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.

Invoke when a downstream system must consume LLM output as JSON matching a fixed schema and malformed or missing fields would break the pipeline.

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

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.

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.

Invoke when an autonomous task may outlast one context window—persist resumable checkpoints to a state file so work survives resets and interruptions.

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.

Design and implement safety guardrails for AI agents: input/output filtering, topic restrictions, PII redaction, and jailbreak resistance.

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.