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/examples.md

@@ -0,0 +1,150 @@
+---
+topic: examples
+source_keys:
+  - context7-promptfoo-dev
+  - context7-promptfoo-github
+---
+
+## Quickstart
+
+```bash
+npx promptfoo@0.121.17 init
+# edit promptfooconfig.yaml
+npx promptfoo@0.121.17 eval
+npx promptfoo@0.121.17 view
+```
+
+## Minimal config
+
+```yaml
+# yaml-language-server: $schema=https://promptfoo.dev/config-schema.json
+prompts:
+  - 'Answer the user question concisely. Question: {{question}}'
+
+providers:
+  - openai:gpt-5-mini
+
+tests:
+  - vars:
+      question: How do I reset my password?
+    assert:
+      - type: contains
+        value: reset
+  - vars:
+      question: Can I cancel my subscription today?
+    assert:
+      - type: llm-rubric
+        value: The answer clearly explains the cancellation path.
+```
+
+## Multi-provider comparison
+
+```yaml
+providers:
+  - openai:gpt-5-mini
+  - anthropic:claude-3-haiku
+
+prompts:
+  - 'You are a helpful customer service agent. {{query}}'
+
+tests:
+  - vars:
+      query: 'I need to return a product'
+    assert:
+      - type: contains
+        value: 'return policy'
+      - type: llm-rubric
+        value: 'Response is helpful and professional'
+```
+
+Running this produces a side-by-side table with both models' outputs and assertion scores.
+
+## Loading tests from CSV
+
+```yaml
+tests:
+  - file://test_scenarios.csv
+```
+
+CSV format: one column per variable, header row must match `{{variable}}` names in the prompt. An `__expected` column maps to the `equals` assertion automatically.
+
+## Factuality evaluation
+
+```yaml
+providers:
+  - openai:gpt-5-mini
+prompts:
+  - |
+    Please answer the following question accurately:
+    Question: What is the capital of {{location}}?
+tests:
+  - vars:
+      location: California
+    assert:
+      - type: factuality
+        value: The capital of California is Sacramento
+```
+
+## defaultTest for shared assertions
+
+```yaml
+defaultTest:
+  assert:
+    - type: llm-rubric
+      value: |
+        Evaluate whether the response correctly answers the question.
+        Question: {{ question }}
+        Model Response: {{ output }}
+        Correct Answer: {{ answer }}
+        Grade accuracy 0.0–1.0. Pass if >= 0.8.
+      threshold: 0.8
+
+tests:
+  - vars:
+      question: What year did WW2 end?
+      answer: '1945'
+  - vars:
+      question: What is the boiling point of water in Celsius?
+      answer: '100'
+```
+
+## Node.js API
+
+```javascript
+import { evaluate } from 'promptfoo';
+
+const evalRecord = await evaluate({
+  prompts: ['Translate to Spanish: {{ text }}'],
+  providers: ['openai:chat:gpt-5.5'],
+  tests: [
+    {
+      vars: { text: 'Hello' },
+      assert: [{ type: 'contains', value: 'Hola', metric: 'translation' }],
+    },
+  ],
+});
+
+const results = await evalRecord.toEvaluateSummary();
+console.log(`Pass rate: ${results.stats.successes}/${results.results.length}`);
+```
+
+## Generating test datasets with AI
+
+```bash
+# Generate test cases based on your prompt template
+promptfoo generate dataset
+promptfoo generate dataset --instructions "Consider edge cases related to international travel"
+promptfoo generate dataset --output generated_tests.yaml
+```
+
+## Saving and sharing results
+
+```yaml
+outputPath: evaluations/results.html
+```
+
+Or via CLI:
+```bash
+promptfoo eval -o results.json
+promptfoo share  # get a shareable URL
+```