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From agent-artifex
Use when the user is unsure which AI services skill to invoke, asks for "AI services guidance" generally, says "help me design my MCP server", "how should I structure my tools?", "what makes a good tool description?", "how do I design my agent?", "help me test my MCP server", "how should I test my agent?", "what testing do I need?", "my tests are flaky", "the agent picks the wrong tool", "how do I set up CI for AI tests?", or wants a structured guided experience that routes across multiple AI services skills. Also use when the user mentions designing or testing chatbots, tool descriptions, evals, or agent behavior without specifying a particular skill.
How this skill is triggered — by the user, by Claude, or both
Slash command
/agent-artifex:guideThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Invoke `agent-artifex:guide` when:
Invoke agent-artifex:guide when:
For faster, more direct access, invoke the atomic skills directly (see table below).
On invocation, agent-artifex:guide asks exactly one question:
"Are you learning about designing and testing AI services, assessing gaps in an existing project, designing a new system, or ready to implement improvements?"
User's answer determines the routing path below. Some answers don't fit neatly into one path — use the intent table at the bottom for common edge cases.
User says: "I'm new to this," "explain the framework," "teach me about AI services," "what is faithfulness?"
agent-artifex:foundations → comprehensive reference overview
agent-artifex:learn → interactive Socratic dialogue
Start with foundations if the user wants to read, learn if they want to be guided through it interactively.
User says: "What testing do we need?", "What are our gaps?", "We have some tests but I'm not sure they're enough," "We keep getting bad responses."
agent-artifex:assess
→ [identifies design and testing gaps across the 5 areas]
→ prioritized recommendations
→ agent-artifex:implement (for areas that need improvement)
User says: "Help me design my MCP server," "How should I structure my tools?", "What makes a good tool description?", "I need to plan my agent architecture."
agent-artifex:design
→ [applies design principles to the user's problem]
→ tool description quality, server structure, agent architecture
→ agent-artifex:implement (when ready to build)
User says: "Help me improve my server," "I need to test my MCP server," "Write tool description quality checks," "Add evals for response accuracy," "Add tests."
agent-artifex:implement
→ [determines which area(s) apply]
→ reads implementation references for code patterns
→ guides implementation with rubrics, formulas, and examples
User describes a specific problem rather than a goal.
Route based on symptom:
| Symptom | Route to | Why |
|---|---|---|
| "The FM picks the wrong tool" | assess then implement (Description Quality + Agent Behavior) | Likely a description quality problem — assess first, then fix |
| "Answers are wrong but the right tool was called" | implement (Response Accuracy) | FM synthesis problem — go straight to implementation |
| "Tests are flaky / too expensive" | assess | Probably LLM tests in CI — assess pyramid placement |
| "Quality degrades in long conversations" | implement (Chatbot Integration) | Context pressure or coreference — implement specific tests |
| "Works with one model but not another" | implement (Agent Behavior, multi-model stability) | Description sensitivity — implement cross-model scenarios |
| "Server errors confuse the FM" | implement (Server Correctness, error structure) | Error actionability — implement error path tests |
| "My tool descriptions are a mess" | design (Description Quality) | Needs design principles before implementation |
| "I have too many tools" | design (Server Structure) | Needs architectural guidance on tool decomposition |
| Skill | Archetype | When to use directly |
|---|---|---|
agent-artifex:foundations | Reference | "What is the framework?" "Give me the big picture." |
agent-artifex:learn | Socratic | "Teach me about designing and testing AI systems." "Walk me through an example." |
agent-artifex:design | Principles | "Help me design my MCP server." "What makes a good tool description?" |
agent-artifex:assess | Assessment | "What design and testing gaps do we have?" "Audit our coverage." |
agent-artifex:implement | Operational | "Help me improve my server." "Add tests." |
Tool Description Quality → Agent Behavior → Server Correctness → Response Accuracy
(Discovery) (Tool Selection) (Invocation) (full loop)
leading leading leading OUTCOME
↑
Chatbot Integration
(multi-turn layer)
Response Accuracy is the only measure the user experiences. The other four are leading indicators that diagnose why response accuracy is high or low. When something goes wrong, trace backward through the chain.
After routing, the guide hands off to the selected skill. If the user's needs span multiple skills, chain them: assess first to identify gaps, design to plan improvements, then implement to build them.
npx claudepluginhub flexion/claude-domestique --plugin agent-artifexGuides designing AI agent tools with principles for workflows, context optimization, naming, input/output schemas, error handling, and evaluation.
Designs effective tools for AI agents, covering descriptions, namespacing, complexity reduction, and contracts. Use when creating, optimizing, debugging, or evaluating agent toolsets.
Provides a checklist for code reviews covering functionality, security, performance, maintainability, tests, and quality. Use for pull requests, audits, team standards, and developer training.