Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add yonatangross/orchestkit --skill testing-llmgit clone --depth 1 https://github.com/yonatangross/orchestkitWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/yonatangross/orchestkit/testing-llm)<a href="https://agentmods.dev/skills/yonatangross/orchestkit/testing-llm"><img src="https://agentmods.dev/badge/skills/yonatangross/orchestkit/testing-llm/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/yonatangross/orchestkit/testing-llm"><img src="https://agentmods.dev/badge/skills/yonatangross/orchestkit/testing-llm.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium MCP Rug Pull · line 209 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00055 | $0.02527 |
| Opus 5.5 | $0.00022 | $0.01011 |
| Sonnet 5.5 | $0.00011 | $0.00505 |
| Haiku 4.5 | $0.00006 | $0.00253 |
Grade A, and why
testing-llm scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 29d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 245 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LLM & AI Testing Patterns
Patterns and tools for testing LLM integrations, evaluating AI output quality, mocking responses for deterministic CI, and applying agentic test workflows (planner, generator, healer). Of that trio only the healer keeps a local reference here; the planner and generator stages belong to the testing-e2e skill.
Quick Reference
| Area | File | Purpose |
|---|---|---|
| Rules | rules/llm-evaluation.md |
DeepEval quality metrics, Pydantic schema validation, timeout testing |
| Rules | rules/llm-mocking.md |
Mock LLM responses, VCR.py recording, custom request matchers |
| Reference | references/ork-delta.md |
House rules the vendor docs do not carry: GEval and RAGAS API corrections, threshold direction, cassette path, golden-dataset and latency budgets |
| Reference | references/healer-agent.md |
Auto-fixes failing tests (selectors, waits, dynamic content) |
| Reference | references/langfuse-v4.md |
Langfuse Python SDK v4 tracing and dataset runs, plus the non-zero throughput assertion |
| Checklist | checklists/llm-test-checklist.md |
Complete LLM testing checklist (setup, coverage, CI/CD) |
Upstream coverage (do not restate)
DeepEval, RAGAS, VCR.py and Playwright document themselves. This skill carries only the
OrchestKit delta (references/ork-delta.md) plus the house subsets in rules/ and
checklists/. Fetch the source below instead of expecting the material here.
| Topic | Source |
|---|---|
Full DeepEval metric catalog and per-metric constructor arguments (the house threshold table and the two-metric quick start stay in this file, rules/llm-evaluation.md and checklists/llm-test-checklist.md) |
https://deepeval.com/docs/metrics-introduction |
GEval custom criteria: evaluation_params, evaluation_steps, criteria (the house import correction stays in references/ork-delta.md) |
https://deepeval.com/docs/metrics-llm-evals |
HallucinationMetric arguments (the house 0.3 ceiling and the inverted-direction warning stay in references/ork-delta.md) |
https://deepeval.com/docs/metrics-hallucination |
RAGAS metric catalog (Faithfulness, LLMContextRecall, FactualCorrectness) |
https://docs.ragas.io/en/stable/concepts/metrics/available_metrics/ |
EvaluationDataset construction (the house note on the post-0.2 field names stays in references/ork-delta.md) |
https://docs.ragas.io/en/stable/concepts/components/eval_dataset/ |
VCR.py configuration keys (the house record-mode gate and header filters stay in rules/llm-mocking.md) |
https://vcrpy.readthedocs.io/en/latest/configuration.html |
Playwright Planner and Generator agents, init-agents CLI and generated files (the house healer subset stays in references/healer-agent.md) |
https://playwright.dev/docs/test-agents |
| Playwright semantic locator ladder used by generated tests | testing-e2e skill (rules/e2e-playwright.md) plus https://playwright.dev/docs/locators |
| Confidence intervals over metric score samples | https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.t.html |
What ships with it
8 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 29d ago Changed 613bbba1c6f3
- 1mo ago Changed · +1 lines 8edd3cd805a9
- 1mo ago First seen · 244 lines · 55 tokens per session scan A 001e87ede389
testing-llm is a skill published in the GitHub repository yonatangross/orchestkit (297 stars, last pushed yesterday), licensed MIT. It adds 55 tokens to every session and 2,527 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-05.
Other skills, from other repositories
deepeval
Use when discussing or working with DeepEval (the python AI evaluation framework).
ai-prompt-regression-testing
Use when comparing prompt changes against a fixed evaluation set: record actual model settings, required checks, missing results, judge limitations and case-level regression evidence.
generate-rag-dataset
Generate a synthetic evaluation dataset from your RAG knowledge base. Creates diverse Q&A pairs with expected answers and relevant context, ready for LangWatch experiments and platform import. Use when you need test data for your RAG pipeline.
trulens-evaluation-workflow
Systematically evaluate your LLM application with TruLens.
trulens-running-evaluations
Execute TruLens evaluations and view results.
test-writer
How to write pytest tests for modules in this workspace. Load whenever you are about to write or extend tests.