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name: pyrit description: "Use Microsoft PyRIT for generative-AI red teaming, prompt targets, scorers, converters, datasets, attack scenarios, scanner CLI, backend, and troubleshooting." disable-model-invocation: true metadata: disco-role: operating license: MIT

PyRIT repo skill

Use this skill when a task names PyRIT, pyrit_scan, pyrit_backend, CoPyRIT, Python Risk Identification Tool, AI red teaming, LLM robustness testing, prompt targets, scorers, converters, seed datasets, attack techniques, or scenario campaigns.

This skill is for operating PyRIT as a package. It does not authorize live red-team execution against external systems; require explicit user scope, target approval, credentials handling, and data rules before sending prompts to live services.

First checks

1. Read references/repo-provenance.md if you need to decide whether this skill is current for a checkout.
2. Read references/install-configuration.md for package install, optional extras, PyRIT home/config, and no-secret import checks.
3. Run scripts/pyrit_api_smoke.py when you need a no-secret installed-package sanity check.
4. Use references/troubleshooting.md for cross-cutting install/import, optional dependency, credential, CLI/backend, and layer-boundary problems.

Route by task

User need	Read
Initialize PyRIT, choose InMemory/SQLite/Azure SQL, load config/env files, inspect registries/models/output helpers	setup-memory-core
Build converters, converter stacks, message normalization, seed prompts/datasets, and data/YAML schemas	converters-datasets
Configure prompt targets, target capabilities, scorers, score aggregation/evaluation, auth/rate-limit/model-service troubleshooting	targets-scorers
Choose/configure attacks, executors, attack techniques, scenarios, benchmarks, prompt generators, concurrency/retries/results	attacks-scenarios
Use pyrit_scan, pyrit_shell, pyrit_backend, backend REST service, scanner workflows, or CoPyRIT GUI operations	cli-backend-scanner

Safe operating defaults

• Prefer no-secret import/signature checks before running examples or scans.
• Prefer offline components (TextTarget, rule scorers, offline converters, in-memory/temporary SQLite memory) for smoke tests.
• Treat OpenAI/Azure/HuggingFace/LiteLLM/HTTP/Playwright/Azure SQL paths as credentialed or service-bound.
• Treat GCG, HuggingFace, media, browser, and benchmark paths as optional/heavy unless the user explicitly selects them.
• Keep secrets out of prompts, command lines, logs, generated examples, and persisted memory unless the user explicitly authorizes the storage location.

Minimal Python check

import pyrit
print(pyrit.__version__)
from pyrit.converter import Base64Converter
from pyrit.prompt_target import TextTarget
from pyrit.score.true_false.substring_scorer import SubStringScorer

For deeper checks, run the bundled scripts in the relevant sub-skill. They are designed to work from any directory where PyRIT is installed and do not depend on the original repository checkout.