Building AI agents means dealing with a new category of engineering problems: non-deterministic execution, tool use reliability, multi-step planning, and failure modes that don't show up in unit tests. These skills cover agent architecture, workflow design, tool schema definition, and the practical patterns for building agents that can actually complete tasks reliably instead of spinning in loops.
Key takeaway
ClaudeVault's agent systems skills give Claude Code structured workflows for the problems that actually break agents in production — tool schema design, Claude Code skill and hook authoring, MCP server construction, and agent workflow topology. Research shows roughly 60% of production agent failures trace back to workflow design rather than model quality, and these skills target that specific failure mode head-on.
When an agent works for the first three steps and then loops, stalls, or hallucinates a nonexistent tool
When a Claude Code hook fires on the wrong event and the team cannot tell whether it is a config bug or a schema bug
When a new MCP server needs to expose a real service and the team is choosing between Python and TypeScript SDKs
When a shared skill library has grown past 20 skills and discoverability is breaking down across the team
A full Claude Code agent build pass layers these five skills from high-level workflow topology down to the specific tool contracts, so every decision is anchored to the agent's actual execution shape.
Start with the agent workflow designer. Claude picks between sequential, parallel, router, and orchestrator-worker patterns based on the task shape. The skill forces a written trade-off — why ReAct instead of plan-and-execute, why router instead of a single loop — so the topology is intentional, not a tutorial default.
Hand the tool surface to the tool schema designer. Claude writes unique tool names, unambiguous descriptions, and tight JSON schema parameters, then flags any turn where active tools would exceed the 20-tool accuracy threshold. Tool sprawl is the single most common cause of agent confusion in production.
The MCP builder generates a Model Context Protocol server in TypeScript or Python that exposes tools, resources, and prompts following Anthropic's reference patterns. Claude picks the transport — stdio for local, SSE for remote — and sets up the auth and error contract so the server ships as more than a demo.
When the agent needs domain expertise it cannot derive from the base model, the skill builder writes a SKILL.md using the template audited against 189 production skills. Claude compresses the expertise to 80-130 body lines, adds the failure-modes table, and names the triggers Claude will match against at runtime.
Finally, the hook builder adds deterministic shell-script enforcement for anything the agent cannot be trusted to do consistently — blocking dangerous commands, auto-formatting files, running a test suite post-edit. Hooks are the safety layer that catches the 5% of cases where the agent would otherwise do the wrong thing.
Outcome
An agent with an intentional topology, tight tool contracts, a production MCP server, authored domain skills, and deterministic hook enforcement — not a ReAct loop that hallucinates tools at turn seven.
| Skill | Best for | Complexity | Primary use case |
|---|---|---|---|
| Agent Workflow Designer | New agent builds and topology reviews | Advanced | Pattern selection with trade-off analysis |
| Tool Schema Designer | Agents with more than five tools | Advanced | Schema design and tool count discipline |
| MCP Builder | Teams exposing internal services to LLMs | Advanced | Model Context Protocol server implementation |
| Claude Code Skill Builder | Teams authoring SKILL.md files | Intermediate | Audited skill template and compression workflow |
| Claude Code Hook Builder | Agents needing deterministic guarantees | Intermediate | PreToolUse, PostToolUse, and Stop hook patterns |
Creates and refines ClaudeVault SKILL.md files — frontmatter, trigger descriptions, body patterns proven in production, and validator-clearing language. Use when building a new skill, rewriting an underperforming one, or migrating an existing skill to the audited template. Skill builder, SKILL.md, Claude Code skill, Agent Skills standard.
Designs multi-step agentic AI architectures — orchestration patterns (ReAct, router, planner, pipeline, collaborative), tool selection, handoff protocols, safety guardrails, and failure recovery. Use when building AI agents that use tools, make decisions, or coordinate with other agents. Agent architecture, orchestration, multi-agent, agentic workflow.
Designs Claude Code hooks — PreToolUse, PostToolUse, and Stop hook patterns with validation logic, safety guardrails, and workflow enforcement. Use when creating hooks for settings.json to block dangerous commands, auto-lint after edits, inject context, or enforce test verification. Claude Code hooks, settings.json, tool validation.
Designs tool/function calling schemas for LLM agents — parameter types, trigger descriptions, enum usage, error contracts, and disambiguation between tools. Use when defining tool schemas for Claude tool_use, OpenAI function calling, or MCP servers. Tool schema, function calling, tool design, agent tools.
Designs and implements Model Context Protocol (MCP) servers — tool definitions, resource endpoints, input schemas, safety patterns, and client configuration. Use when building an MCP server to expose tools, data, or APIs to Claude Code, Cursor, or other MCP clients. MCP server, Model Context Protocol, tool integration.
MCP is an open standard Anthropic released in November 2024 for connecting LLMs to tools, data, and resources. It matters because it standardizes the tool surface across providers — a single MCP server can be used by Claude, ChatGPT clients, and any MCP-compatible host without rewriting the integration.
Roughly 60% of production agent failures trace back to workflow design rather than model quality — wrong topology, too many active tools, unclear tool descriptions, or missing error contracts. The agent workflow designer and tool schema designer skills target this category directly, because the model upgrade path usually does not help.
Keep the active tool count under 20 per turn. Both Anthropic and OpenAI document this as the accuracy threshold above which the model starts confusing tool parameters. If you need more than 20, split them across router subagents so only the relevant subset is active at any given turn.
Skills are context packs Claude invokes when their trigger phrases match the task — they add expertise. Hooks are deterministic shell scripts the harness runs on specific events — they add enforcement. Slash commands are user-invoked prompts — they are shortcuts. Each layer owns a different class of problem.
ReAct fits adaptive short-horizon tasks where the next step depends on the previous observation — tool-heavy agents and exploratory workflows. Plan-and-Execute fits long-horizon tasks with known structure because the plan itself becomes an audit surface. The agent workflow designer picks based on task predictability.
Use the official MCP TypeScript or Python SDK, define tools with tight JSON schemas, pick stdio transport for local and SSE for remote, and write an error contract that distinguishes retryable failures from hard errors. The MCP builder skill generates this scaffold and walks Claude through the decisions the SDK leaves open.