run-pdt 0.1.0

Process Deploy Tool: Git-native runtime for state-bounded operational AI workflows.

PDT (Process Deploy Tool)

Git-native runtime for state-bounded operational AI workflows.

pdt is a developer-focused Command Line Interface (CLI) and runtime engine designed to govern, parse, lint, and execute standard-conforming PROCESS.md files.

Inspired by analytics engineering tools like dbt, pdt decouples general-purpose LLM capabilities (skills) from operational business logic and policies (processes). It provides deterministic execution boundaries, step-by-step state preservation, and human-in-the-loop (HITL) gates.

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Core Philosophy: Workflow Engineering

Most operational teams do not need another open-ended autonomous agent. They require a reliable framework to transform recurring, high-stakes business processes into structured, version-controlled workflows.

PDT establishes this via Workflow Engineering—pairing deterministic execution structures with bounded model reasoning:

• Decoupled Skills & Processes: Skills describe how to perform a reusable task (general/mechanical); processes describe what should happen, in what order, and under what constraints (contextual/governing).
• Markdown as Code: PROCESS.md files are the "SQL of operations"—readable by non-developer process owners, versioned in Git, and executable by a computer.
• Deterministic Bounding: Instead of letting an LLM navigate a workflow in an open-ended loop (resulting in unpredictable execution costs and loops), the PDT runtime runs one isolated step at a time, enforcing boundaries and security.
• Human-Centered Exceptions: When exceptions or gates are hit, the runtime halts, saves state, and alerts humans to verify or approve the execution.

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Workspace Layout

A conforming PDT workspace is organized as follows:

/workspace
├── pdt.yaml                          # Workspace configuration
├── processes/                        # Executable workflows
│   └── growth_experiment_review/
│       └── PROCESS.md
├── skills/                           # Capability guides
│   └── experiment-analysis/
│       └── SKILL.md
├── tools/                            # Code execution units
│   └── experiment_lookup/
│       ├── tool.yaml
│       └── main.py
└── schemas/                          # Data validation contracts
    └── experiment-summary.schema.json

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Anatomy of PROCESS.md

An executable SOP contains three main components:

---
id: growth_experiment_review
name: Growth Experiment Review
version: 0.1.0
owner: growth-team
status: active
runtime: pdt.process.v0
---
# Description
A workflow to review growth experiments, aggregate conversions, and perform high-level evaluation before approval.

# Workflow
## Step 1: Load active experiments
Lookup all active experiments using the tool `tool/experiment_lookup`.

## Step 2: Assess statistical performance
Evaluate the total conversion metrics using `skill/experiment-analysis` and construct a structured JSON summary matching `schema/experiment-summary`.

## Step 3: Approve experiment
Review the assessment and request final business approval before closing.

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CLI Commands & Usage

Install the PDT package:

pip install run-pdt

1. pdt init

Initialize a new standard workspace directory layout with default configuration:

pdt init [workspace_path]

2. pdt lint

Validate workspace config, verify step index ordering, and resolve all inline reference links to confirm they point to valid skills, tools, processes, and schemas:

pdt lint processes/growth_experiment_review/PROCESS.md

3. pdt parse

Parse a PROCESS.md file and output a clean Abstract Syntax Tree (AST) in JSON format:

pdt parse processes/growth_experiment_review/PROCESS.md

4. pdt run

Execute the workflow steps sequentially. PDT automatically runs local tools, saves evidence, compiles bounded prompts, and pauses when a human gate (e.g. "approval") is encountered.

• Execute workflow fully:

  pdt run processes/growth_experiment_review/PROCESS.md --input metrics.json
  

• Run a single step only:

  pdt run processes/growth_experiment_review/PROCESS.md --step 2
  

• Resume a paused workflow:

  pdt run --resume run_98a72f1c
  

5. pdt deploy

Package the workspace and generate container/deployment configurations:

pdt deploy --target docker --dry-run

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Webhook Server Daemon

Deploy the workspace as a serverless runtime using the built-in FastAPI daemon:

uvicorn pdt_cli.server:app --port 8080

This exposes REST API endpoints to trigger and manage workflows remotely:

• POST /run/{process_id}: Trigger step execution with input payload.
• GET /status/{run_id}: Check status and inspect run evidence.
• POST /approve/{run_id}: Submit approval inputs to resume paused states.