Results for “generator-evaluator”

GAN-inspired Generator-Evaluator agent harness for building high-quality applications autonomously. Based on Anthropic's March 2026 harness design paper.

Builds and validates DeepStream GStreamer pipelines through an interactive questionnaire and a BM25 retrieval engine over 270+ verified pipelines.

Generate go test generator operations. Auto-activating skill for Test Automation. Triggers on: go test generator, go test generator Part of the Test Automation skill category. Use when writing or running tests. Trigger with phrases like "go test generator", "go generator", "go".

Generate Playwright tests. Use when user says "write tests", "generate tests", "add tests for", "test this component", "e2e test", "create test for", "test this page", or "test this feature".

Expert technology assessment specialist focused on evaluating, testing, and recommending tools, software, and platforms for business use and productivity optimization

Assesses new tools, technologies, and integration options for adoption, comparing vendors and recommending implementations.

Grades and classifies evaluation batch results, applying exclusions, diagnosing failure modes, computing pass rates, and generating summary tables for papers.

Evaluates execution transcripts and output files against a list of expectations, assigning pass/fail verdicts with cited evidence and critiquing the assertions themselves.

Generates detailed, technology-agnostic implementation blueprints by analyzing project architecture, technology stacks, and data flow patterns.

Generates and writes unit tests for any programming language using a multi-agent pipeline that researches, plans, and implements tests with build and verification steps.

Generates structured abstracts for research papers with background, methods, results, and conclusions

Build quizzes with question types and scoring. TRIGGERS - Use when user needs help with quiz-builder related tasks.

Generate and rank theory-first or proof-oriented research ideas. Use when the user wants non-experimental research ideas, theoretical methods, proof programs, theorem candidates, impossibility results, convergence/sample-complexity analyses, or "analysis" variants of idea creation.

Build quizzes with question types and scoring. TRIGGERS - Use when user needs help with quiz-builder related tasks.

Generate and rank research ideas given a broad direction. Use when user says "找idea", "brainstorm ideas", "generate research ideas", "what can we work on", or wants to explore a research area for publishable directions.

Evaluate a raw app, startup, SaaS, internal tool, marketplace, or AI product idea before planning features. Use when the user shares a vague concept and needs pain analysis, market realism, feasibility, differentiation, risks, or a build/no-build recommendation.

Generate invoice generator operations. Auto-activating skill for Business Automation. Triggers on: invoice generator, invoice generator Part of the Business Automation skill category. Use when working with invoice generator functionality. Trigger with phrases like "invoice generator", "invoice generator", "invoice".

Generate fixture generator operations. Auto-activating skill for Test Automation. Triggers on: fixture generator, fixture generator Part of the Test Automation skill category. Use when working with fixture generator functionality. Trigger with phrases like "fixture generator", "fixture generator", "fixture".

Giskard RAGET (RAG Evaluation Toolkit): automatic testset generation (simple / complex / distracting / conversational), component-level scoring (retriever / generator / rewriter), hallucination and bias tests, CI integration. Compared to RAGAS and DeepEval. USE WHEN: user mentions "Giskard", "RAGET", "Giskard RAG toolkit", "automatic testset generation", "component-level RAG scoring", "hallucination test Giskard" DO NOT USE FOR: general RAGAS usage - use `rag-evaluation`; Stanford ARES - use `ares-framework`; CI/CD wiring - use `continuous-evaluation`

Generates comprehension questions and language exercises based on analyzed content to create a complete learning loop.

Simulates meiotic recombination to produce offspring genomes from parent pairs, modeling Mendelian segregation, de novo mutation, sex determination, trait inference, and clinical evaluation against a disease registry.

Runs regression evaluations comparing agent edits against human-approved golden projects, and registers new goldens after human approval.

Generate a structured specification document before writing implementation code, validating scope, drafting specs, and registering them in the project.

Analyzes type design for encapsulation, invariant expression, usefulness, and enforcement, providing scores and improvement suggestions.

Validates agent outputs against schemas and quality criteria, scores confidence, detects hallucinations, monitors convergence, decides when to iterate, and synthesizes actionable feedback. Use when checking if a node's output is acceptable, scoring confidence, detecting fabricated content, deciding whether to re-execute, or generating improvement feedback. Activate on "validate output", "check quality", "confidence score", "hallucination check", "should we iterate", "improvement feedback". NOT for executing DAGs (use dag-runtime), planning DAGs (use dag-planner), or matching skills (use dag-skills-matcher).

ticket-writer

Generates multiple app variations in parallel, each a distinct interpretation of a theme, then ranks them and presents a gallery.

Changelog Generator

Changelog Generator

Design reproducible evaluations for AI agents with representative task sets, explicit rubrics, appropriate graders, baselines, regression gates, and failure analysis. Use when defining agent quality, comparing prompts or models, validating a release, measuring tool-use reliability, investigating regressions, or deciding whether an agent is ready for production.

Generate, edit, and read PowerPoint presentations using PptxGenJS for creation, XML workflows for editing, and markitdown for text extraction.

Design and implement evaluation harnesses for models, agents, and code. Use when creating benchmarks, designing eval metrics, or comparing system outputs.

Revisa criticamente as especificações geradas pelo reversa-writer — encontra inconsistências, reclassifica confiança e gera perguntas para validação humana. Use na fase de revisão de uma análise de engenharia reversa.

Guides users through creating, refining, and evaluating agent skills, including drafting, testing, and optimizing descriptions for better triggering.

Generates editable diagram source code (Mermaid, Graphviz DOT, PlantUML, SVG) from natural language, code, schemas, or notes for flowcharts, sequence diagrams, ER diagrams, architecture diagrams, and more. Renders to PNG/SVG/PDF on request via a local Python script.