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 cognitiveframe: primary: compositional goalanalysis: firstorder: "Execute prompt-forge workflow" secondorder: "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:

claude mcp add memory-mcp npx @modelcontextprotocol/server-memory

---

Core Operations

Operation 1: Analyze Prompt

Before improving, deeply understand the target prompt.

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.

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

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

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]

## Source & license

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

- **Author:** [DNYoussef](https://github.com/DNYoussef)
- **Source:** [DNYoussef/context-cascade](https://github.com/DNYoussef/context-cascade)
- **License:** MIT

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