Marketplace

Design voice assistant experiences: intent design, dialogue management, error recovery, and multi-modal handoff.

Use when creating, improving, or reviewing an agent skill: scope one job, write routing-grade descriptions, and verify with fresh-agent tests.

Use when a prompt underperforms and needs systematic diagnosis, single-variable experiments, and A/B evidence instead of guess-and-check rewrites.

Use when asked to build or review an MCP server wrapping an API, database, or CLI so agents can call it through well-designed, strictly typed tools.

Design Model Context Protocol resources and prompts that expose data and workflows to AI agents following the MCP specification.

Build knowledge graphs from unstructured data for agent grounding: entity extraction, relation mapping, and graph-based retrieval.

Drive headless browsers (Playwright/Puppeteer) to navigate pages, auto-screenshot at each step, fill forms, click elements, and capture visual evidence of workflows.

Design AI writing assistant features: tone control, suggestion timing, feedback loops, and human-in-the-loop editing.

Design AI-powered search experiences: semantic search, conversational interfaces, result ranking, and relevance tuning.

Design AI content moderation pipelines: multi-layer classification, appeal workflows, edge case handling, and trust safety.

Design AI code review systems: PR analysis, finding prioritization, false positive suppression, and developer feedback.

Design complex agentic workflows: DAG-based task graphs, conditional branching, human-in-the-loop checkpoints, and error recovery paths.

Design and implement multi-agent workflows with role assignment, task decomposition, and inter-agent communication protocols.

Design first-run experiences and onboarding flows for AI agents: capability discovery, permission grants, and initial task routing.

Implement tracing, logging, and monitoring for AI agent systems: LLM call traces, tool execution logs, latency tracking, and replay debugging.

Design persistent memory systems for AI agents: episodic, semantic, and procedural memory with retrieval strategies.

Design seamless handoff protocols between specialized agents: context transfer, state serialization, and recovery on failure.

Design self-improving agent systems: outcome tracking, reward signal extraction, prompt refinement cycles, and human preference collection.

Use when an agent workflow needs measurable quality: build task-based evals with graders, baselines, and A/B comparisons before shipping changes.

Design and run comprehensive eval suites for AI agents: task completion rates, tool call accuracy, cost tracking, and regression testing.

Deploy and run a SearXNG meta-search instance for AI agents to query the live web: Docker setup, engine configuration, API integration, and result parsing.

Validate and test tool/function calling schemas: JSON Schema compliance, edge case generation, parameter boundary testing, and mock servers.

Create production-grade tools/functions that AI agents can call: define schemas, implement handlers, add validation and error handling.

Build retrieval-augmented generation pipelines: chunking strategies, embedding models, vector stores, and reranking for accurate grounding.