---

name: giskard-oss description: "Routes Giskard OSS v3 package tasks across checks, LLM providers, agents, scans, runtime setup, troubleshooting, and maintainer validation." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Giskard OSS

Use this repo skill for Giskard OSS v3, a split Python package set for evaluating and red-teaming agentic systems. It covers giskard.checks, giskard.llm, giskard.agents, giskard.scan, and giskard.core.

Start Here

• Package overview explains package names, install extras, import namespaces, and version boundaries.
• Cross-cutting troubleshooting covers install, import, telemetry, optional extras, provider credentials, and scan triage.
• Development and testing notes are only for tasks already working inside a Giskard OSS source checkout.
• Repository provenance records the source snapshot used to generate this skill and when to refresh it.
• Repo routing metadata is structured import metadata for repo-skills-router publication checks.
• scripts/check_giskard_imports.py checks an installed environment without provider calls or network access.

Minimal Runtime Facts

• Requires Python >=3.12.
• Use namespace imports such as import giskard.checks; do not use giskard_checks or other underscore package names.
• The root giskard distribution is a v3 meta-distribution; the operational APIs live under split packages.
• Set DO_NOT_TRACK=1 or GISKARD_TELEMETRY_DISABLED=1 before the first import when the user requests telemetry opt-out.
• No console entry points are declared by the package metadata; primary usage is through Python APIs.

Route by Task

User task	Read
Install Giskard, choose extras, inspect versions, disable telemetry, diagnose import conflicts, or use giskard.core utilities	runtime-setup
Build evaluation scenarios/suites, choose deterministic checks or LLM judges, generate inputs, write custom checks, or export JUnit	checks-evals
Configure OpenAI/Google/Anthropic/Azure provider aliases, call completions/embeddings/responses, inspect typed messages/tools, or handle provider errors	llm-providers
Build async chat workflows with Generator, ChatWorkflow, tools, prompt templates, structured output, retries, rate limiting, or LiteLLM	agents-workflows
Plan or run vulnerability scans, RAG quality scans, scan suite generation, KnowledgeBase, prompt-injection generators, or garak/deepteam/lidar integrations	scan-redteam

Safe Checks Before Deeper Work

Run installed-package diagnostics first when the environment is uncertain:

python scripts/check_giskard_imports.py
python scripts/check_giskard_imports.py --require-scan

Then use sub-skill scripts for focused no-key checks:

python sub-skills/checks-evals/scripts/run_checks_smoke.py
python sub-skills/llm-providers/scripts/inspect_llm_routing.py
python sub-skills/agents-workflows/scripts/run_agents_smoke.py
python sub-skills/scan-redteam/scripts/inspect_scan_api.py

These helpers are deterministic and do not call live providers, remote datasets, third-party scanners, or source-checkout tests.

Optional Dependencies and External Inputs

Install only the extras the task needs. Provider-backed LLM judges, generators, agents, and scans require matching SDK extras plus credentials. Third-party scan integrations may require garak, deepteam, private packages, network access, or additional API keys. Do not treat a successful import smoke as proof that a live provider or scanner workflow is ready.

Refresh Rule

Before relying on this skill for a changed checkout, compare the current commit, working-tree state, and package versions against repo-provenance.md. If they differ materially, run the repo-skill refresh workflow instead of patching runtime guidance ad hoc.