Content Refiner (The Fixer)

Purpose

POST-GATE TOOL. Transforms content that FAILED Gate 4 into passing content. Focuses on trimming verbosity and fixing continuity.

When to Use

• Trigger: Gate 4 (Acceptance Auditor) returned [FAIL] .

• Goal: Fix word count OR continuity issues (or both).

• Key: Diagnose what failed BEFORE applying fixes.

CRITICAL: Pre-Refinement Diagnosis

DO NOT apply fixes blindly. Gate 4 fails for different reasons requiring different strategies.

Step 0: Identify What Failed (Mandatory)

Ask the user OR examine the Gate 4 failure message:

Failure Type Question Action

Word Count "Is the lesson over the target (typically 1500 words)?" Calculate exact % to cut

Continuity "Does the opening reference the previous lesson?" Rewrite opening only

Both "Word count AND continuity broken?" Two-phase approach

DIAGNOSIS EXAMPLES:

Example 1: Word Count Only

Content: 1950 words, Target: 1500 Excess: 450 words % to cut: (450 / 1950) × 100 = 23% → CUT EXACTLY 23%, not generic 15-20%

Example 2: Continuity Only

Opening: "Let's explore this new topic..." Problem: Doesn't reference Lesson N-1 → Rewrite opening only; don't cut words

Example 3: Both

Word count: 1950 (23% over) Opening: Generic, missing prior lesson reference → Phase 1: Rewrite opening (identify anchor from Lesson N-1) → Phase 2: Cut words to 23% (context-aware)

Step 1: Assess Content Layer (Context-Aware Cutting)

Read the lesson's frontmatter to determine layer:

Layer Cutting Strategy

L1 (Manual) Keep foundational explanations; cut elaboration

L2 (AI-Collaboration) Keep Try With AI sections (core); cut narrative padding

L3 (Intelligence) Keep pattern insights; cut explanatory scaffolding

L4 (Spec-Driven) Keep specification details; cut conceptual scaffolding

The Refinement Procedure (Layer-Aware)

Phase 1: The Connection Builder (Continuity Fix)

Do this FIRST if opening is generic.

Formula:

In [Previous Lesson], you [SPECIFIC OUTCOME from Lesson N-1]. Now, we will [CONNECT outcome to new goal] by [STRATEGY].

Validation:

• Opening references Lesson N-1 by name

• Specific outcome (not generic "learned about...")

• Clear connection shows why this lesson matters (builds on N-1)

After fixing: Proceed to Fluff Cutter if word count also fails.

Phase 2: The Fluff Cutter (Word Count Fix)

Apply layer-specific cuts in this order:

FOR ALL LAYERS:

• Delete redundant "Why This Matters" sections

• Keep ONLY if it reveals non-obvious insight

• If same point made in text AND in "Why This Matters" → delete WTM

• Merge repeated examples

• Find duplicate explanations

• Keep first, delete second

• Tighten transitions between sections

• Replace "As we discussed earlier, X..." with direct reference

FOR L1-L2 ONLY (students still building foundation): 4. Reduce "Try With AI" sections to exactly 2 prompts

• Keep foundational + one advanced

• Delete exploratory extras

• Keep educational scaffolding (explanations, examples)

FOR L3-L4 ONLY (students ready for advanced patterns): 4. Trim narrative scaffolding

• Keep pattern insights and rules

• Delete "why this matters philosophically"

• Remove beginner-level explanations

• Assume students understand fundamentals

FOR ALL LAYERS: 6. One Analogy Rule: Keep the BEST analogy for the concept; delete redundant ones 7. Merge Tables/Text: Use ONE format (table OR prose), never both 8. Reduce Examples: Keep 2-3 best; delete "also consider..." 9. Tighten Lists: Convert 5-item lists to 3 core items

Verification:

• Word count after cuts: [TARGET ± 5%]

• No L1 content cut from L1 lessons

• No pattern insights lost from L3-L4 lessons

• Try With AI: 2 prompts if L1-L2, keep all if L3-L4

Phase 3: Post-Refinement Validation (CRITICAL)

After applying fixes, verify the content now PASSES Gate 4:

✓ Word Count Check: Current: [X] words Target: [target_from_spec] Status: [PASS if ≤target ± 5%, FAIL if over]

✓ Continuity Check: Opening references Lesson [N-1]? [YES/NO] Specific outcome mentioned? [YES/NO] Connection to new lesson clear? [YES/NO]

✓ Layer Appropriateness: No foundational cuts from L1-L2? [YES/NO] No pattern insight loss from L3-L4? [YES/NO]

✓ Content Integrity: Removed examples still explained elsewhere? [YES/NO] Cut sections non-essential? [YES/NO]

NEXT STEP RECOMMENDATION:

"Refined content is ready.

Word count: [after] (target: ≤[target]) Continuity: Now references Lesson [N-1]

Recommend re-submitting to acceptance-auditor for Gate 4 re-validation. Command: [provide re-validation instruction]"

Output Format

Refinement Report: [Lesson Name]

Diagnosis

Issue Found: [Word count | Continuity | Both] Layer: [L1/L2/L3/L4]

Metrics

Fixes Applied

1. Phase 1: Rewrote opening to reference "booking-agent implementation" from Lesson 2
2. Phase 2: Deleted 240 words using layer-aware cuts:
   • Removed redundant "Why This Matters" section (line 45, 120 words)
   • Merged duplicate example (lines 67-89, 85 words)
   • Cut 1 extra "Try With AI" prompt (35 words)
3. Phase 3: Validated word count and continuity

Ready for Re-validation

✅ Word count: 1485 (≤1500) ✅ Continuity: Opening references Lesson 2 ✅ Layer integrity: All L2 AI examples preserved

Next: Re-submit to acceptance-auditor for Gate 4 validation

Refined Content

[Full refined lesson content]