Scout OSS

Scout OSS is a local web research agent and mission-driven intelligence scanner.

It searches multiple sources, follows promising leads, and writes structured markdown briefs. Use it for open-source discovery, trend monitoring, opportunity scouting, builder research, and technical signal tracking.

Video demo

• inline preview: media/scout-oss-quickstart-preview.gif
• full video: media/scout-oss-quickstart.mp4

Why Scout

• local-first: run it from your machine, keep your workflow in files
• markdown-first: every run ends as a readable brief, not a chat log
• mission-driven: tell it what to hunt, not just what to answer
• multi-source: combine Hacker News, GitHub, Jina Reader, and more as adapters are added
• skill-friendly: use it directly from CLI or as an OpenCode skill

Best use cases

• find open-source AI tools worth tracking
• monitor technical trends and product launches
• scout grants, fellowships, hackathons, and programs
• discover builders publishing interesting work in public
• generate recurring research briefs for a niche

What works today

• autonomous run planning
• exploration loop with tool calls
• markdown brief generation
• local memory file for dedupe across runs
• working adapters: Hacker News, GitHub, Jina Reader

Current limitations

• many adapters are still being ported: exa, reddit, youtube, tavily, gemini, linkedin, lobsters, x
• GitHub trending is approximated through the GitHub Search API
• right now the strongest experience is technical signal scanning with the working adapters

Install

git clone https://github.com/lout33/scout-oss.git scout-oss
cd scout-oss
pip install -r requirements.txt
cp .env.example .env

If you already downloaded or copied the folder, start here:

cd scout-oss
pip install -r requirements.txt
cp .env.example .env

Then edit .env and set the backend you want.

Model backends

Scout supports three backend paths:

• gpt-* models through Codex auth
• Anthropic models through ANTHROPIC_API_KEY
• Bedrock-hosted Anthropic models through AWS_BEARER_TOKEN_BEDROCK

Examples:

• SCOUT_MODEL=gpt-5.4
• SCOUT_MODEL=claude-sonnet-4-6
• SCOUT_MODEL=claude-haiku-4-5-20251001
• SCOUT_MODEL=sonnet
• SCOUT_MODEL=haiku

Selection logic:

• if the model starts with gpt- and Codex auth exists, Scout uses Codex
• else if AWS_BEARER_TOKEN_BEDROCK is set, Scout uses Bedrock
• else if ANTHROPIC_API_KEY is set, Scout uses Anthropic directly

Minimum useful setup:

• SCOUT_MODEL=gpt-5.4
• codex login --device-auth

Optional env vars:

• ANTHROPIC_API_KEY
• AWS_BEARER_TOKEN_BEDROCK
• JINA_API_KEY
• GITHUB_TOKEN
• SCOUT_OUTPUT_DIR
• SCOUT_CONTEXT_DIR
• SCOUT_MEMORY_FILE
• SCOUT_LOOP_INTERVAL_MINUTES

Quickstart

Run a direct scan:

python agent.py --mission "Find 3 interesting technical signals from today" --tokens 5000

Cheap helper:

./quick.sh --mission "Find 2 interesting GitHub repos from this week"

Standard wrapper:

./scout.sh --tokens 20000 --mission "Find open-source agent tooling worth tracking"

Three parallel scans:

./daily.sh --tokens 10000

Loop mode:

./scout.sh --tokens 10000 --interval 60 --loop --mission "Track daily technical signals"

Example missions

• Find 3 interesting technical signals from the last 48 hours
• Find open-source AI tooling shipped this week
• Find grants, fellowships, or programs with real deadlines
• Find builders publishing concrete experiments in public
• Find AI developer tools with real traction on GitHub and Hacker News

OpenCode skill usage

Scout can also run as an OpenCode skill.

Expected layout:

scout-oss/
  SKILL.md
  agent.py
  scout.sh
  quick.sh
  daily.sh
  adapters/

Then use the skill entrypoint or run the commands directly from the repo folder.

Output

By default Scout writes markdown briefs to:

• ./inbox/

Sample output shape:

• examples/sample-brief.md
• quick video walkthrough: media/scout-oss-quickstart.mp4

Working adapters

• adapters/hn/scripts/hn.py
• adapters/github/scripts/githunt.py
• adapters/jina/scripts/jina_read.py

Repo map

• agent.py - core scout engine
• scout.sh - standard wrapper
• quick.sh - cheap helper
• daily.sh - three parallel missions
• templates/missions/ - reusable mission prompts
• examples/sample-brief.md - sample output shape
• SKILL.md - OpenCode skill entry

First-run smoke test

This should work after setup:

python agent.py --mission "Use hn_front, github_trending, and jina_read to find 2 interesting technical signals from today and write a brief." --tokens 2500

If setup is correct, Scout writes a markdown brief to inbox/.

Positioning

Scout OSS is for people who want a local research agent, not just an LLM chat window. If you care about research automation, open-source discovery, technical trend monitoring, or markdown-first agent workflows, this repo is for you.