges, typically 8 bits per transaction
  • Identifying features: Regular clock signal, CS goes LOW during transaction

I2C (Inter-Integrated Circuit)

  • 2 signals: SDA (data), SCL (clock)
  • Idle state: Both HIGH (pulled up)
  • Start condition: SDA falls while SCL is HIGH
  • Stop condition: SDA rises while SCL is HIGH
  • Data: 8 bits + ACK/NACK, MSB first
  • Address: 7-bit (first byte after START)
  • Identifying features: START/STOP conditions, 9 clock pulses per byte (8 data + ACK)

1-Wire

  • Single signal: DQ (data/power)
  • Idle state: HIGH (pulled up)
  • Reset pulse: Master pulls LOW for 480us minimum
  • Presence pulse: Slave responds LOW for 60-240us
  • Write 0: LOW for 60-120us
  • Write 1: LOW for 1-15us, then release
  • Read: Master samples 15us after pulling LOW

Analysis Workflow

Step 1: Initial Exploration

python
from saleae.mso_api.binary_files import read_file
import numpy as np

f = read_file("capture.bin")
chunk = f.contents.chunks[0]

print(f"Sample rate: {chunk.sample_rate/1e6:.1f} MHz")
print(f"Duration: {chunk.end_time - chunk.begin_time:.3f}s")
print(f"Initial state: {'HIGH' if chunk.initial_state else 'LOW'}")
print(f"Transitions: {len(chunk.transition_times)}")

Step 2: Analyze Timing Patterns

python
times = np.array(chunk.transition_times)
durations_us = np.diff(times) * 1e6  # microseconds

# Separate HIGH and LOW durations
high_idx = 0 if chunk.initial_state == 0 else 1
high_durations = durations_us[high_idx::2]
low_durations = durations_us[(1-high_idx)::2]

print(f"HIGH pulses: min={min(high_durations):.1f}us, max={max(high_durations):.1f}us")
print(f"LOW gaps: min={min(low_durations):.1f}us, max={max(low_durations):.1f}us")

# Find unique timing values (cluster detection)
unique_high = sorted(set(round(d, -1) for d in high_durations))  # Round to 10us
unique_low = sorted(set(round(d, -1) for d in low_durations))
print(f"HIGH clusters: {unique_high}")
print(f"LOW clusters: {unique_low}")

Step 3: Identify Protocol

Based on timing patterns:

  • UART: Consistent bit periods, durations are multiples of base period, idles HIGH
  • SPI/I2C: us-scale timing, needs clock signal analysis, look for regular patterns
  • 1-Wire: Reset pulses ~480us, data pulses 1-120us

Step 4: Decode

Once protocol is identified, decode based on protocol rules. For unknown/custom protocols, analyze the timing clusters and bit patterns to determine encoding scheme.

UART Decoding Example

python
from saleae.mso_api.binary_files import read_file
import numpy as np

f = read_file("uart_capture.bin")
chunk = f.contents.chunks[0]
times = np.array(chunk.transition_times)

BAUD = 115200
BIT_PERIOD = 1 / BAUD

def decode_uart_byte(start_time, times, bit_period):
    """Decode a single UART byte starting at start_time."""
    byte_val = 0
    for bit_num in range(8):
        # Sample at center of each bit (1.5, 2.5, 3.5... bit periods from start)
        sample_time = start_time + (1.5 + bit_num) * bit_period
        # Find state at sample_time
        idx = np.searchsorted(times, sample_time)
        state = (chunk.initial_state + idx) % 2
        if state:
            byte_val |= (1 << bit_num)  # LSB first
    return byte_val

# Find start bits (falling edges when idle HIGH)
decoded_bytes = []
i = 0
while i < len(times) - 1:
    # Look for falling edge (start bit)
    if chunk.initial_state == 1 or i > 0:
        byte_val = decode_uart_byte(times[i], times, BIT_PERIOD)
        decoded_bytes.append(byte_val)
        # Skip to next potential start bit (after stop bit)
        i += 1
        while i < len(times) and times[i] < times[i-1] + 10 * BIT_PERIOD:
            i += 1
    else:
        i += 1

print("Decoded:", bytes(decoded_bytes))

CTF Tips

  1. Unknown protocol: Start with analyze_protocol.py --clusters to see timing distribution
  2. Multiple channels: Export each channel separately, identify clock vs data lines
  3. Inverted signals: Some captures have inverted logic levels
  4. Timing variations: Real hardware has jitter, use threshold-based detection
  5. Partial captures: Check if capture starts mid-transmission
  6. Custom protocols: Look for repeating patterns, identify sync/framing bytes

Troubleshooting

"No module named 'saleae.mso_api'"

First verify it's truly missing:

bash
python3 -c "from saleae.mso_api.binary_files import read_file"

Only if the import fails, install it:

bash
pip install saleae-mso-api

Empty or corrupt file

Check file size and try re-exporting from Saleae Logic software.

No transitions detected

  • Signal may be constant (stuck high/low)
  • Check if correct channel was exported
  • Verify trigger settings in original capture

Timing seems wrong

  • Check sample rate matches original capture settings
  • Verify time units (seconds vs milliseconds vs microseconds)

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Logicmso AI skill do?

Analyze digital and analog captures from Saleae Logic MSO devices. Decode protocols like UART, SPI, I2C from exported binary files. Use when analyzing logic analyzer captures for CTF challenges, hardware reverse engineering, or protocol decoding.

Why use Logicmso on TypingMind?

Because you install it once and use it with any model. Logicmso is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Logicmso in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/BrownFineSecurity/iothackbot/tree/master/skills/logicmso. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Logicmso?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Logicmso?

As many as you like. As long as a model supports skills, you can use Logicmso with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Logicmso AI skill free?

Yes. It is published on GitHub by BrownFineSecurity under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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