# Cognitive Lensing > Cognitive Lensing - Skill: `dnyoussef/cognitive-lensing` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add dnyoussef/cognitive-lensing` - Raw SKILL.md: https://api.skillmd.com/api/skills/dnyoussef/cognitive-lensing/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: DNYoussef (https://skillmd.com/u/dnyoussef) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/dnyoussef/cognitive-lensing --- /*============================================================================*/ /* 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: | Order | Question | Your Answer | |-------|----------|-------------| | 1st Order Goal | What is the IMMEDIATE task? | _______________ | | 2nd Order Goal | WHY are we doing this task? | _______________ | | 3rd Order Goal | What is the ULTIMATE outcome? | _______________ | **Example Analysis**: | Order | Question | Answer | |-------|----------|--------| | 1st Order | Immediate task | Write unit tests for API endpoint | | 2nd Order | Why | Verify endpoint behavior is correct | | 3rd Order | Ultimate outcome | Ensure production reliability | ### Step 2: Identify Dominant Thought Process | Question | If YES, Use Frame | |----------|-------------------| | Is tracking "done vs not done" critical? | Aspectual (Russian) | | Is source reliability critical? | Evidential (Turkish) | | Is audience/formality critical? | Hierarchical (Japanese) | | Is semantic decomposition needed? | Morphological (Arabic/Hebrew) | | Is physical/visual comparison needed? | Classifier (Mandarin) | | Is spatial navigation needed? | Spatial-Absolute (Guugu Yimithirr) | | Is mathematical precision needed? | Numerical-Transparent (Chinese/Japanese) | **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]