Agent Creation Skill for Claude

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/* 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

cognitive_frame:

primary: compositional

goal_analysis:

first_order: "Execute agent-creation workflow"

second_order: "Ensure quality and consistency"

third_order: "Enable systematic foundry processes"

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/* S0 META-IDENTITY */

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[define|neutral] SKILL := {

name: "agent-creation",

category: "foundry",

version: "1.0.0",

layer: L1

} [ground:given] [conf:1.0] [state:confirmed]

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/* S1 COGNITIVE FRAME */

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[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.

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/* S2 TRIGGER CONDITIONS */

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[define|neutral] TRIGGER_POSITIVE := {

keywords: ["agent-creation", "foundry", "workflow"],

context: "user needs agent-creation capability"

} [ground:given] [conf:1.0] [state:confirmed]

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/* S3 CORE CONTENT */

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<!-- SKILL SOP IMPROVEMENT v1.0 -->

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"

<!-- END SKILL SOP IMPROVEMENT -->

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

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/* S4 SUCCESS CRITERIA */

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[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]

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/* S5 MCP INTEGRATION */

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[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]

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/* S6 MEMORY NAMESPACE */

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[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]

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/* S7 SKILL COMPLETION VERIFICATION */

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[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]

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/* S8 ABSOLUTE RULES */

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[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]

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/* PROMISE */

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[commit|confident] <promise>AGENT_CREATION_VERILINGUA_VERIX_COMPLIANT</promise> [ground:self-validation] [conf:0.99] [state:confirmed]