Agent Creation

/============================================================================/ / AGENT-CREATION SKILL :: VERILINGUA x VERIX EDITION / /============================================================================/

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name: agent-creation version: 1.0.0 description: | [assert|neutral] Systematic agent creation using evidence-based prompting principles and 4-phase SOP methodology. Use when creating new specialist agents, refining existing agent prompts, or designing multi-agent syst [ground:given] [conf:0.95] [state:confirmed] category: foundry tags:

• foundry
• creation
• meta-tools

author: ruv cognitiveframe: primary: compositional goalanalysis: firstorder: "Execute agent-creation workflow" secondorder: "Ensure quality and consistency" third_order: "Enable systematic foundry processes" ---

/----------------------------------------------------------------------------/ / S0 META-IDENTITY / /----------------------------------------------------------------------------/

[define|neutral] SKILL := { name: "agent-creation", category: "foundry", version: "1.0.0", layer: L1 } [ground:given] [conf:1.0] [state:confirmed]

/----------------------------------------------------------------------------/ / S1 COGNITIVE FRAME / /----------------------------------------------------------------------------/

[define|neutral] COGNITIVE_FRAME := { frame: "Compositional", source: "German", force: "Build from primitives?" } [ground:cognitive-science] [conf:0.92] [state:confirmed]

Kanitsal Cerceve (Evidential Frame Activation)

Kaynak dogrulama modu etkin.

/----------------------------------------------------------------------------/ / S2 TRIGGER CONDITIONS / /----------------------------------------------------------------------------/

[define|neutral] TRIGGER_POSITIVE := { keywords: ["agent-creation", "foundry", "workflow"], context: "user needs agent-creation capability" } [ground:given] [conf:1.0] [state:confirmed]

/----------------------------------------------------------------------------/ / S3 CORE CONTENT / /----------------------------------------------------------------------------/

Skill Execution Criteria

When to Use This Skill

• Creating new specialist agents with domain-specific expertise
• Refining existing agent system prompts for better performance
• Designing multi-agent coordination systems
• Implementing role-based agent hierarchies
• Building production-ready agents with embedded domain knowledge

When NOT to Use This Skill

• For simple one-off tasks that don't need agent specialization
• When existing agents already cover the required domain
• For casual conversational interactions without systematic requirements
• When the task is better suited for a slash command or micro-skill

Success Criteria

• [assert|neutral] primary_outcome: "Production-ready agent with optimized system prompt, clear role definition, and validated performance" [ground:acceptance-criteria] [conf:0.90] [state:provisional]
• [assert|neutral] quality_threshold: 0.9 [ground:acceptance-criteria] [conf:0.90] [state:provisional]
• [assert|neutral] verification_method: "Agent successfully completes domain-specific tasks with consistent high-quality output, passes validation tests, and integrates with Claude Agent SDK" [ground:acceptance-criteria] [conf:0.90] [state:provisional]

Edge Cases

• case: "Vague agent requirements"

handling: "Use Phase 1 (Initial Analysis) to research domain, identify patterns, and clarify scope before proceeding"

• case: "Overlapping agent capabilities"

handling: "Conduct agent registry search, identify gaps vs duplicates, propose consolidation or specialization"

• case: "Agent needs multiple conflicting personas"

handling: "Decompose into multiple focused agents with clear coordination pattern"

Skill Guardrails

NEVER:

• "Create agents without deep domain research (skipping Phase 1 undermines quality)"
• "Use generic prompts without evidence-based techniques (CoT, few-shot, role-based)"
• "Skip validation testing (Phase 3) before considering agent production-ready"
• "Create agents that duplicate existing registry agents without justification"

ALWAYS:

• "Complete all 4 phases: Analysis -> Prompt Engineering -> Testing -> Integration"
• "Apply evidence-based prompting: Chain-of-Thought for reasoning, few-shot for patterns, clear role definition"
• "Validate with diverse test cases and measure against quality criteria"
• "Document agent capabilities, limitations, and integration points"

Evidence-Based Execution

selfconsistency: "After agent creation, test with same task multiple times to verify consistent outputs and reasoning quality" programofthought: "Decompose agent creation into: 1) Domain analysis, 2) Capability mapping, 3) Prompt architecture, 4) Test design, 5) Validation, 6) Integration" planand_solve: "Plan: Research domain + identify capabilities -> Execute: Build prompts + test cases -> Verify: Multi-run consistency + edge case handling"

Agent Creation - Systematic Agent Design

Kanitsal Cerceve (Evidential Frame Activation)

Kaynak dogrulama modu etkin.

Evidence-based agent creation following best practices for prompt engineering and agent specialization.

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When to Use This Skill

Use when creating new specialist agents for specific domains, refining existing agent capabilities, designing multi-agent coordination systems, or implementing role-based agent hierarchies.

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4-Phase Agent Creation SOP

Phase 1: Specification

• Define agent purpose and domain
• Identify core capabilities needed
• Determine input/output formats
• Specify quality criteria

Tools: Use resources/scripts/generate_agent.sh for automated generation

Phase 2: Prompt Engineering

• Apply evidence-based prompting principles
• Use Chain-of-Thought for reasoning tasks
• Implement few-shot learning with examples (2-5 examples)
• Define role and persona clearly

Reference: See references/prompting-principles.md for detailed techniques

Phase 3: Testing & Vali

/----------------------------------------------------------------------------/ / S4 SUCCESS CRITERIA / /----------------------------------------------------------------------------/

[define|neutral] SUCCESS_CRITERIA := { primary: "Skill execution completes successfully", quality: "Output meets quality thresholds", verification: "Results validated against requirements" } [ground:given] [conf:1.0] [state:confirmed]

/----------------------------------------------------------------------------/ / S5 MCP INTEGRATION / /----------------------------------------------------------------------------/

[define|neutral] MCPINTEGRATION := { memorymcp: "Store execution results and patterns", tools: ["mcp__memory-mcp__memorystore", "mcp_memory-mcp__vector_search"] } [ground:witnessed:mcp-config] [conf:0.95] [state:confirmed]

/----------------------------------------------------------------------------/ / S6 MEMORY NAMESPACE / /----------------------------------------------------------------------------/

[define|neutral] MEMORYNAMESPACE := { pattern: "skills/foundry/agent-creation/{project}/{timestamp}", store: ["executions", "decisions", "patterns"], retrieve: ["similartasks", "proven_patterns"] } [ground:system-policy] [conf:1.0] [state:confirmed]

[define|neutral] MEMORYTAGGING := { WHO: "agent-creation-{sessionid}", WHEN: "ISO8601timestamp", PROJECT: "{projectname}", WHY: "skill-execution" } [ground:system-policy] [conf:1.0] [state:confirmed]

/----------------------------------------------------------------------------/ / S7 SKILL COMPLETION VERIFICATION / /----------------------------------------------------------------------------/

[direct|emphatic] COMPLETIONCHECKLIST := { agentspawning: "Spawn agents via Task()", registryvalidation: "Use registry agents only", todowritecalled: "Track progress with TodoWrite", work_delegation: "Delegate to specialized agents" } [ground:system-policy] [conf:1.0] [state:confirmed]

/----------------------------------------------------------------------------/ / S8 ABSOLUTE RULES / /----------------------------------------------------------------------------/

[direct|emphatic] RULENOUNICODE := forall(output): NOT(unicodeoutsideascii) [ground:windows-compatibility] [conf:1.0] [state:confirmed]

[direct|emphatic] RULE_EVIDENCE := forall(claim): has(ground) AND has(confidence) [ground:verix-spec] [conf:1.0] [state:confirmed]

[direct|emphatic] RULEREGISTRY := forall(agent): agent IN AGENTREGISTRY [ground:system-policy] [conf:1.0] [state:confirmed]

/----------------------------------------------------------------------------/ / PROMISE / /----------------------------------------------------------------------------/

[commit|confident] AGENTCREATIONVERILINGUAVERIXCOMPLIANT [ground:self-validation] [conf:0.99] [state:confirmed]

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: DNYoussef
• Source: DNYoussef/context-cascade
• License: MIT

Install and usage instructions live in the source repository linked above.