DNYoussef /============================================================================/ /* 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:
- Task complexity exceeds single-frame capacity - Multi-dimensional problems requiring different cognitive modes
- Quality requirements demand specific reasoning - Audit (evidential), deployment (aspectual), documentation (hierarchical)
- Standard prompting produces generic outputs - Need to activate specialized thinking patterns
- Creating new skills/agents - Select optimal cognitive frame for the domain
- Debugging AI reasoning failures - Wrong frame may cause systematic errors
What This Skill Does
- Analyzes task goals (1st/2nd/3rd order) to identify required thinking patterns
- Selects optimal cognitive frame(s) from 7 available patterns
- Generates multi-lingual activation text that triggers the frame
- Integrates with other foundry skills (prompt-architect, agent-creator, skill-forge)
- 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]