COLDFORGE

Render personalized cold-outreach sequences from Markdown templates + a contacts CSV, with spam-score linting and per-send dry-run preview.

Part of the Cognis Neural Suite.

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

🔎 Example output

Real, reproducible output from the tool — runs offline:

$ coldforge-emit --version
coldforge 0.1.0

$ coldforge-emit --help
usage: coldforge [-h] [--version] [--format {table,json}] {render,lint} ...

Outreach-as-code: render personalized cold emails from a template + contacts CSV, with a CI spam linter.

positional arguments:
  {render,lint}
    render              render template over a contacts CSV + lint
    lint                lint a single template/draft file

options:
  -h, --help            show this help message and exit
  --version             show program's version number and exit
  --format {table,json}
                        output format (default: table)

COLDFORGE command-line interface.

Subcommands
-----------
  render   Render a template against a contacts CSV and lint each message.
  lint     Lint a single template/text file (no CSV needed).

Examples
--------
  # Render + lint every contact, pretty table
  coldforge render --template body.txt --contacts contacts.csv

  # CI gate: fail (exit 2) if any message scores above 25
  coldforge render -t body.txt -c contacts.csv --max-score 25 --format json

  # Just lint a draft
  coldforge lint --template body.txt

Exit codes
----------
  0  success, nothing over threshold
  2  one or more messages exceeded --max-score (CI gate failure)
  3  rendering had missing required fields and --strict was set
  1  usage / IO error

Blocks above are real coldforge output — reproduce them from a clone.

Sample result format (illustrative values — run on your own data for real findings):

{
  "finding": {
    "id": "1234567890",
    "title": "Suspicious Network Activity",
    "description": "Potential malicious activity detected on network segment 192.168.1.0/24",
    "created_by": "John Doe",
    "created_at": "2023-02-15T14:30:00Z"
  }
}

Usage — step by step

1. Install:

   pip install -e .

2. Lint a single draft (no CSV needed) with the lint subcommand to check a template/body for spam-trigger issues:

   coldforge lint --template body.txt

3. Render + lint against your contacts with the render subcommand — it substitutes each contact's fields into the template and scores every resulting message:

   coldforge render --template body.txt --contacts contacts.csv

   Add --subject subject.txt to render a subject line too.

4. Read the result. The table shows each email, spam score, grade, any missing fields, and the worst top_issue. Use --format json for the full per-contact payload. Set --max-score to define the gate: the process exits 2 if any message exceeds it (and 3 on missing required fields under --strict):

   coldforge render -t body.txt -c contacts.csv --max-score 25 --format json

5. Use it in CI — block a campaign whose copy scores too spammy:

   coldforge render -t body.txt -c contacts.csv --max-score 25 --format json || {
     echo "Message(s) over spam threshold"; exit 1; }

Contents

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

Why coldforge?

Outreach-as-code: templates live in the repo, the spam-linter runs in CI, and you ship a sequence by merging a PR instead of clicking in a SaaS UI.

coldforge 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.

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Features

• ✅ Load Contacts
• ✅ Find Placeholders
• ✅ Missing Fields
• ✅ Render Template
• ✅ Render All
• ✅ Lint Text
• ✅ Runs on Linux/macOS/Windows · Docker · devcontainer
• ✅ Ports in Python, JavaScript, Go, and Rust (ports/)

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Quick start

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

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Example

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

  2 findings · risk score 5 · 38ms

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Architecture

flowchart LR
  IN[target / manifest] --> P[coldforge<br/>checks + rules]
  P --> OUT[findings (JSON / SARIF)]

Loading

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Use it from any AI stack

coldforge is interoperable with every popular way of using AI:

• MCP server — coldforge mcp (Claude Desktop, Cursor, Cognis.Studio, uncensored-fleet)
• OpenAI-compatible / JSON — pipe coldforge 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

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How it compares

	Cognis coldforge	Hugo
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 Hugo/Jinja templating in the spirit of instantly.ai and lemlist, re-framed the Cognis way. Missing a credit? Open a PR.

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Integrations

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

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Install — every way, every platform

pip install "git+https://github.com/cognis-digital/coldforge.git"    # pip (works today)
pipx install "git+https://github.com/cognis-digital/coldforge.git"   # isolated CLI
uv tool install "git+https://github.com/cognis-digital/coldforge.git" # uv
pip install cognis-coldforge                                          # PyPI (when published)
docker run --rm ghcr.io/cognis-digital/coldforge:latest --help        # Docker
brew install cognis-digital/tap/coldforge                             # Homebrew tap
curl -fsSL https://raw.githubusercontent.com/cognis-digital/coldforge/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/coldforge	DEPLOY.md (AWS/Azure/GCP/k8s)

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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.
• 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

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Contributing

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

⭐ If coldforge 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 ([email protected]). See LICENSE.

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