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Get Started Free →Creates beautiful ASCII art visualizations for plans, architectures, workflows, and data. This skill should be used when explaining system architecture, creating implementation plans, showing workflows, visualizing comparisons, or documenting file structures. NOT for code syntax highlighting or markdown tables. User explicitly loves ASCII art - use liberally for visual communication.
.claude/skills/aiskillstore-ascii-visualizer/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-03 | ✗→✓ | ▲ Improved | 41% | 0% |
| case-11 | ✗→✓ | ▲ Improved | 82% | 0% |
| case-12 | ✗→✓ | ▲ Improved | 16% | 0% |
| case-13 | ✗→✓ | ▲ Improved | -54% | 0% |
| case-18 | ✗→✓ | ▲ Improved | 53% | 0% |
🎯 SKILL ACTIVATION PROTOCOL To use this skill, announce at the start of the response:
🎯 Using ascii-visualizer skill for visual diagram generationCreate clear ASCII visualizations for ANY concept. USER EXPLICITLY LOVES ASCII ART - use liberally!
┌─────────────────┐
│ Component A │
│ (Description) │
└────────┬────────┘
│
▼
┌─────────────────┐
│ Component B │
└─────────────────┘test-orchestration-demo/
├── .claude/
│ ├── skills/ ⭐ This skill!
│ └── instructions/
├── Docs/
│ └── results-implementation/
└── frontend/ ✨ 7-folder architecture
├── app/ (Next.js routes)
├── modules/ (Feature modules)
├── shared/ (UI components)
├── lib/ (Integrations)
├── store/ (Global state)
├── styles/ (Design system)
└── types/ (TypeScript)User Answer
│
▼
tRPC Endpoint
│
▼
Claude AI → Evaluation
│
▼
Results Store → UI┌──────────────────────────────────────────┐
│ BEFORE (17 folders) AFTER (7 folders)│
├──────────────────────────────────────────┤
│ Complexity: High Simple -60% ⬇️│
│ Type Safety: 70% 100% +30% ✅│
│ Code Lines: 3,455 2,500 -955 🧹│
│ Build Time: 8.5s 7.2s -15% ⚡│
└──────────────────────────────────────────┘DevPrep AI - Results Analytics
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Tab 1: Overview ████████████████ 100% ✅
Tab 2: Questions ████████████████ 100% ✅
Tab 3: Hint Analytics████████████████ 100% ✅
Tab 4: Insights ████████████████ 100% ✅
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┌─┬─┐ ╔═╦═╗ Basic boxes
├─┼─┤ ╠═╬═╣ Heavy boxes
└─┴─┘ ╚═╩═╝ Rounded corners
│ ║ Vertical lines
─ ═ Horizontal lines
▲ ▼ Arrows
► ◄ Arrows horizontal
✅ ❌ Status indicators
🚧 📋 Progress states
⭐ 🔥 PrioritiesCreate ASCII visualizations for:
See examples/devprep-architecture.md for a comprehensive example showing:
This example demonstrates how to create layered visualizations that progress from high-level architecture to detailed implementation flows.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 23,436 | 22,224 | -5% | 1 | 1 | 0% | 3,480 | 4,292 | +23% | 0 | 0 | — |
case-02 | pass→pass | 15,640 | 17,893 | +14% | 1 | 1 | 0% | 2,580 | 3,245 | +26% | 0 | 0 | — |
case-03 | fail→pass | 22,713 | 18,686 | -18% | 1 | 1 | 0% | 2,828 | 3,987 | +41% | 0 | 0 | — |
case-04 | pass→fail | 11,613 | 14,877 | +28% | 1 | 1 | 0% | 978 | 2,634 | +169% | 0 | 0 | — |
case-05 | pass→fail | 8,699 | 5,854 | -33% | 1 | 1 | 0% | 715 | 2,129 | +198% | 0 | 0 | — |
case-06 | pass→pass | 12,208 | 13,630 | +12% | 1 | 1 | 0% | 1,453 | 2,491 | +71% | 0 | 0 | — |
case-07 | pass→fail | 14,184 | 20,547 | +45% | 1 | 1 | 0% | 2,428 | 3,685 | +52% | 0 | 0 | — |
case-08 | pass→pass | 14,382 | 12,591 | -12% | 1 | 1 | 0% | 1,796 | 3,354 | +87% | 0 | 0 | — |
case-09 | fail→fail | 13,110 | 11,096 | -15% | 1 | 1 | 0% | 1,423 | 2,904 | +104% | 0 | 0 | — |
case-10 | pass→pass | 19,428 | 12,641 | -35% | 1 | 1 | 0% | 3,698 | 3,047 | -18% | 0 | 0 | — |
case-11 | fail→pass | 14,452 | 16,119 | +12% | 1 | 1 | 0% | 1,656 | 3,013 | +82% | 0 | 0 | — |
case-12 | fail→pass | 26,705 | 22,239 | -17% | 1 | 1 | 0% | 4,217 | 4,906 | +16% | 0 | 0 | — |
case-13 | fail→pass | 49,989 | 14,633 | -71% | 1 | 1 | 0% | 8,241 | 3,774 | -54% | 0 | 0 | — |
case-14 | pass→pass | 16,322 | 19,773 | +21% | 1 | 1 | 0% | 2,938 | 4,512 | +54% | 0 | 0 | — |
case-15 | fail→fail | 28,057 | 33,884 | +21% | 1 | 1 | 0% | 1,921 | 6,333 | +230% | 0 | 0 | — |
case-16 | pass→pass | 14,091 | 16,407 | +16% | 1 | 1 | 0% | 2,380 | 2,927 | +23% | 0 | 0 | — |
case-17 | fail→fail | 18,433 | 41,259 | +124% | 1 | 1 | 0% | 2,485 | 5,379 | +116% | 0 | 0 | — |
case-18 | fail→pass | 18,210 | 20,512 | +13% | 1 | 1 | 0% | 2,250 | 3,433 | +53% | 0 | 0 | — |
case-19 | fail→fail | 32,490 | 20,751 | -36% | 1 | 1 | 0% | 3,076 | 4,881 | +59% | 0 | 0 | — |
case-20 | fail→pass | 13,976 | 20,856 | +49% | 1 | 1 | 0% | 1,514 | 4,008 | +165% | 0 | 0 | — |
case-21 | pass→pass | 14,055 | 12,070 | -14% | 1 | 1 | 0% | 2,698 | 3,059 | +13% | 0 | 0 | — |
case-22 | fail→pass | 19,345 | 24,763 | +28% | 1 | 1 | 0% | 2,644 | 4,638 | +75% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted. The headline lift of +18 percentage points is the difference between those two pass rates over the 22 comparable cases. 3 cases got worse with the skill loaded, and they are included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.