Quotecraft

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

🔎 Example output

Real, reproducible output from the tool — runs offline:

$ quotecraft-emit --version
quotecraft 0.1.0

$ quotecraft-emit --help
usage: quotecraft [-h] [--version] [--format {table,json}]
                  {render,preview,total} ...

QUOTECRAFT - turn a YAML proposal into a branded PDF quote / SOW.

positional arguments:
  {render,preview,total}
    render              render a YAML proposal to a PDF file
    preview             print a plain-text preview of the proposal
    total               compute and show proposal totals only

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

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

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

{
"timestamp": "2023-02-15T14:30:00Z",
"findings": [
  {
    "id": "1234567890abcdef",
    "title": "Suspicious Network Traffic",
    "description": "Possible malicious activity detected on port 443.",
    "labels": ["network", "suspicious"],
    "objects": [
      {
        "type": "indicator",
        "value": "192.168.1.100"
      },
      {
        "type": "indicator",
        "value": "example.com"
      }
    ]
  }
]
}

Usage — step by step

1. Install the CLI (Python 3.9+):

   pip install git+https://github.com/cognis-digital/quotecraft.git
   

2. Preview a YAML proposal as plain text before rendering:

   quotecraft preview proposal.yaml
   

3. Render the proposal to a branded PDF quote / SOW:

   quotecraft render proposal.yaml -o quote.pdf
   

4. Compute just the totals (e.g. to show numbers in JSON for another system):

   quotecraft --format json total proposal.yaml
   

5. Automate quote generation in CI / a pipeline:

   - name: build quote PDF
     run: |
       pip install git+https://github.com/cognis-digital/quotecraft.git
       quotecraft render proposal.yaml -o dist/quote.pdf
   

Contents

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

Why quotecraft?

agencies + consultants

quotecraft 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.

Features

• ✅ Parse Yaml
• ✅ Build Proposal
• ✅ Load Proposal
• ✅ Compute Totals
• ✅ Money
• ✅ Render Text
• ✅ Render Pdf
• ✅ Runs on Linux/macOS/Windows · Docker · devcontainer
• ✅ Ports in Python, JavaScript, Go, and Rust (ports/)

Quick start

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

Example

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

  2 findings · risk score 5 · 38ms

Architecture

flowchart LR
  IN[input] --> P[quotecraft<br/>analyze + score]
  P --> OUT[report]

Use it from any AI stack

quotecraft is interoperable with every popular way of using AI:

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

How it compares

	Cognis quotecraft	PandaDoc
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 PandaDoc, re-framed the Cognis way. Missing a credit? Open a PR.

Integrations

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

Install — every way, every platform

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

Related Cognis tools

• invoctl — CLI invoicing + payment-link generator with PDF and a local ledger
• churnlens — Self-hosted SaaS metrics — MRR, churn, LTV from Stripe or CSV
• leadforge — Lightweight MCP-native CRM pipeline with email sequences
• boardroom — Investor-update and KPI one-pager generator from your metrics
• seataudit — SaaS license, seat-usage and shadow-IT auditor
• paywatch — Recurring-charge and subscription detector from bank/Plaid CSV

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

Contributing

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

⭐ If quotecraft 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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