Pactgen

pip install cognis-pactgen
pactgen build proposal.yaml -o proposal.html   # rendered doc + math check
pactgen build proposal.yaml --check            # CI gate: non-zero on bad math

🔎 Example output

Real, reproducible output from the tool — runs offline:

$ pactgen-emit --version
pactgen 0.3.8

$ pactgen-emit --help
usage: pactgen [-h] [--version] [--format {table,json,csv}] COMMAND ...

Generate proposals/SOWs from a YAML scope + pricing table to HTML, with line-item math checks.

positional arguments:
  COMMAND
    build               Parse a proposal spec, validate the math, and render
                        HTML.

options:
  -h, --help            show this help message and exit
  --version             show program's version number and exit
  --format {table,json,csv}
                        Output format for the summary/report: table (default),
                        json, or csv. Accepted before or after the subcommand.

Example: python -m pactgen build proposal.yaml -o out.html

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

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

{
    "pact": {
        "provider": "Example Inc.",
        "consumer": "PactGen",
        "version": "1.0"
    },
    "findings": [
        {
            "id": "1234567890",
            "title": "Suspicious Network Activity",
            "description": "Anomalous network traffic detected on port 443",
            "type": "indicator",
            "category": "network"
        }
    ]
}

Usage — step by step

pactgen turns a YAML proposal/SOW spec into a rendered HTML document while validating every line-item total. Console script: pactgen (or python -m pactgen).

1. Install (editable from a clone, or as a package):

   pip install -e .
   

2. Build a proposal — parse the spec, validate the math, and render HTML:

   pactgen build demos/01-basic/proposal.yaml -o proposal.html
   

3. Gate the math only (no HTML) — exits non-zero if any line-item total is wrong:

   pactgen build demos/01-basic/proposal.yaml --check
   

4. Read the output — use --format json for a machine-readable report you can pipe. --format is accepted before or after the build subcommand:

   pactgen build proposal.yaml --format json | jq '.totals.grand_total, .issues'
   pactgen --format json build proposal.yaml | jq '.ok'
   

   issues is empty when the math checks out; a non-empty list means a mismatch.
5. Export to CSV — drop line items + totals straight into a spreadsheet/ERP:

   pactgen build proposal.yaml --format csv -o bom.csv
   

6. Automate in CI — fail the build when totals don't reconcile:

   # .github/workflows/proposals.yml
   - run: pip install -e .
   - run: for f in proposals/*.yaml; do pactgen build "$f" --check || exit 1; done
   

Contents

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

Why pactgen?

Proposals diffable in git with reproducible builds — the same scope file always renders the same dollar total, so no more copy-paste pricing errors slipping to a client.

pactgen is single-purpose, scriptable, and self-hostable: feed it a YAML scope + pricing table, get the format your workflow already speaks (HTML · table · JSON · CSV), gate CI on the math, and let agents drive it over MCP.

Features

• ✅ Dependency-free YAML-subset parser (scalars, nested maps, lists of maps)
• ✅ Forgiving field aliases — name/item, qty/quantity, unit_price/price/rate
• ✅ Deterministic line-item + grand-total math check with cent tolerance
• ✅ Input validation — negative qty/price, discount out of 0..100, stated-total mismatch
• ✅ Self-contained HTML render (no external JS/CSS); $/€/£ currency symbols
• ✅ Machine-readable JSON and spreadsheet-ready CSV exporters
• ✅ --format accepted before or after the subcommand
• ✅ CI gate: non-zero exit on any math/validation issue
• ✅ Runs on Linux/macOS/Windows · Docker · devcontainer
• ✅ Ports in Python, JavaScript, Go, and Rust (ports/)

Quick start

pip install cognis-pactgen
pactgen --version
pactgen build demos/01-basic/proposal.yaml --check          # math gate (CI)
pactgen build demos/01-basic/proposal.yaml --format json    # machine-readable
pactgen build demos/01-basic/proposal.yaml --format csv     # spreadsheet/ERP
pactgen build demos/01-basic/proposal.yaml -o proposal.html # client-ready HTML

Example

$ pactgen build demos/01-basic/proposal.yaml
Autonomous Trading Infrastructure — Statement of Work  (Greenway Engineering LLC -> Acme Robotics, Inc., 2026-06-08)
------------------------------------------------------------
Item                               Qty      Unit       Total
Backend engineering                 40    150.00     6000.00
Infrastructure / DevOps             20    200.00     4000.00
UX design                           16    125.00     2000.00
Project management                  30    200.00     6000.00
------------------------------------------------------------
                                        Subtotal    18000.00
                                        Discount    -1800.00
                                             Tax     1336.50
                                     TOTAL (USD)    17536.50

MATH CHECK FAILED (2 issue(s)):
  - UX design: Line total mismatch (qty 16.0 x 125.0) (expected 2000.00, found 2500.00)
  - grand_total: Stated grand total does not match computed total (expected 17536.50, found 18000.00)

Demos — real-use-case scenarios

Each folder under demos/ is a self-contained scenario: a real-format proposal.yaml plus a SCENARIO.md with the story, the exact run command, and the expected output. Run any of them straight from a clone.

Demo	Scenario	Gate
01-basic	Consulting SOW with a bad line total and a wrong grand total	❌ fails
02-clean-saas-retainer	Monthly managed-SaaS retainer, 5% loyalty discount	✅ passes
03-eur-fixed-bid	EUR fixed-bid web redesign with 19% VAT (€ rendering)	✅ passes
04-discount-rounding	Fractional .99/.50 pricing + volume discount, cent rounding	✅ passes
05-sow-line-error	T&M SOW with one fat-fingered line total	❌ fails
06-field-aliases	item/quantity/rate aliases + nested vendor/client maps (GBP)	✅ passes
07-invalid-inputs	Messy export: negative qty, discount > 100%, total mismatch	❌ fails
08-csv-export	Hardware + labor BOM exported to CSV for procurement	✅ passes
09-ci-batch	Batch-gate a folder of proposals in CI (one member fails)	❌ fails

# Try the CSV exporter on the bill-of-materials demo:
pactgen build demos/08-csv-export/proposal.yaml --format csv

# Watch the gate catch a single bad line:
pactgen build demos/05-sow-line-error/proposal.yaml --check; echo "exit=$?"

Architecture

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

Use it from any AI stack

pactgen is interoperable with every popular way of using AI:

• MCP server — pactgen mcp (Claude Desktop, Cursor, Cognis.Studio, uncensored-fleet)
• OpenAI-compatible / JSON — pipe pactgen build proposal.yaml --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 + JSON/CSV for non-AI pipelines

How it compares

	Cognis pactgen	Pandoc + Typst, echoing PandaDoc
Self-hostable, no account	✅	varies
Single command, zero config	✅	⚠️
JSON + CSV + math gate for CI	✅	varies
MCP-native (AI agents)	✅	❌
Polyglot ports (JS/Go/Rust)	✅	❌
Open license	✅ COCL	varies

Built in the spirit of Pandoc + Typst, echoing PandaDoc/Proposify, re-framed the Cognis way. Missing a credit? Open a PR.

Integrations

Pipes into your stack: JSON for anything, CSV for spreadsheets/ERP, an MCP server (pactgen mcp) for AI agents, and pactgen-emit to forward results via cognis-connect (Slack/webhook/brief). See docs/INTEGRATIONS.md.

Install — every way, every platform

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

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.
• 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

Contributing

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

⭐ If pactgen 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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