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

---

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
  cognitive_frame:
  primary: compositional
  goal_analysis:
  first_order: "Execute agent-creation workflow"
  second_order: "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

self_consistency: "After agent creation, test with same task multiple times to verify consistent outputs and reasoning quality"
program_of_thought: "Decompose agent creation into: 1) Domain analysis, 2) Capability mapping, 3) Prompt architecture, 4) Test design, 5) Validation, 6) Integration"
plan_and_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.

---

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.

---

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] MCP_INTEGRATION := {
memory_mcp: "Store execution results and patterns",
tools: ["mcp__memory-mcp__memory_store", "mcp__memory-mcp__vector_search"]
} [ground:witnessed:mcp-config] [conf:0.95] [state:confirmed]

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

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

[define|neutral] MEMORY_TAGGING := {
WHO: "agent-creation-{session_id}",
WHEN: "ISO8601_timestamp",
PROJECT: "{project_name}",
WHY: "skill-execution"
} [ground:system-policy] [conf:1.0] [state:confirmed]

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

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

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

[direct|emphatic] RULE_NO_UNICODE := forall(output): NOT(unicode_outside_ascii) [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] RULE_REGISTRY := forall(agent): agent IN AGENT_REGISTRY [ground:system-policy] [conf:1.0] [state:confirmed]

/----------------------------------------------------------------------------/
/* PROMISE /
/----------------------------------------------------------------------------*/

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