Apply visual changes from the Retune overlay to source code. Use this skill when receiving output from retune MCP tools (retune_get_formatted_changes, retune_get_pending_changes) OR when the user pastes structured visual change output containing "# Visual Changes", "# Comments", "Prop Changes", "Attribute Changes", "SVG Attribute Changes", a Before/After changes table, or property diffs with Token/Variable columns. Triggers on: retune, "Visual Changes", "apply these changes", style diff, design tokens, design variables, property before/after table, visual tweaks, overlay changes, "Comment #", "Address each comment", "Prop Changes", "Attribute Changes".
Multi-source content aggregation for hot topics research. Supports 15+ data sources.
Dynamic research workflow management with self-reflection and backtracking
Analyze test coverage, identify gaps, and create a testing strategy. Does NOT write tests.
- 📁 .claude/
- 📁 .github/
- 📁 .playwright-mcp/
- 📄 .gitignore
- 📄 .pre-commit-config.yaml
- 📄 .python-version
A skill
CSS and UI animation patterns for responsive, polished interfaces. Use when implementing hover effects, tooltips, button feedback, transitions, or fixing animation issues like flicker and shakiness. Covers animation theory, CSS animations, Framer Motion, performance, accessibility, and real-world walkthrough patterns.
- 📁 references/
- 📁 scripts/
- 📄 README.md
- 📄 SKILL.md
Consolidate recent logs, sessions, and existing memory files into durable topic memories, normalize dates, prune stale entries, and keep MEMORY.md short enough for prompt use.
- 📄 examples.md
- 📄 reference.md
- 📄 SKILL.md
Performs GPU kernel correctness and performance evaluation and LLM inference benchmarking with Magpie. Analyzes single or multiple kernels (HIP/CUDA/PyTorch), compares kernel implementations, runs vLLM/SGLang benchmarks with profiling and TraceLens, and runs gap analysis on torch traces. Creates kernel config YAMLs, discovers kernels in a project, and queries GPU specs. Use when the user mentions Magpie, kernel analyze or compare, HIP/CUDA kernel evaluation, vLLM/SGLang benchmark, gap analysis, TraceLens, creating kernel configs, or discovering GPU kernels.
- 📁 bin/
- 📁 docs/
- 📁 evaluations/
- 📄 .gitignore
- 📄 EVALUATION.md
- 📄 LICENSE
Structured B2B software vendor evaluation for buyers. Researches your company, asks domain-expert questions, engages vendor AI agents via the Salespeak Frontdoor API, scores vendors across 7 dimensions, and produces a comparative recommendation with evidence transparency. Use when asked to evaluate, compare, or research B2B software vendors.
The Balanced Coupling model for software design. Use when: designing modular architectures, evaluating coupling between components, reviewing code modularity, deciding whether to split or merge modules/services, assessing integration patterns, classifying coupling as balanced or unbalanced, applying DDD strategic and tactical patterns, reasoning about cohesion vs coupling trade-offs, identifying distributed monolith risks, or explaining why a system is hard to change. Provides the three-dimensional framework (integration strength, distance, volatility) and the balance rule for making coupling decisions.
**Role**: Step 8 (Mode A) — Generate the Mode A Quick Briefing HTML file from analysis-result.json.
Use after implementing features, before claiming a phase is complete, when reviewing AI-generated code, or when code feels overly complex. Also use when you notice repeated patterns across files, a function exceeds 40 lines, nesting exceeds 3 levels, or an abstraction has only one implementation. Covers duplication, dead code, over-engineering, and AI-specific bloat patterns like verbose error handling and redundant type checks.
