Why Use This This skill provides specialized capabilities for panaversity's codebase.
Use Cases Developing new features in the panaversity repository Refactoring existing code to follow panaversity standards Understanding and working with panaversity's codebase structure
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Updated At Jan 17, 2026, 05:30 AM
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---
name: content-refiner
description: POST-GATE TOOL. Refine verbose content by eliminating redundancy, trimming word count, and strengthening lesson connections. Use ONLY to fix Gate 4 failures.
---
# 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:**
```markdown
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:**
1. 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
2. Merge repeated examples
- Find duplicate explanations
- Keep first, delete second
3. 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
5. 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"
5. 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
```markdown
## Refinement Report: [Lesson Name]
### Diagnosis
**Issue Found**: [Word count | Continuity | Both]
**Layer**: [L1/L2/L3/L4]
### Metrics
| Metric | Before | After | Target | Status |
|--------|--------|-------|--------|--------|
| Word Count | 1950 | 1485 | ≤1500 | ✅ PASS |
| Continuity | Generic opening | References Lesson 2 | Specific reference | ✅ PASS |
### 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]
```