diagram-quality

PlantUML diagram quality on the agent-uml collaborative canvas — three tiers: rendering safety (syntax that prevents HTTP 400 blank canvas), conversation mechanics (when to push a version vs ask a question, what to write in the message parameter), and design effectiveness (decomposition thresholds, cross-diagram traceability, export readiness). Trigger whenever calling agent-uml MCP tools (design_create, diagram_upsert, design_feedback, design_export) — even when the task seems simple, since a missing `as alias` makes elements un-annotatable and a skinparam mismatch makes diagrams unreadable.

Ir a la instalación Revisar archivos

Repositorio: pproenca/dot-skills
Última actividad en el origen: 5 de julio de 2026 a las 07:23
Idioma detectado de SKILL.md: inglés
Estrellas: 211
Forks: 18

Opciones de instalación

De forma predeterminada está seleccionado el prompt que primero revisa el origen. Puedes cambiar a un comando directo o descargar una copia local.

Instalar con un asistente de IA

Copia este prompt en el asistente de IA que estés usando.

Follow https://skillsmp.com/skill-install/prompt.md to install only the Agent Skill at https://github.com/pproenca/dot-skills/tree/master/skills/.experimental/diagram-quality.

Un comando directo omite el prompt de revisión. Revisa el origen antes de ejecutarlo.

npx skills add https://github.com/pproenca/dot-skills --skill diagram-quality

El comando permanece en una sola línea. Desplázate horizontalmente para revisarlo antes de copiarlo.

¿Prefieres una copia local? Descarga los archivos que SkillsMP tiene disponibles ahora.

Revisa los archivos de origen

Lee SKILL.md y los archivos complementarios que muestra SkillsMP antes de decidir si quieres instalarlo.

Explorador de archivos

9 archivos

SKILL.md

Instrucciones de origen · Vista previa de solo lectura

name: diagram-quality
description: PlantUML diagram quality on the agent-uml collaborative canvas — three tiers: rendering safety (syntax that prevents HTTP 400 blank canvas), conversation mechanics (when to push a version vs ask a question, what to write in the message parameter), and design effectiveness (decomposition thresholds, cross-diagram traceability, export readiness). Trigger whenever calling agent-uml MCP tools (design_create, diagram_upsert, design_feedback, design_export) — even when the task seems simple, since a missing `as alias` makes elements un-annotatable and a skinparam mismatch makes diagrams unreadable.

# agent-uml Diagram Quality Best Practices Make the human-Claude design conversation converge faster on the agent-uml canvas. ## When to Apply - Before every `diagram_upsert` call — check syntax safety rules and apply the correct skinparam preset - After every `design_feedback` response — consult the versioning decision table to choose the right next action - When starting a new design session — follow the progressive detail layers for diagram ordering - Before calling `design_export` — run the export-readiness checklist - When a diagram renders blank or elements aren't clickable — consult rendering safety rules ## Rule Categories by Priority | Priority | Section | Impact | Reference | |----------|---------|--------|-----------| | 1 | Rendering Safety | CRITICAL | [syn-safety.md](references/syn-safety.md), [_presets.md](references/_presets.md) | | 2 | Conversation Mechanics | HIGH | [_conversation.md](references/_conversation.md) | | 3 | Design Effectiveness | MEDIUM | [_design.md](references/_design.md) | ## Quick Reference ### 1. Rendering Safety (CRITICAL) 18 rules that prevent blank canvas and ensure interactive SVG. Read [syn-safety.md](references/syn-safety.md) before writing any PlantUML source. Key rules: - **Every element gets `as Alias`** — without it, element is not clickable on canvas - **`skinparam backgroundColor transparent`** — white default clashes with #f4f1ec canvas - **Copy the correct preset from [_presets.md](references/_presets.md)** — one block per diagram type, matched to canvas CSS variables ### 2. Conversation Mechanics (HIGH) 5 decision tables for the feedback-response loop. Read [_conversation.md](references/_conversation.md) when deciding what tool to call next. Key tables: - **Table 4: Signal → Action** — maps annotation, chat, silence, and timeout to the correct tool call - **Table 5: Message content** — what to write in the `message` parameter (explain change + ask focusing question) ### 3. Design Effectiveness (MEDIUM) 4 heuristics with concrete thresholds. Read [_design.md](references/_design.md) when planning diagram sequences or preparing for export. Key heuristics: - **Progressive detail layers** — L1 context → L2 container → L3 class → L4 behavior → L5 state - **Cross-reference traceability** — every sequence participant must map to a component; every interface belongs to exactly one component boundary - **Decomposition thresholds** — component >12, class >15, sequence >10 participants or >20 messages → split ## How to Use 1. **Starting a session:** Read [_conversation.md](references/_conversation.md) Table 3 (diagram type scope) to decide which diagram to create first 2. **Writing PlantUML:** Apply the skinparam preset from [_presets.md](references/_presets.md), then check [syn-safety.md](references/syn-safety.md) for the rules relevant to your diagram type 3. **After feedback:** Consult [_conversation.md](references/_conversation.md) Table 4 (signal → action) to decide whether to push a new version or reply with a question 4. **Before export:** Run the checklist in [_design.md](references/_design.md) Heuristic 4 ## Reference Files | File | Description | |------|-------------| | [references/_sections.md](references/_sections.md) | Section definitions and ordering | | [references/syn-safety.md](references/syn-safety.md) | 18 rendering safety rules (Incorrect/Correct) | | [references/_presets.md](references/_presets.md) | 5 copy-paste skinparam presets per diagram type | | [references/_conversation.md](references/_conversation.md) | 5 decision tables for conversation loop | | [references/_design.md](references/_design.md) | 4 heuristics with thresholds | | [assets/templates/_template.md](assets/templates/_template.md) | Template for adding new rules | | [metadata.json](metadata.json) | Version and reference information |

Ver en GitHub

Volver a la búsqueda Volver a la instalación

ray-llm

LLM workloads on open-source Ray (pinned to 2.57) — OpenAI-compatible serving with ray.serve.llm (vLLM-backed LLMConfig + build_openai_app) and batch inference with ray.data.llm (build_processor). Corrects the stale defaults a model produces — the archived ray-llm repo and its YAML configs, hand-rolled vLLM engines inside plain Serve deployments, the removed build_llm_processor name, deprecated boolean stage flags, top-level LLMServer/LLMRouter imports, free-form accelerator strings, one-deployment-per-LoRA-adapter designs — with the 2.57 idioms (stage configs, placement_group_config over hand-rolled PGs, deployment_config autoscaling, dynamic LoRA multiplexing, prefix-cache-affinity routing, the full OpenAI endpoint surface). Use when writing, reviewing, or productionizing LLM serving or batch inference on Ray. Classic-ML Ray (Train/Tune/Data/Serve/clusters) lives in the sibling ray skill.

2026-08-15

ray

Production Ray (open-source, pinned to 2.57) for classic-ML workloads from training to serving — Ray Train, Tune, Data, Serve, Core, and cluster deployment on KubeRay. Corrects the older-corpus defaults a model reaches for (ray.air session reporting, Trainer-inside-Tuner, tune.run, map_batches concurrency=, DatasetPipeline/to_torch, max_concurrent_queries, RayServeHandle + ray.get, Deployment.deploy, ray.state, ray.get-in-a-loop) with the 2.57 idioms that replaced them (Train V2 defaults, driver-function tuning, compute strategies, streaming datasets, DeploymentHandle/DeploymentResponse, serve build/deploy, ray.util.state, KubeRay CRDs and Jobs API). Use when writing, reviewing, or productionizing Python code that touches Ray distributed training, data pipelines, hyperparameter tuning, model serving, or Ray cluster operations. LLM serving/batch-inference on Ray lives in the sibling ray-llm skill.

2026-08-15

mlflow-3

MLflow 3 (open-source, pinned to 3.15) for classic-ML MLOps — logging and registering models, promoting versions across dev/staging/prod, standing up a tracking server, evaluating with gates, and serving. Corrects the MLflow 2-era defaults a model reaches for (artifact_path, registry stages and get_latest_versions, top-level mlflow.evaluate with baseline_model, runs-URI registration, pickle serialization, mlruns file stores, MLServer serving) with the MLflow 3 idioms that replaced them (named LoggedModels, aliases and copy_model_version, models.evaluate plus validate_evaluation_results, skops/torch.export defaults, database backends, the FastAPI scoring server). Use when writing, reviewing, or migrating Python code that touches MLflow tracking, the model registry, evaluation, or serving.

2026-08-15

Ver todos los de este repositorio

diagram-quality

Datos de origen

Opciones de instalación

Instalar con un asistente de IA

Revisa los archivos de origen

Más de este repositorio

Instalar con un asistente de IA

Más de este repositorio