RBraga01

99 mods across 5 repositories, 32 stars between them.

fishbone-analysis

25

RBraga01/Quality-Engineering-Skills

Skill Claude CodeCodex

Build an Ishikawa diagram, cause and effect analysis, or 6M fishbone to brainstorm and categorise all possible causes before narrowing to root cause with 5-Why. Covers Man, Machine, Method, Material, Measurement, and Environment (Mother Nature). Essential for 8D D4 brainstorming sessions and CAPA root cause…

24 4d ago A 74 tokens original MIT

is-is-not-scoping

26

RBraga01/Quality-Engineering-Skills

Skill Claude CodeCodex

Problem scoping with Is/Is-Not for 8D D2 problem description, CAPA investigation, or hypothesis elimination. Defines the precise boundary by contrasting what IS observed vs what IS NOT — eliminating hypotheses that don't fit the pattern. A Ford-originated automotive technique used in every structured quality…

24 4d ago A 67 tokens original MIT

pdca-improvement

27

RBraga01/Quality-Engineering-Skills

Skill Claude CodeCodex

Run a PDCA cycle, Plan Do Check Act improvement cycle, or structured improvement project. Guides through problem analysis, piloting, verification, and standardisation. Distinguished from 8D: PDCA is for proactive improvement initiatives, 8D is for reactive defect response. Use for process optimisation, lessons learned…

24 4d ago A 73 tokens original MIT

RBraga01/Quality-Engineering-Skills

Skill Claude CodeCodex

Run quality problem zeroing, perform 双归零 (double-five zeroing), write a technical zeroing report, write a management zeroing report, or close an aerospace quality problem under GB/T 29076—2021. Covers problem reporting, emergency response, team formation, technical zeroing (定位准确、机理清楚、问题复现、 措施有效、举一反三), management…

24 4d ago A 149 tokens original MIT

action-priority-ap

29

RBraga01/Quality-Engineering-Skills

Skill Claude CodeCodex

Assign AP table ratings (H-AP, M-AP, L-AP) in PFMEA or DFMEA — the RPN replacement from AIAG-VDA FMEA Handbook 2019. Explains H/M/L classification logic, mandatory action requirements for High Priority items, and OEM-specific thresholds. Use when assigning risk levels in PFMEA or DFMEA, or auditing FMEA documents for…

24 4d ago A 87 tokens original MIT

dfmea-design

30

RBraga01/Quality-Engineering-Skills

Skill Claude CodeCodex

Build a design risk analysis, DFMEA worksheet, or interface analysis using the AIAG-VDA FMEA Handbook 2019. Covers design intent, interface failures, boundary diagram, and design robustness before manufacturing. Use during new product development, design changes, or when a field failure reveals a design weakness.…

24 4d ago A 79 tokens original MIT

pfmea-process

31

RBraga01/Quality-Engineering-Skills

Skill Claude CodeCodex

Build a PFMEA worksheet, process risk analysis, or AP table using the AIAG-VDA FMEA Handbook 2019 7-step approach. Covers Structure Analysis, Function Analysis, Failure Analysis, Risk Analysis (Action Priority H/M/L), Optimization, and Documentation. Required by IATF 16949 and OEM customer-specific requirements for…

24 4d ago A 86 tokens original MIT

supplier-scar

32

RBraga01/Quality-Engineering-Skills

Skill Claude CodeCodex

Supplier Corrective Action Request (SCAR) — escalate a supplier non-conformance to a formal corrective action request, define response requirements, evaluate the supplier's 8D response, and verify effectiveness. Use when an NCR escalates to a SCAR, when a supplier delivers repeated non-conformances, or when a field…

24 4d ago A 99 tokens original MIT

builder-design

33

RBraga01/builder-design

Plugin Claude Code

AI UI design quality enforcement: 8 skills and 5 agents for UI design teams.

2 2mo ago A tokens not measured original MIT

RBraga01/builder-design

Agent Claude Code

Reviews AI feature UI for accessibility — ARIA live regions, reading order, cognitive load, keyboard navigation, and screen reader compatibility. Use before any AI feature ships. Produces a verdict with blocking issues, not a list of suggestions.

2 2mo ago A 52 tokens original MIT

ai-ui-designer

35

RBraga01/builder-design

Agent Claude Code

Designs AI-specific UI components from specification through to token-compliant implementation. Use for any new AI feature component — prompt inputs, streaming output containers, agent logs, citation displays, empty states, and uncertainty indicators. Produces full specs before any code.

2 2mo ago A 56 tokens original MIT

prompt-ux-designer

36

RBraga01/builder-design

Agent Claude Code

Designs the prompt input experience for AI features — input pattern, suggestion system, feedback loop, history, and constraints. Use before implementing any AI prompt input or chat interface.

2 2mo ago A 41 tokens original MIT

state-designer

37

RBraga01/builder-design

Agent Claude Code

Designs all required states for AI feature UI touchpoints. Use at the start of any new AI feature — before component code is written. Produces the state map and spec that every other agent depends on.

2 2mo ago A 46 tokens original MIT

ux-critic

38

RBraga01/builder-design

Agent Claude Code

Critiques AI product UX against the builder-design patterns. Use after any AI feature UI is drafted — before it ships. Produces a structured verdict with blocking issues and required fixes, not a list of suggestions.

2 2mo ago A 46 tokens original MIT

RBraga01/builder-design

Instructions file CodexOpenCode

Instructions for RBraga01/builder-design, covering agents — builder-design v1.0.0, ai-ui-designer (opus), ux-critic (sonnet), state-designer (sonnet) and prompt-ux-designer (sonnet).

2 2mo ago A 547 tokens original MIT

RBraga01/builder-design

Instructions file

Instructions for RBraga01/builder-design, covering builder-design — ai ui design skills pack v1.0.0, what this pack is, skills, agents and enforcement model.

2 2mo ago A 556 tokens original MIT

RBraga01/builder-design

Skill Claude CodeCodex

Use before shipping any UI that renders AI-generated content. Dynamic model output requires ARIA live regions, reading order, and cognitive load review that static content does not. Blocks "we'll do accessibility later" completions.

2 2mo ago A 48 tokens original MIT

RBraga01/builder-design

Skill Claude CodeCodex

Use when designing or implementing any of the 6 core AI UI components. Each has specific patterns, pitfalls, and required sub-components that generic UI components don't address.

2 2mo ago A 38 tokens original MIT

RBraga01/builder-design

Skill Claude CodeCodex

Use when designing first-run flows and empty states for AI features. AI onboarding has specific requirements — model capability communication, trust building, and graceful degradation when the model doesn't know — that generic onboarding patterns miss.

2 2mo ago A 46 tokens original MIT

ai-output-design

44

RBraga01/builder-design

Skill Claude CodeCodex

Use when designing how AI-generated content is rendered — streaming text, structured data, citations, code blocks, and uncertainty signals. Covers both visual rendering and the accessibility layer.

2 2mo ago A 38 tokens original MIT

ai-states-required

45

RBraga01/builder-design

Skill Claude CodeCodex

Use before writing any code for an AI feature's UI. All 7 states must be designed and documented before implementation begins. Blocks "we'll add loading states later" completions.

2 2mo ago A 42 tokens original MIT

design-before-code

46

RBraga01/builder-design

Skill Claude CodeCodex

Use before implementing any new AI feature UI component. Requires a written shape/spec — layout, states, copy, interactions — before a single line of implementation code is written. Blocks "I'll design it as I build it" completions.

2 2mo ago A 51 tokens original MIT

design-token-audit

47

RBraga01/builder-design

Skill Claude CodeCodex

Use before any AI feature UI lands in production. Every colour, spacing value, typography, shadow, and border in AI components must reference design tokens — no hardcoded values. Blocks "I'll align with the design system later" completions.

2 2mo ago A 53 tokens original MIT

prompt-ux-design

48

RBraga01/builder-design

Skill Claude CodeCodex

Use when designing the user-facing prompt experience for any AI feature. Covers input design, suggestion patterns, history, feedback signals, and the interaction model between user intent and model execution.

2 2mo ago A 41 tokens original MIT