recon-oss

Reconnaissance for open source contributors.

Monitors a GitHub repo's merged PRs and open issues, uses a local LLM to generate a daily digest, and personalizes it based on what you're trying to contribute — and remembers your feedback over time.

Built because manually reading 10-20 merged PRs a day to figure out what to work on next is exhausting.

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The idea

When you're trying to contribute to an active OSS project, you have two problems:

1. The repo moves fast — PRs merge daily across multiple subsystems
2. You don't know which open issues the team actually cares about right now

recon-oss solves this by running a daily pipeline that fetches what merged, correlates it with open issues, and asks an LLM to surface contribution opportunities — personalized to your specific goal (e.g. "I want hard backend auth bugs, not frontend work").

It also remembers your feedback. If you reply "focus on issue #442 from now on", the next digest reflects that.

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What's working right now

• Fetches merged PRs from the last 24h via GitHub API
• On first run, bootstraps up to 500 issues and 100 PRs into a local SQLite database
• Every subsequent run does a delta sync — only fetches what changed since last time
• Builds a prompt combining three memory layers and calls a local Ollama model
• Prints the digest to terminal and saves it to data/summary.txt
• Accepts feedback after each run — stored in data/chat.json and injected into the next prompt
• Telegram delivery is wired up but optional

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Architecture

The pipeline runs in five stages:

GitHub API (delta only after first run)
        ↓
   SQLite sync (repo.db)
        ↓
   Prompt assembly
   [ motive.json + chat.json + SQL queries ]
        ↓
   Local LLM (Ollama)
        ↓
   Delivery (terminal / Telegram)
        ↓
   User feedback → stored in chat.json → used in next run

Three memory layers:

Memory	File	What it stores
Motive	data/motive.json	Your contribution goals, subsystems to focus on, what to avoid
Repo state	data/repo.db	All issues and merged PRs, incrementally updated
Chat history	data/chat.json	Past digests and your replies — feeds into next run's prompt

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File structure

recon-oss/
├── run.ts               # pipeline entry point — exported as runPipeline()
├── cli.ts               # interactive CLI entry point (recon-oss binary)
├── config.ts            # all settings live here (repo, LLM, limits)
├── tsconfig.json        # TypeScript compiler config
├── core/
│   ├── sync.ts          # GitHub API fetcher — bootstrap + delta sync
│   ├── context.ts       # builds the LLM prompt from all three memories
│   └── llm.ts           # Ollama / Anthropic API call
├── memory/
│   ├── db.ts            # SQLite read/write for issues and PRs
│   ├── chat.ts          # chat history read/write
│   └── motive.ts        # loads and formats user motive
├── delivery/
│   ├── index.ts         # routes to the right delivery channel
│   ├── cli.ts           # terminal output + feedback prompt
│   └── telegram.ts      # Telegram bot send + reply capture
└── data/                # local state — db and summary gitignored, chat/motive tracked for GH Actions
    ├── repo.db          # gitignored
    ├── motive.json      # tracked (GH Actions persistence)
    ├── chat.json        # tracked (GH Actions persistence)
    └── summary.txt      # gitignored

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Running it

Requirements: Node.js 18+, Ollama running locally with a model pulled.

Via Homebrew

brew tap x15sr71/recon-oss https://github.com/x15sr71/recon-oss-homebrew
brew install recon-oss

Via npm

npm install -g recon-oss
recon-oss
# → select "Initialize recon-oss"

From source

git clone https://github.com/x15sr71/recon-oss
cd recon-oss
npm install
npm run build
node dist/cli.js
# → select "Initialize recon-oss"

The wizard will walk you through:

• GitHub repo to monitor and your PAT (public_repo scope)
• LLM provider — Ollama (local) or Anthropic (Claude)
• Delivery channel — terminal or Telegram
• Your contribution goals, focus subsystems, and what to avoid

Once set up, run the digest:

recon-oss
# → select "Run digest now"

First run bootstraps the database (takes a minute). Every run after that is fast — only syncs what changed.

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What's coming

• recon-oss init — interactive CLI setup wizard, no manual config editing
• npm install -g recon-oss — proper global install
• Cron setup built into the CLI
• Anthropic / Claude as LLM option (set LLM_PROVIDER=anthropic)
• Better prompt tuning — current model sometimes ignores user directives
• Homebrew tap

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Contributing

Note for contributors: data/chat.json and data/motive.json are intentionally tracked in this repo for GitHub Actions persistence. If you clone and use recon-oss locally, these files will contain your personal goals and conversation history. Run this once after cloning to prevent accidental commits:

git update-index --skip-worktree data/chat.json data/motive.json

Open to contributions, especially on:

• Prompt improvements — making the LLM follow user feedback more strictly
• Additional delivery channels (Discord, Slack, email)
• LLM provider adapters (the interface is already modular in core/llm.ts)
• Testing with repos other than Infisical

If you're using it to track a different repo and hit issues, open an issue with the repo name and what broke.

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Why local LLM by default

No API keys needed, no per-run cost, runs offline. The architecture supports cloud LLMs and that's coming — but Ollama first means anyone can run it immediately without a credit card.