/============================================================================/ /* SKILL SKILL :: VERILINGUA x VERIX EDITION / /============================================================================*/

---

name: SKILL version: 1.0.0 description: | [assert|neutral] SKILL skill for research workflows [ground:given] [conf:0.95] [state:confirmed] category: research tags:

• general author: system cognitive_frame: primary: evidential goal_analysis: first_order: "Execute SKILL workflow" second_order: "Ensure quality and consistency" third_order: "Enable systematic research processes"

---

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

[define|neutral] SKILL := { name: "SKILL", category: "research", version: "1.0.0", layer: L1 } [ground:given] [conf:1.0] [state:confirmed]

/----------------------------------------------------------------------------/ /* S1 COGNITIVE FRAME / /----------------------------------------------------------------------------*/

[define|neutral] COGNITIVE_FRAME := { frame: "Evidential", source: "Turkish", force: "How do you know?" } [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: ["SKILL", "research", "workflow"], context: "user needs SKILL capability" } [ground:given] [conf:1.0] [state:confirmed]

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

name: baseline-replication description: "Replicate published ML baseline experiments with exact reproducibility
\ (\xB11% tolerance) for Deep Research SOP Pipeline D. Use when validating baselines,
\ reproducing experiments, verifying published results, or preparing for novel method
\ development." version: 1.0.0 category: research tags:

• research
• analysis
• planning author: ruv

---

Baseline Replication

Kanitsal Cerceve (Evidential Frame Activation)

Kaynak dogrulama modu etkin.

Overview

Replicates published machine learning baseline methods with exact reproducibility, ensuring results match within ±1% tolerance. This skill implements Deep Research SOP Pipeline D baseline validation, which is a prerequisite for developing novel methods.

Prerequisites

• Python 3.8+ with PyTorch/TensorFlow
• Docker (for reproducibility)
• Git and Git LFS
• Access to datasets (HuggingFace, academic repositories)

What This Skill Does

1. Extracts methodology from papers and code repositories
2. Validates datasets match baseline specifications exactly
3. Implements baseline with exact hyperparameters
4. Runs experiments with deterministic settings
5. Validates results within ±1% statistical tolerance
6. Creates reproducibility package tested in fresh Docker environment
7. Generates Quality Gate 1 validation checklist

---

Quick Start (30 minutes)

Basic Replication

# 1. Specify baseline to replicate
BASELINE_PAPER="BERT: Pre-training of Deep Bidirectional Transformers (Devlin et al., 2019)"
BASELINE_CODE="https://github.com/google-research/bert"
TARGET_METRIC="Accuracy on SQuAD 2.0"
PUBLISHED_RESULT=0.948

# 2. Run replication workflow
./scripts/replicate-baseline.sh \
  --paper "$BASELINE_PAPER" \
  --code "$BASELINE_CODE" \
  --metric "$TARGET_METRIC" \
  --expected "$PUBLISHED_RESULT"

# 3. Review results
cat output/baseline-bert/replication-report.md

Expected output:

✓ Paper analyzed: Extracted 47 hyperparameters
✓ Dataset validated: SQuAD 2.0 matches baseline
✓ Implementation complete: 12 BERT layers, 110M parameters
✓ Training complete: 3 epochs, 26.3 GPU hours
✓ Results validated: 0.945 vs 0.948 (within ±1% tolerance)
✓ Reproducibility verified: 3/3 fresh reproductions successful
→ Quality Gate 1: APPROVED

---

Step-by-Step Guide

Phase 1: Paper Analysis (15 minutes)

Extract Methodology

# Coordinate with researcher agent
./scripts/analyze-paper.sh --paper "arXiv:2103.00020"

The script extracts:

• Model architecture (layers, hidden sizes, attention heads)
• Training hyperparameters (learning rate, batch size, warmup)
• Optimization details (optimizer type, weight decay, dropout)
• Dataset specifications (splits, preprocessing, tokenization)
• Evaluation metrics (primary and secondary)

Output: baseline-specification.md with all extracted details

Validate Completeness

# Check for missing hyperparameters
./scripts/validate-spec.sh baseline-specification.md

Common Missing Details:

• Learning rate schedule (linear warmup, cosine decay)
• Random seeds (NumPy, PyTorch, Python)
• Hardware specifications (GPU type, memory)
• Framework versions (PyTorch 1.7 vs 1.13 numerical differences)

If details missing:

1. Check paper supplements
2. Check official code config files
3. Check GitHub issues
4. Contact authors

---

Phase 2: Dataset Validation (20 minutes)

Coordinate with data-steward Agent

# Validate dataset matches baseline specs
./scripts/validate-dataset.sh \
  --dataset "SQuAD 2.0" \
  --splits "train:130k,dev:12k" \
  --preprocessing "WordPiece tokenization, max_length=384"

data-steward checks:

• Exact dataset version (v2.0, not v1.1)
• Sample counts match (training: 130,319 examples)
• Data splits match (80/10/10 vs 90/10)
• Preprocessing matches (lower-casing, accent stripping)
• Checksum validation (SHA256 hashes)

Output: dataset-validation-report.md

---

Ph

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

[define|neutral] MEMORY_TAGGING := { WHO: "SKILL-{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] <promise>SKILL_VERILINGUA_VERIX_COMPLIANT</promise> [ground:self-validation] [conf:0.99] [state:confirmed]