INTROBOT

Find warm-intro paths through your team's combined network graph and draft double-opt-in intro requests from a single contacts manifest.

Part of the Cognis Neural Suite.

pip install cognis-introbot
introbot scan .            # → prioritized findings in seconds

Usage — step by step

1. Install:

   pip install introbot

2. Find the warmest intro path to a target through your team's combined network — -m manifest is JSON or CSV (- for stdin):

   introbot path --manifest contacts.json --target "Dana Reyes"

   Output shows the hop chain, total warmth, and each introducer -> introducee step.

3. Constrain the sources — restrict which teammates can originate the intro (repeatable --source):

   introbot path -m contacts.csv -t "Dana Reyes" --source Alice --source Bob

4. Rank super-connectors — find the network's most-connected hubs:

   introbot connectors -m contacts.json --top 10

5. Automation — JSON output plus the exit code (0 = path found, 1 = no warm path, 2 = bad manifest) makes a clean CI gate:

   introbot path -m contacts.json -t "Dana Reyes" --format json | jq '.found'

Contents

• Why introbot? · Features · Quick start · Example · Architecture · AI stack · How it compares · Integrations · Install anywhere · Related · Contributing

Why introbot?

Self-hosted relationship graph — keep your investor/partner network private on your own infra and surface the shortest warm path to any target company.

introbot is single-purpose, scriptable, and self-hostable: point it at a target, get prioritized results in the format your workflow already speaks (table · JSON · SARIF), gate CI on it, and let agents drive it over MCP.

↑ back to top

Features

• ✅ Load Manifest
• ✅ Build Graph
• ✅ Find Intro Path
• ✅ Rank Connectors
• ✅ Runs on Linux/macOS/Windows · Docker · devcontainer
• ✅ Ports in Python, JavaScript, Go, and Rust (ports/)

↑ back to top

Quick start

pip install cognis-introbot
introbot --version
introbot scan .                       # scan current project
introbot scan . --format json         # machine-readable
introbot scan . --fail-on high        # CI gate (non-zero exit)

↑ back to top

Example

$ introbot scan .
  [HIGH    ] INT-001  example finding             (./src/app.py)
  [MEDIUM  ] INT-002  another signal              (./config.yaml)

  2 findings · risk score 5 · 38ms

↑ back to top

Architecture

flowchart LR
  IN[capture / scan] --> P[introbot<br/>parse + map]
  P --> OUT[report]

Loading

↑ back to top

Use it from any AI stack

introbot is interoperable with every popular way of using AI:

• MCP server — introbot mcp (Claude Desktop, Cursor, Cognis.Studio, uncensored-fleet)
• OpenAI-compatible / JSON — pipe introbot scan . --format json into any agent or LLM
• LangChain · CrewAI · AutoGen · LlamaIndex — wrap the CLI/JSON as a tool in one line
• CI / scripts — exit codes + SARIF for non-AI pipelines

↑ back to top

How it compares

	Cognis introbot	Affinity
Self-hostable, no account	✅	varies
Single command, zero config	✅	⚠️
JSON + SARIF for CI	✅	varies
MCP-native (AI agents)	✅	❌
Polyglot ports (JS/Go/Rust)	✅	❌
Open license	✅ COCL	varies

Built in the spirit of Affinity/Boomerang's intro paths, built on networkx, re-framed the Cognis way. Missing a credit? Open a PR.

↑ back to top

Integrations

Pipes into your stack: SARIF for code-scanning, JSON for anything, an MCP server (introbot mcp) for AI agents, and a webhook forwarder for SIEM/Slack/Jira. See docs/INTEGRATIONS.md.

↑ back to top

Install — every way, every platform

pip install "git+https://github.com/cognis-digital/introbot.git"    # pip (works today)
pipx install "git+https://github.com/cognis-digital/introbot.git"   # isolated CLI
uv tool install "git+https://github.com/cognis-digital/introbot.git" # uv
pip install cognis-introbot                                          # PyPI (when published)
docker run --rm ghcr.io/cognis-digital/introbot:latest --help        # Docker
brew install cognis-digital/tap/introbot                             # Homebrew tap
curl -fsSL https://raw.githubusercontent.com/cognis-digital/introbot/main/install.sh | sh

Linux	macOS	Windows	Docker	Cloud
scripts/setup-linux.sh	scripts/setup-macos.sh	scripts/setup-windows.ps1	docker run ghcr.io/cognis-digital/introbot	DEPLOY.md (AWS/Azure/GCP/k8s)

↑ back to top

Related Cognis tools

• warmline — Score and rank inbound/outbound leads from a YAML rulebook, emitting a ranked queue as JSON/CSV for your SDRs and CI gates.
• coldforge — Render personalized cold-outreach sequences from Markdown templates + a contacts CSV, with spam-score linting and per-send dry-run preview.
• pactgen — Generate branded sales proposals and SOWs from a YAML scope file + pricing table into PDF/HTML, with a deterministic line-item math check.
• crmsync — Bidirectional, idempotent sync of contacts/deals between a local SQLite source-of-truth and CRM APIs (HubSpot/Pipedrive/Salesforce) via one config.
• dripcheck — Lint email sequences and drip campaigns for deliverability: SPF/DKIM/DMARC, link health, unsubscribe presence, and CAN-SPAM/GDPR compliance.
• dealflow — Model your sales pipeline as a YAML state machine and compute conversion rates, stage velocity, and weighted forecast straight from CRM exports.

Explore the suite → 🗂️ all 170+ tools · ⭐ awesome-cognis · 🔗 cognis-sources · 🤖 uncensored-fleet · 🧠 engram

↑ back to top

Contributing

PRs, new rules, and demo scenarios are welcome under the collaboration-pull model — see CONTRIBUTING.md and SECURITY.md.

⭐ If introbot saved you time, star it — it genuinely helps others find it.

Interoperability

{} composes with the 300+ tool Cognis suite — JSON in/out and a shared OpenAI-compatible /v1 backbone. See INTEROP.md for the suite map, composition patterns, and reference stacks.

License

Source-available under the Cognis Open Collaboration License (COCL) v1.0 — free for personal, internal-evaluation, research, and educational use; commercial / production use requires a license (licensing@cognis.digital). See LICENSE.

---

_{Cognis Digital · one of 170+ tools in the Cognis Neural Suite · Making Tomorrow Better Today}