Marketplace

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

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.

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

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

Establish versioned prompt management: semantic versioning, A/B testing, rollback, and changelog practices for production prompt systems.

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.

Use when building or auditing an LLM-as-judge: calibrate it against human labels, measure agreement, and de-bias position and verbosity effects.

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.

Optimize how context is assembled for LLM calls: system prompt design, few-shot example selection, context pruning, and token budget management.

Design effective chatbot conversations: intent mapping, escalation flows, personality design, and fallback strategies.

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.

Build automated test suites that exercise agent behavior across edge cases: adversarial inputs, tool failures, multi-turn conversations, and recovery.

Design secure execution environments for AI agents: Docker sandboxes, API key isolation, network policies, and resource limits.

Craft detailed agent personas with expertise areas, communication styles, decision frameworks, and behavioral boundaries.

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.