# Prompt Forge > Prompt Forge - Skill: `dnyoussef/prompt-forge` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add dnyoussef/prompt-forge` - Raw SKILL.md: https://api.skillmd.com/api/skills/dnyoussef/prompt-forge/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: DNYoussef (https://skillmd.com/u/dnyoussef) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/dnyoussef/prompt-forge --- /*============================================================================*/ /* PROMPT-FORGE SKILL :: VERILINGUA x VERIX EDITION */ /*============================================================================*/ --- name: prompt-forge version: 2.0.1 description: | [assert|neutral] Meta-prompt that generates improved prompts and templates. Can improve other prompts including Skill Forge and even itself. All improvements are gated by frozen eval harness. Use when optimizing promp [ground:given] [conf:0.95] [state:confirmed] category: foundry tags: - meta-prompt - self-improvement - recursive - dogfooding - cognitive-frames author: system cognitive_frame: primary: compositional goal_analysis: first_order: "Execute prompt-forge workflow" second_order: "Ensure quality and consistency" third_order: "Enable systematic foundry processes" --- /*----------------------------------------------------------------------------*/ /* S0 META-IDENTITY */ /*----------------------------------------------------------------------------*/ [define|neutral] SKILL := { name: "prompt-forge", category: "foundry", version: "2.0.1", 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: ["prompt-forge", "foundry", "workflow"], context: "user needs prompt-forge capability" } [ground:given] [conf:1.0] [state:confirmed] /*----------------------------------------------------------------------------*/ /* S3 CORE CONTENT */ /*----------------------------------------------------------------------------*/ # Prompt Forge (Meta-Prompt) ## Kanitsal Cerceve (Evidential Frame Activation) Kaynak dogrulama modu etkin. ## Purpose Generate improved prompts and templates with: - Explicit rationale for each change - Predicted improvement metrics - Risk assessment - Actionable diffs **Key Innovation**: Can improve Skill Forge prompts, then Skill Forge can improve Prompt Forge prompts - creating a recursive improvement loop. ## When to Use - Optimizing existing prompts for better performance - Creating prompt diffs with clear rationale - Running the recursive improvement loop - Auditing prompts for common issues ## MCP Requirements ### memory-mcp (Required) **Purpose**: Store proposals, test results, version history **Activation**: ```bash claude mcp add memory-mcp npx @modelcontextprotocol/server-memory ``` --- ## Core Operations ### Operation 1: Analyze Prompt Before improving, deeply understand the target prompt. ```yaml analysis: target: "{prompt_path}" structural_analysis: sections: [list of sections] flow: "How sections connect" dependencies: "What inputs/outputs exist" quality_assessment: clarity: score: 0.0-1.0 issues: ["Ambiguous instruction in section X"] completeness: score: 0.0-1.0 issues: ["Missing failure handling for case Y"] precision: score: 0.0-1.0 issues: ["Vague success criteria in section Z"] pattern_detection: evidence_based_techniques: self_consistency: present|missing|partial program_of_thought: present|missing|partial plan_and_solve: present|missing|partial failure_handling: explicit_errors: present|missing|partial edge_cases: present|missing|partial uncertainty: present|missing|partial improvement_opportunities: - area: "Section X" issue: "Lacks explicit timeout handling" priority: high|medium|low predicted_impact: "+X% reliability" ``` ### Operation 2: Generate Improvement Proposal Create concrete, testable improvement proposals. ```yaml proposal: id: "prop-{timestamp}" target: "{prompt_path}" type: "prompt_improvement" summary: "One-line description of improvement" changes: - section: "Section name" location: "Line X-Y" before: | Original text... after: | Improved text... rationale: "Why this change improves the prompt" technique: "Which evidence-based technique applied" predicted_improvement: primary_metric: "success_rate" expected_delta: "+5%" confidence: 0.8 reasoning: "Based on similar improvements in prompt X" risk_assessment: regression_risk: low|medium|high affected_components: - "Component 1" - "Component 2" rollback_complexity: simple|moderate|complex test_plan: - test: "Run on benchmark task A" expected: "Improvement in clarity score" - test: "Check for regressions in task B" expected: "No degradation" ``` ### Operation 3: Apply Evidence-Based Techniques Systematically apply research-validated prompting patterns. #### Self-Consistency Enhancement ```markdown BEFORE: "Analyze the code and report issues" AFTER: "Analyze the code from three perspectives: 1. Security perspective: What vulnerabilities exist? 2. Performance perspective: What bottlenecks exist? 3. Maintainability perspective: What code smells exist? Cross-reference findings. Flag any inconsistencies between perspectives. Provide confidence scores for each finding. Return only findings that appear in 2+ perspectives OR have >80% confidence." ``` #### Program-of-Thought Enhancement ```markdown BEFORE: "Calculate the optimal configuration" AFTER: "Calculate the optimal configuration step by step: Step 1: Identify all configuration parameters - List each parameter - Document valid ranges - Note dependencies between parameters Step 2: Define optimization criteria - Primary metric: [what to maximize/minimize] - Constraints: [hard limits] - T /*----------------------------------------------------------------------------*/ /* 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/prompt-forge/{project}/{timestamp}", store: ["executions", "decisions", "patterns"], retrieve: ["similar_tasks", "proven_patterns"] } [ground:system-policy] [conf:1.0] [state:confirmed] [define|neutral] MEMORY_TAGGING := { WHO: "prompt-forge-{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] PROMPT_FORGE_VERILINGUA_VERIX_COMPLIANT [ground:self-validation] [conf:0.99] [state:confirmed]