Free to get · One-click to use
Clear all Unity Console log entries. Use when you need to: (1) Clear console before running tests or compilation, (2) Start a fresh debugging session, (3) Remove noisy logs to isolate specific output.
Profile Scala compilation at the JVM level using async-profiler to identify bottlenecks in the compiler, JIT, GC, and macro expansion. Use this skill whenever investigating why compilation is slow at the JVM level, generating flame graphs, analyzing async-profiler output, or understanding where the Scala compiler spends time. Complements the macro-profile skill (macro-level) with JVM-level visibility. Triggers on: "flame graph", "async-profiler", "JVM profile", "compilation bottleneck", "where does the compiler spend time", "JIT warmup", "GC during compilation", "profile the build". --- # JVM Compilation Profiling Profile Scala compilation at the JVM level using async-profiler. This gives visibility into JIT compilation, GC pressure, and Scala compiler internals that the macro-level `macro-profile` skill can't see. ## Prerequisites ```bash brew install async-profiler ``` ## Workflow ### 1. Capture profile data Use `JAVA_TOOL_OPTIONS` (not `JAVA_OPTS`) to profile ALL JVMs including the zinc compilation worker. Use `collapsed` format for the analysis script, and optionally also generate an HTML flame graph for visual inspection. ```bash # Clean compilation cache first rm -rf out/benchmark/sanely # Capture collapsed stacks (for analysis script) JAVA_TOOL_OPTIONS="-agentpath:$(brew --prefix async-profiler)/lib/libasyncProfiler.dylib=start,event=cpu,file=/tmp/profile-collapsed.txt,collapsed" \ ./mill --no-server benchmark.sanely.compile # Capture HTML flame graph (for visual inspection) rm -rf out/benchmark/sanely JAVA_TOOL_OPTIONS="-agentpath:$(brew --prefix async-profiler)/lib/libasyncProfiler.dylib=start,event=cpu,file=/tmp/flamegraph.html" \ ./mill --no-server benchmark.sanely.compile open /tmp/flamegraph.html ```
skill-sample/ ├─ SKILL.md ⭐ Required: skill entry doc (purpose / usage / examples / deps) ├─ manifest.sample.json ⭐ Recommended: machine-readable metadata (index / validation / autofill) ├─ LICENSE.sample ⭐ Recommended: license & scope (open source / restriction / commercial) ├─ scripts/ │ └─ example-run.py ✅ Runnable example script for quick verification ├─ assets/ │ ├─ example-formatting-guide.md 🧩 Output conventions: layout / structure / style │ └─ example-template.tex 🧩 Templates: quickly generate standardized output └─ references/ 🧩 Knowledge base: methods / guides / best practices ├─ example-ref-structure.md 🧩 Structure reference ├─ example-ref-analysis.md 🧩 Analysis reference └─ example-ref-visuals.md 🧩 Visual reference
More Agent Skills specs Anthropic docs: https://agentskills.io/home
├─ ⭐ Required: YAML Frontmatter (must be at top) │ ├─ ⭐ name : unique skill name, follow naming convention │ └─ ⭐ description : include trigger keywords for matching │ ├─ ✅ Optional: Frontmatter extension fields │ ├─ ✅ license : license identifier │ ├─ ✅ compatibility : runtime constraints when needed │ ├─ ✅ metadata : key-value fields (author/version/source_url...) │ └─ 🧩 allowed-tools : tool whitelist (experimental) │ └─ ✅ Recommended: Markdown body (progressive disclosure) ├─ ✅ Overview / Purpose ├─ ✅ When to use ├─ ✅ Step-by-step ├─ ✅ Inputs / Outputs ├─ ✅ Examples ├─ 🧩 Files & References ├─ 🧩 Edge cases ├─ 🧩 Troubleshooting └─ 🧩 Safety notes
Skill files are scattered across GitHub and communities, difficult to search, and hard to evaluate. SkillWink organizes open-source skills into a searchable, filterable library you can directly download and use.
We provide AI semantic + keyword search, version updates, multi-metric ranking (downloads / likes / comments / updates), and open SKILL.md standards. You can also discuss usage and improvements on skill detail pages.
Quick Start:
Import/download skills (.zip/.skill), then place locally:
~/.claude/skills/ (Claude Code)
~/.codex/skills/ (Codex CLI)
One SKILL.md can be reused across tools.
Everything you need to know: what skills are, how they work, how to find/import them, and how to contribute.
A skill is a reusable capability package, usually including SKILL.md (purpose/IO/how-to) and optional scripts/templates/examples.
Think of it as a plugin playbook + resource bundle for AI assistants/toolchains.
Skills use progressive disclosure: load brief metadata first, load full docs only when needed, then execute by guidance.
This keeps agents lightweight while preserving enough context for complex tasks.
Use these three together:
Note: file size for all methods should be within 10MB.
Typical paths (may vary by local setup):
One SKILL.md can usually be reused across tools.
Yes. Most skills are standardized docs + assets, so they can be reused where format is supported.
Example: retrieval + writing + automation scripts as one workflow.
Some skills come from public GitHub repositories and some are uploaded by SkillWink creators. Always review code before installing and own your security decisions.
Most common reasons:
We try to avoid that. Use ranking + comments to surface better skills:
