/============================================================================/
/* 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]