/============================================================================/ /* COGNITIVE-LENSING SKILL :: VERILINGUA x VERIX EDITION / /============================================================================*/

name: cognitive-lensing version: 1.0.1 description: | [assert|neutral] Cross-lingual cognitive framing system that activates different reasoning patterns by embedding multi-lingual activation phrases. Use when facing complex tasks that benefit from specific thinking patt [ground:given] [conf:0.95] [state:confirmed] category: foundry tags:

• cognitive-science
• cross-lingual
• meta-prompting
• frame-selection
• reasoning-enhancement author: system cognitive_frame: primary: compositional goal_analysis: first_order: "Execute cognitive-lensing workflow" second_order: "Ensure quality and consistency" third_order: "Enable systematic foundry processes"

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

[define|neutral] SKILL := { name: "cognitive-lensing", category: "foundry", version: "1.0.1", 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: ["cognitive-lensing", "foundry", "workflow"], context: "user needs cognitive-lensing capability" } [ground:given] [conf:1.0] [state:confirmed]

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

Cognitive-Lensing v1.0.0

Kanitsal Cerceve (Evidential Frame Activation)

Kaynak dogrulama modu etkin.

Purpose

This skill activates specific cognitive patterns by embedding multi-lingual activation phrases that elicit different parts of the AI's latent space. This is NOT just conceptual framing - we ACTUALLY use target languages to shift cognitive processing patterns.

Core Mechanism

Large language models trained on multilingual corpora develop language-specific reasoning patterns tied to grammatical structures:

• Turkish evidential markers activate source-attribution patterns
• Russian aspectual verbs activate completion-state tracking
• Japanese honorific levels activate audience-awareness calibration
• Arabic morphological roots activate semantic decomposition
• Mandarin classifiers activate object-category reasoning
• Guugu Yimithirr cardinal directions activate absolute spatial encoding
• Chinese/Japanese number systems activate transparent place-value arithmetic

By embedding authentic multi-lingual text in prompts, we trigger these latent reasoning modes.

When to Use This Skill

Use cognitive-lensing when:

1. Task complexity exceeds single-frame capacity - Multi-dimensional problems requiring different cognitive modes
2. Quality requirements demand specific reasoning - Audit (evidential), deployment (aspectual), documentation (hierarchical)
3. Standard prompting produces generic outputs - Need to activate specialized thinking patterns
4. Creating new skills/agents - Select optimal cognitive frame for the domain
5. Debugging AI reasoning failures - Wrong frame may cause systematic errors

What This Skill Does

1. Analyzes task goals (1st/2nd/3rd order) to identify required thinking patterns
2. Selects optimal cognitive frame(s) from 7 available patterns
3. Generates multi-lingual activation text that triggers the frame
4. Integrates with other foundry skills (prompt-architect, agent-creator, skill-forge)
5. Stores frame selections in memory-mcp for consistency across sessions

Goal-Based Frame Selection Checklist

Step 1: Analyze Goals

Complete this for every non-trivial task:

Example Analysis:

Step 2: Identify Dominant Thought Process

Example Selection:

For "Write unit tests for API endpoint":

• Tracking done/not done: YES (need to track test coverage completion)
• Source reliability: YES (need to verify test assertions match specs)

Selected Frames:

• Primary: Aspectual (Russian) - for completion tracking
• Secondary: Evidential (Turkish) - for assertion verification

Step 3: Select Primary Frame

Based on analysis, select:

• Primary Frame: _______________
• Secondary Frame (optional): _______________
• Rationale: _______________

Seven Frame Activation Protocols

Frame 1: Evidential (Turkish - Kanitsal Cerceve)

**When to

/----------------------------------------------------------------------------/ /* 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/cognitive-lensing/{project}/{timestamp}", store: ["executions", "decisions", "patterns"], retrieve: ["similar_tasks", "proven_patterns"] } [ground:system-policy] [conf:1.0] [state:confirmed]

[define|neutral] MEMORY_TAGGING := { WHO: "cognitive-lensing-{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] COGNITIVE_LENSING_VERILINGUA_VERIX_COMPLIANT [ground:self-validation] [conf:0.99] [state:confirmed]