pretext

Turn a one-page persona into a runnable social-engineering exercise.

Narrative, opening message, escalation path, and the red flags defenders should have caught, generated, validated, then rendered where you need it.

Authorized training only. Everything produced is a fictional exercise. Domains, names, and systems are invented unless you say otherwise. pretext never sends anything to a target; it writes the material and the analysis. Run an exercise against your own people only after your security team agrees to it, and check the lures before sending.

Why

Security-awareness decks are generic ("you receive an email with a typo"), so staff learn to spot a template rather than a technique. A useful exercise is grounded in one role at one company: what that person controls, who they trust, and what an operator would do next.

Doing that by hand costs an afternoon per persona. pretext takes a YAML profile and produces the exercise in one call, defender's side included, so training value does not depend on whoever wrote the scenario remembering to add it.

What you get

docs/running-an-exercise.md covers authorization, delivery, scoring and the debrief, which is the half the tool cannot do.

Every output carries a disclaimer. sample-output.md is a complete Markdown export; docs/demo.png is the terminal view.

Install

uvx pretext-sim generate persona.yaml     # no install
pipx install pretext-sim                  # or a permanent command

Python 3.10+. The distribution is pretext-sim; the command is pretext. For the source tree: uv sync --extra dev then uv run pretext.

Quickstart

pretext example -o persona.yaml            # fictional profile to edit
pretext validate persona.yaml              # schema check, no API call
pretext generate persona.yaml --dry-run    # resolved config + exact prompt, no call
pretext generate persona.yaml -f markdown -o exercise.md

Point it at any OpenAI-compatible chat completions endpoint with a flag, env var, or .env:

export PRETEXT_API_KEY=sk-...
export PRETEXT_BASE_URL=http://localhost:11434/v1   # local proxy, if you prefer
export PRETEXT_MODEL=gpt-oss:20b

Or name a known host instead of the URL: --provider openai|openrouter|venice|ollama|lmstudio (PRETEXT_PROVIDER), which fills the base URL. An explicit --base-url wins. Local providers need no key.

pretext config shows what resolves, with the key masked.

Profile

Only four fields are required (name, role, company, communication_style). The rest sharpen the result.

name: Dana Iyer
role: Senior Payroll Analyst
company: Northwind Logistics
communication_style: Brisk and formal, mixes payroll shorthand with chatty asides
department: People Operations
tenure: 3 months
reports_to: Marta Kwan
systems: [Workday, the payroll mailbox]
notes: New enough to the team to be unsure of process.

Optional: department, tenure, reports_to, systems, notes. Unknown keys are rejected so typos surface early. Fixture personas live in tests/fixtures/profiles.

Command reference

Flags on generate:

Exit codes: 0 success, 1 bad profile/config/API/schema failure, 2 usage error, 130 interrupted.

--from-file accepts a --format json export, a bare simulation object, or a raw chat completion body, so formatting work stays free and offline.

Diagnose first

pretext doctor              # config, TLS, /models, JSON mode
pretext doctor --full       # also runs one real simulation through the schema

Exits 1 on any failure and prints the provider's own error, so a wrong model name or a blocked key shows up before a batch run wastes retries.

Batch runs

For a whole team, point batch at a directory and it writes one file per profile, continuing past failures:

pretext batch profiles/ --out-dir packet/ --format markdown

Progress and a wrote 7/9 summary go to stderr, the failure reasons are listed, and the exit code is 1 if anything failed. Endpoint flags are the same as generate; --sections, --format and --usage apply to the run as a whole.

Configuration

First hit wins:

1. Flags (--base-url, --model, --api-key, --provider)
2. Environment: the same names under PRETEXT_ (PRETEXT_API_KEY, PRETEXT_MODEL, ...)
3. .env in the working directory
4. OPENAI_API_KEY for the key, then https://api.openai.com/v1 and gpt-4o-mini

The key is never printed and never written to output.

How it works

profile.yaml ─> Profile ─> prompt ─> POST /chat/completions ─> extract JSON
                                                                    │
     rich | markdown | json  <─ render <─ validate <────────────────┘

Model output is untrusted. It is parsed out of fences or prose, validated against a strict pydantic schema, and only then rendered. Escalation stages must run 1..n with no gaps, red_flags needs at least two entries, and unknown keys are rejected, so a loose model fails loudly instead of producing a half-filled exercise.

Diagnostics go to stderr, keeping stdout pipeable: pretext generate p.yaml -f json | jq.

Releasing

Tagging vX.Y.Z runs release.yml: it checks the tag against __version__, gates on lint, format, types and tests, builds, then publishes a release whose notes come from CHANGELOG.md. Version bumps and changelog sections land before the tag.

Development

uv run pytest --cov=pretext # offline: MockTransport and a stubbed client
uv run ruff check src tests
uv run mypy

The suite needs no key. conftest.py chdirs each test into a temp directory so your own .env cannot leak in, and pins the terminal width so Rich reflow stays stable.

scripts/capture_corpus.py refreshes tests/fixtures/corpus, which tests/test_corpus.py checks for structural promises.

AGENTS.md documents the layout, the hardening rules, and the traps for agent contributors. CONTRIBUTING.md covers process; security reports follow SECURITY.md.

Status

Pre-1.0: the CLI surface and JSON schema can still move between minor versions; CHANGELOG.md records what changed.

Output has been checked against gpt-4o-mini, gemini-2.5-flash, claude-haiku-4.5 and llama-3.3-70b through OpenRouter, plus venice-uncensored-1-2 and qwen3-235b-a22b-instruct on Venice. All six held the JSON contract and used distinct channels. Weak models will still need prompt tuning, and the suite covers the pipeline rather than any one model's prose.

Not planned: delivery to targets, click tracking, an SMTP client. pretext writes the exercise; sending stays your job.

License

MIT. See LICENSE.