Score and rank inbound/outbound leads from a YAML rulebook, emitting a ranked queue as JSON/CSV for your SDRs and CI gates.
Part of the Cognis Neural Suite.
pip install cognis-warmline
warmline scan . # → prioritized findings in seconds- Install the CLI (console script
warmline):pip install cognis-warmline
- Score & rank leads against a YAML rulebook (leads file may be
.csv,.json, or.yaml):warmline score --rules rulebook.yaml --leads leads.csv
- Read the ranked queue as JSON for a CRM sync or pipeline, and trim to the top N:
warmline score -r rulebook.yaml -l leads.json --format json --top 25 > queue.json - Gate on a tier or score — exit 2 if no lead reaches the threshold:
warmline score -r rulebook.yaml -l leads.csv --min-tier hot warmline score -r rulebook.yaml -l leads.csv --min-score 80
- Automate in CI — fail the job unless at least one lead is hot:
- run: pip install cognis-warmline - run: warmline score -r rulebook.yaml -l leads.csv --format json --min-tier hot
- Why warmline? · Features · Quick start · Example · Architecture · AI stack · How it compares · Integrations · Install anywhere · Related · Contributing
A self-hostable, git-versioned lead-scoring engine — every score change is a reviewable PR diff, killing the 'why did this lead get routed here' black box.
warmline 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.
- ✅ Parse Simple Yaml
- ✅ Load Rulebook
- ✅ Load Rulebook File
- ✅ Load Leads
- ✅ Load Leads File
- ✅ Score Lead
- ✅ Score Leads
- ✅ Rank
- ✅ Runs on Linux/macOS/Windows · Docker · devcontainer
- ✅ Ports in Python, JavaScript, Go, and Rust (
ports/)
pip install cognis-warmline
warmline --version
warmline scan . # scan current project
warmline scan . --format json # machine-readable
warmline scan . --fail-on high # CI gate (non-zero exit)$ warmline scan .
[HIGH ] WAR-001 example finding (./src/app.py)
[MEDIUM ] WAR-002 another signal (./config.yaml)
2 findings · risk score 5 · 38ms
flowchart LR
IN[input] --> P[warmline<br/>analyze + score]
P --> OUT[report]
warmline is interoperable with every popular way of using AI:
- MCP server —
warmline mcp(Claude Desktop, Cursor, Cognis.Studio, uncensored-fleet) - OpenAI-compatible / JSON — pipe
warmline scan . --format jsoninto 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
| Cognis warmline | n8n + dbt (declarative scoring) crossed with HubSpot lead scoring | |
|---|---|---|
| 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 n8n + dbt (declarative scoring) crossed with HubSpot lead scoring, re-framed the Cognis way. Missing a credit? Open a PR.
Pipes into your stack: SARIF for code-scanning, JSON for anything, an MCP server (warmline mcp) for AI agents, and a webhook forwarder for SIEM/Slack/Jira. See docs/INTEGRATIONS.md.
pip install "git+https://github.com/cognis-digital/warmline.git" # pip (works today)
pipx install "git+https://github.com/cognis-digital/warmline.git" # isolated CLI
uv tool install "git+https://github.com/cognis-digital/warmline.git" # uv
pip install cognis-warmline # PyPI (when published)
docker run --rm ghcr.io/cognis-digital/warmline:latest --help # Docker
brew install cognis-digital/tap/warmline # Homebrew tap
curl -fsSL https://raw.githubusercontent.com/cognis-digital/warmline/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/warmline |
DEPLOY.md (AWS/Azure/GCP/k8s) |
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.introbot— Find warm-intro paths through your team's combined network graph and draft double-opt-in intro requests from a single contacts manifest.
Explore the suite → 🗂️ all 170+ tools · ⭐ awesome-cognis · 🔗 cognis-sources · 🤖 uncensored-fleet · 🧠 engram
PRs, new rules, and demo scenarios are welcome under the collaboration-pull model — see CONTRIBUTING.md and SECURITY.md.
{} 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.
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.