Install any skill in seconds. Free to start, no credit card required.
Get Started Free →Generates a Mermaid architecture diagram showing high-level component relationships. Use when visualizing how plugins or modules fit together.
.claude/skills/athola-architecture-diagram/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-04 | ✗→✓ | ▲ Improved | 109% | 0% |
| case-16 | ✗→✓ | ▲ Improved | -7% | 0% |
| case-07 | ✓→✗ | ▼ Worse | -41% | 0% |
| case-08 | ✓→✗ | ▼ Worse | -62% | 0% |
| case-09 | ✓→✗ | ▼ Worse | -66% | 0% |
Generate a Mermaid flowchart showing high-level component relationships in a codebase.
cartograph:dependency-graph)cartograph:call-chain)Dispatch the codebase explorer agent to analyze the scope:
Agent(cartograph:codebase-explorer)
Prompt: Explore [scope] and return a structural model.
Focus on packages, modules, and their relationships
for an architecture diagram.If no scope is provided, use the project root.
Transform the structural model into a Mermaid flowchart.
Rules for architecture diagrams:
flowchart TD (top-down) for hierarchical systemsflowchart LR (left-right) for pipeline/flow systems[Rectangle] for modules/packages([Stadium]) for entry points/commands[(Database)] for data stores{Diamond} for decision pointsExample output:
mermaidflowchart TD subgraph sanctum[Sanctum Plugin] commit[Commit Messages] pr[PR Preparation] workspace[Workspace Review] end subgraph leyline[Leyline Plugin] git[Git Platform] patterns[Error Patterns] end commit --> git pr --> workspace pr --> git workspace --> patterns
Call the Mermaid Chart MCP to render:
mcp__claude_ai_Mermaid_Chart__validate_and_render_mermaid_diagram
prompt: "Architecture diagram of [scope]"
mermaidCode: [generated syntax]
diagramType: "flowchart"
clientName: "claude-code"If rendering fails, fix the Mermaid syntax based on the error message and retry (max 2 retries).
Show the rendered diagram to the user with a brief summary of what it depicts (2-3 sentences).
flowchart syntax generated containing at leastone subgraph block grouping related modules
mcp__claude_ai_Mermaid_Chart__validate_and_render_mermaid_diagramcalled and returns without error (or retry attempted on first failure and result reported)
alongside the rendered output
still presented as a code block with the error surfaced
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 8,043 | 5,945 | -26% | 1 | 1 | 0% | 1,214 | 1,096 | -10% | 0 | 0 | — |
case-02 | fail→fail | 15,443 | 5,768 | -63% | 1 | 1 | 0% | 2,266 | 1,034 | -54% | 0 | 0 | — |
case-03 | fail→fail | 2,900 | 6,937 | +139% | 1 | 1 | 0% | 262 | 1,142 | +336% | 0 | 0 | — |
case-04 | fail→pass | 5,736 | 8,079 | +41% | 1 | 1 | 0% | 947 | 1,977 | +109% | 0 | 0 | — |
case-05 | fail→fail | 16,171 | 25,371 | +57% | 1 | 1 | 0% | 2,702 | 4,789 | +77% | 0 | 0 | — |
case-06 | pass→pass | 7,239 | 26,838 | +271% | 1 | 1 | 0% | 1,198 | 2,027 | +69% | 0 | 0 | — |
case-07 | pass→fail | 11,449 | 7,198 | -37% | 1 | 1 | 0% | 2,069 | 1,211 | -41% | 0 | 0 | — |
case-08 | pass→fail | 15,277 | 5,703 | -63% | 1 | 1 | 0% | 2,601 | 978 | -62% | 0 | 0 | — |
case-09 | pass→fail | 15,956 | 4,752 | -70% | 1 | 1 | 0% | 2,784 | 942 | -66% | 0 | 0 | — |
case-10 | fail→fail | 10,951 | 6,967 | -36% | 1 | 1 | 0% | 1,961 | 979 | -50% | 0 | 0 | — |
case-11 | pass→fail | 13,337 | 25,297 | +90% | 1 | 1 | 0% | 2,335 | 908 | -61% | 0 | 0 | — |
case-12 | pass→fail | 12,859 | 7,028 | -45% | 1 | 1 | 0% | 2,309 | 1,169 | -49% | 0 | 0 | — |
case-13 | pass→fail | 16,291 | 6,426 | -61% | 1 | 1 | 0% | 2,676 | 1,101 | -59% | 0 | 0 | — |
case-14 | fail→fail | 9,796 | 5,450 | -44% | 1 | 1 | 0% | 1,660 | 1,101 | -34% | 0 | 0 | — |
case-15 | fail→fail | 12,169 | 9,949 | -18% | 1 | 1 | 0% | 2,163 | 1,143 | -47% | 0 | 0 | — |
case-16 | fail→pass | 14,655 | 8,834 | -40% | 1 | 1 | 0% | 2,339 | 2,177 | -7% | 0 | 0 | — |
case-17 | pass→fail | 15,381 | 6,792 | -56% | 1 | 1 | 0% | 2,641 | 1,043 | -61% | 0 | 0 | — |
case-18 | fail→fail | 11,254 | 7,266 | -35% | 1 | 1 | 0% | 1,813 | 1,088 | -40% | 0 | 0 | — |
case-19 | fail→fail | 15,190 | 6,398 | -58% | 1 | 1 | 0% | 2,494 | 1,143 | -54% | 0 | 0 | — |
case-20 | pass→fail | 6,701 | 21,830 | +226% | 1 | 1 | 0% | 1,175 | 1,978 | +68% | 0 | 0 | — |
case-21 | pass→fail | 15,468 | 8,065 | -48% | 1 | 1 | 0% | 2,655 | 1,052 | -60% | 0 | 0 | — |
case-22 | fail→fail | 11,371 | 28,269 | +149% | 1 | 1 | 0% | 2,012 | 1,132 | -44% | 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, and 4 counted toward the lift figure. The other 18 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of -32 percentage points is the difference between those two pass rates over the 4 comparable cases. 10 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.