feat(skills): add promptfoo skill for LLM evaluation and red-teaming

Covers install, configuration, running evals, red-teaming, CI/CD
integration, and dataset generation. Pins to v0.121.17 with acquisition
notice (OpenAI, March 2026) and documented fallbacks (DeepEval, Arize Phoenix).

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

plugins/kyberforge/skills/promptfoo/references/overview.md

@@ -0,0 +1,40 @@
+---
+topic: overview
+source_keys:
+  - context7-promptfoo-dev
+  - context7-promptfoo-github
+---
+
+## What is Promptfoo
+
+Promptfoo is an open-source, local-first CLI and library for evaluating and red-teaming LLM applications. It enables systematic, repeatable testing of prompts across multiple providers with assertions that grade outputs automatically. Results are stored locally and can be visualised in a browser UI or exported.
+
+It is not a hosted service — all evaluation state stays on your machine unless you explicitly share a result.
+
+## Core concepts
+
+**Prompt** — a template with `{{variable}}` placeholders. Can be a plain string, a JSON chat array (`file://prompts/chat.json`), or a JavaScript function (`file://prompts/generate.js`) that returns a string or message array dynamically.
+
+**Provider** — an LLM endpoint to send the rendered prompt to. Providers are declared as strings (`openai:gpt-5-mini`, `anthropic:messages:claude-sonnet-4-5`) or objects with a `config:` block for additional parameters.
+
+**Test case** — one input scenario. Contains `vars:` (values substituted into prompt variables) and `assert:` (a list of assertions that the response must satisfy).
+
+**Assertion** — a pass/fail check on the model output. Ranges from deterministic (`contains`, `regex`, `equals`) to model-graded (`llm-rubric`, `factuality`).
+
+**Eval** — one complete run: every prompt × every provider × every test case is executed and each assertion is scored. Results are a table of pass/fail cells with per-assertion metrics.
+
+**defaultTest** — a top-level config key whose `assert:` and `options:` are merged into every test case, avoiding repetition.
+
+## Mental model
+
+Think of an eval as a spreadsheet where rows are test cases and columns are (prompt, provider) pairs. Each cell contains the model output and assertion results. Running `promptfoo eval` fills the spreadsheet; `promptfoo view` opens it in a browser.
+
+The config file (`promptfooconfig.yaml`) is the source of truth for a given eval. It is committed alongside your prompt files so evals are reproducible.
+
+## What it is used for
+
+- **Regression testing** — catch prompt regressions before deploying changes
+- **Side-by-side model comparison** — evaluate multiple providers on identical test suites
+- **Red-teaming** — generate and run adversarial tests (jailbreaks, prompt injection, harmful content)
+- **Dataset generation** — AI-generate test cases from a prompt template
+- **CI/CD gating** — fail a pull request when assertion pass rate drops