Fory Performance Optimization
apache/fory
Run profile-driven bottleneck optimization across Apache Fory implementations (Java, C++, Python/Cython, Go, Rust, Swift, C, JavaScript/TypeScript, Dart, Kotlin, Scala).
Profile programs at the function/method level to identify performance hotspots, bottlenecks, and optimization opportunities.
Install Interval Profiling Performance Analyzer
$ npx skills add ArabelaTso/Skills-4-SE --skill interval-profiling-performance-analyzer -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install ArabelaTso/Skills-4-SE interval-profiling-performance-analyzer --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/ArabelaTso/Skills-4-SE.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/interval-profiling-performance-analyzer .claude/skills/interval-profiling-performance-analyzer && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "interval-profiling-performance-analyzer" agent skill from https://github.com/ArabelaTso/Skills-4-SE/tree/main/skills/interval-profiling-performance-analyzer into .claude/skills/interval-profiling-performance-analyzer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "interval-profiling-performance-analyzer", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/ArabelaTso/Skills-4-SE/tree/main/skills/interval-profiling-performance-analyzerType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add ArabelaTso/Skills-4-SE --skill interval-profiling-performance-analyzer -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install ArabelaTso/Skills-4-SE interval-profiling-performance-analyzer --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ArabelaTso/Skills-4-SE.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/interval-profiling-performance-analyzer .agents/skills/interval-profiling-performance-analyzer && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "interval-profiling-performance-analyzer" agent skill from https://github.com/ArabelaTso/Skills-4-SE/tree/main/skills/interval-profiling-performance-analyzer into .agents/skills/interval-profiling-performance-analyzer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "interval-profiling-performance-analyzer", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add ArabelaTso/Skills-4-SE --skill interval-profiling-performance-analyzer -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install ArabelaTso/Skills-4-SE interval-profiling-performance-analyzer --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ArabelaTso/Skills-4-SE.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/interval-profiling-performance-analyzer .cursor/skills/interval-profiling-performance-analyzer && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "interval-profiling-performance-analyzer" agent skill from https://github.com/ArabelaTso/Skills-4-SE/tree/main/skills/interval-profiling-performance-analyzer into .cursor/skills/interval-profiling-performance-analyzer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "interval-profiling-performance-analyzer", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/ArabelaTso/Skills-4-SE.git --path skills/interval-profiling-performance-analyzer--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add ArabelaTso/Skills-4-SE --skill interval-profiling-performance-analyzer -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install ArabelaTso/Skills-4-SE interval-profiling-performance-analyzer --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ArabelaTso/Skills-4-SE.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/interval-profiling-performance-analyzer .gemini/skills/interval-profiling-performance-analyzer && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "interval-profiling-performance-analyzer" agent skill from https://github.com/ArabelaTso/Skills-4-SE/tree/main/skills/interval-profiling-performance-analyzer into .gemini/skills/interval-profiling-performance-analyzer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "interval-profiling-performance-analyzer", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install ArabelaTso/Skills-4-SE interval-profiling-performance-analyzerInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add ArabelaTso/Skills-4-SE --skill interval-profiling-performance-analyzer -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/ArabelaTso/Skills-4-SE.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/interval-profiling-performance-analyzer .github/skills/interval-profiling-performance-analyzer && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "interval-profiling-performance-analyzer" agent skill from https://github.com/ArabelaTso/Skills-4-SE/tree/main/skills/interval-profiling-performance-analyzer into .github/skills/interval-profiling-performance-analyzer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "interval-profiling-performance-analyzer", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add ArabelaTso/Skills-4-SE --skill interval-profiling-performance-analyzer -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install ArabelaTso/Skills-4-SE interval-profiling-performance-analyzer --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ArabelaTso/Skills-4-SE.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/interval-profiling-performance-analyzer .opencode/skills/interval-profiling-performance-analyzer && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "interval-profiling-performance-analyzer" agent skill from https://github.com/ArabelaTso/Skills-4-SE/tree/main/skills/interval-profiling-performance-analyzer into .opencode/skills/interval-profiling-performance-analyzer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "interval-profiling-performance-analyzer", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
interval-profiling-performance-analyzer- GitHub stars
- 253
- Token cost
- ~1.7k tokens
- SKILL.md length
- 653 words
- Files
- 8 (incl. scripts, references)
- Skills in repo
- 150
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Profile programs at the function/method level to identify performance hotspots, bottlenecks, and optimization opportunities.
- Works in 6 steps: Understand Requirements → Select Profiling Tool → Run Profiling → …
- Analyze program performance
- SKILL.md covers Workflow, Common Patterns, Important Notes and Troubleshooting, plus 1 more section
- Runs Python scripts from its folder; calls python and apt-get
What it does
Interval Profiling Performance Analyzer is an agent skill from ArabelaTso/Skills-4-SE. Profile programs at the function/method level to identify performance hotspots, bottlenecks, and optimization opportunities. Records execution time, memory usage, and call frequency for each interval. Generates actionable recommendations and visualizations. Use when users need to (1) analyze program performance, (2) identify slow functions or bottlenecks, (3) optimize execution time or memory usage, (4) profile Python, Java, or C/C++ programs with test cases or workload scenarios, or (5) generate performance…
Its SKILL.md is about 1.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 9 other files, including scripts and reference files (for example `references/optimization-patterns.md`, `references/profiling-tools.md` and `scripts/generate_visualization.py`).
It sits in Development, covering Performance optimization and Test generation. It works with Java, C++ and Python. The repository describes itself as: A curated list of 180+ useful Claude Skills for Software Engineering and resources for customizing AI for SE workflows. The licence is Apache-2.0.
When your agent uses it
- Analyze program performance
- Identify slow functions
- Optimize execution time
- C/C++ programs with test cases
Example prompts
- “/interval-profiling-performance-analyzer”
Requirements
- Python 3
Workflow steps
6 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit 4f38503. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships 4 files in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
pythonapt-get
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Interval Profiling Performance Analyzer loads about 1.7k tokens when it runs, and up to ~4.8k if it reads all its reference files. Until then it costs about 150 tokens; SKILL.md has 653 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check: notes
The automated check noted patterns worth knowing about, such as sudo or a known installer.
- NoteRuns commands with sudoSKILL.md:176
sudo apt-get install linux-tools-generic
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.
SKILL.md
The full file from ArabelaTso/Skills-4-SE at commit 4f38503, republished under its Apache-2.0 licence (© ArabelaTso). 653 words, ~1,682 tokens.
Download SKILL.mdSave it as
.claude/skills/interval-profiling-performance-analyzer/SKILL.md (or your agent's skills folder). This skill also uses 7 other files; get the full folder from GitHub.- name
- interval-profiling-performance-analyzer
- description
- Profile programs at the function/method level to identify performance hotspots, bottlenecks, and optimization opportunities. Records execution time, memory usage, and call frequency for each interval. Generates actionable recommendations and visualizations. Use when users need to (1) analyze program performance, (2) identify slow functions or bottlenecks, (3) optimize execution time or memory usage, (4) profile Python, Java, or C/C++ programs with test cases or workload scenarios, or (5) generate performance reports with flame graphs and recommendations.
Interval Profiling & Performance Analyzer
Profile programs to identify performance bottlenecks and generate optimization recommendations with visualizations.
Workflow
1. Understand Requirements
Clarify the profiling task:
- Target program: Which file/executable to profile?
- Language: Python, Java, or C/C++?
- Test scenarios: What workload or test cases to run?
- Focus area: CPU time, memory usage, or both?
2. Select Profiling Tool
Choose based on language and environment:
Python:
- Use
scripts/profile_python.py(cProfile + tracemalloc) - Captures function-level timing and memory usage
- No code modification required
Java:
- Use
scripts/profile_java.py(Java Flight Recorder) - Requires JDK 11+ with JFR support
- Low overhead, production-safe
C/C++:
- Use
scripts/profile_cpp.py(perf or gprof) perf: Linux only, no recompilation neededgprof: Cross-platform, requires-pgcompilation flag
For detailed tool information, see references/profiling-tools.md.
3. Run Profiling
Execute the appropriate profiling script:
Python example:
python scripts/profile_python.py target_script.pyJava example:
python scripts/profile_java.py MainClass ./bin 30
# Arguments: MainClass, classpath, duration_secondsC/C++ example:
python scripts/profile_cpp.py ./program --tool perf
# Or use gprof (requires compilation with -pg):
python scripts/profile_cpp.py ./program --tool gprofAll scripts generate profile_results.json containing:
- intervals: All profiled functions with metrics
- hotspots: Functions exceeding 5% of execution time
- recommendations: Actionable optimization suggestions
- summary: Overall statistics
4. Generate Visualizations
Create interactive HTML report and flame graph data:
python scripts/generate_visualization.py profile_results.json profile_report.htmlOutputs:
profile_report.html: Interactive report with charts and recommendationsflamegraph.txt: Data for flame graph generation (use flamegraph.pl if available)
5. Analyze Results
Review the generated report:
Hotspots section: Functions consuming the most time
- Focus optimization efforts here (80/20 rule)
- Look for high call counts or slow per-call times
Recommendations section: Specific suggestions for each hotspot
- Language-specific patterns (e.g., use StringBuilder in Java)
- Algorithm improvements (e.g., reduce call frequency)
- Data structure optimizations
Memory usage: Identify memory-intensive operations
- Large allocations or many small objects
- Potential memory leaks
6. Provide Recommendations
Summarize findings for the user:
- Top 3-5 hotspots with their impact (% of total time)
- Specific optimization suggestions from the recommendations
- Quick wins: Easy changes with high impact
- Deeper optimizations: Algorithm or architecture changes
Reference references/optimization-patterns.md for detailed optimization techniques.
Common Patterns
Pattern 1: Quick Performance Check
User wants to know "why is my program slow?"
- Run profiling script on the program
- Generate HTML report
- Identify top 3 hotspots
- Provide specific recommendations for each
Pattern 2: Before/After Comparison
User wants to verify optimization effectiveness.
- Profile original version → save as
before.json - User applies optimizations
- Profile optimized version → save as
after.json - Compare hotspots and total execution time
- Quantify improvement
Pattern 3: Memory Leak Investigation
User suspects memory issues.
- Run Python profiling (includes memory tracking)
- Review memory_usage section in results
- Identify functions with high memory allocation
- Suggest using generators, object pooling, or cleanup
Show full SKILL.md (244 more words)Show less
Pattern 4: Multi-Scenario Profiling
User wants to profile different workloads.
- Create test scripts for each scenario
- Profile each scenario separately
- Compare hotspots across scenarios
- Identify common bottlenecks vs scenario-specific issues
Important Notes
Python Profiling
- Profiling adds ~10-30% overhead
- Memory tracking (tracemalloc) adds additional overhead
- Results are deterministic (not sampling-based)
Java Profiling
- Requires JDK 11+ for JFR
- JFR has <1% overhead, safe for production
- May need to adjust duration for long-running programs
- Alternative: Use VisualVM for GUI-based profiling
C/C++ Profiling
- perf: Linux only, requires debug symbols for readable output
- Compile with
-gflag for function names - May need
sudofor system-wide profiling
- Compile with
- gprof: Requires recompilation with
-pgflag- Not suitable for multithreaded programs
- Higher overhead than perf
Optimization Guidelines
- Profile first, optimize second: Don't guess where the bottleneck is
- Focus on hotspots: Optimizing cold code wastes time
- Measure impact: Verify optimizations actually help
- Consider readability: Don't sacrifice maintainability for minor gains
- Algorithm > micro-optimizations: O(n²) → O(n log n) beats loop tweaks
Troubleshooting
"perf not found" (C/C++):
sudo apt-get install linux-tools-generic"JFR file not created" (Java):
- Ensure JDK 11+ is installed
- Check program actually runs and completes
- Try increasing duration parameter
"No profiling data" (any language):
- Verify program actually executes (doesn't exit immediately)
- Check for errors in program output
- Ensure test scenarios exercise the code
"Flame graph not generating":
- Install flamegraph.pl from github.com/brendangregg/FlameGraph
- Or use the HTML report which includes bar charts
Resources
- references/profiling-tools.md: Detailed tool documentation and selection guide
- references/optimization-patterns.md: Language-specific optimization patterns and best practices
© ArabelaTso, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 7 other files (scripts, references) in skills/interval-profiling-performance-analyzer of ArabelaTso/Skills-4-SE.
- SKILL.md
- USAGE.txt
- references/optimization-patterns.md
- references/profiling-tools.md
- scripts/generate_visualization.py
- scripts/profile_cpp.py
- scripts/profile_java.py
- scripts/profile_python.py
Open the folder on GitHubat commit 4f38503
Compare with similar skills
Interval Profiling Performance Analyzer next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Interval Profiling Performance Analyzer this skillArabelaTso/Skills-4-SE | 253 | — | ~1.7k | Automated safety check: Notes | Apache-2.0 | |
| Fory Performance Optimizationapache/fory | 4.6k | — | ~2.2k | Automated safety check: Pass | Apache-2.0 | |
| MCP Debuggerdebugmcp/mcp-debugger | 171 | — | ~3.8k | Automated safety check: Pass | MIT | |
| Dbgtheodo-group/debug-that | 158 | — | ~2.2k | Automated safety check: Pass | MIT | |
| Climber Step Minimizationben-manes/caffeine | 18k | — | ~3k | Automated safety check: Notes | Apache-2.0 | |
| Code Review Excellenceandrew-yangy/gru-ai | 155 | — | ~1.7k | Automated safety check: Notes | MIT |
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Categories
Questions about Interval Profiling Performance Analyzer
What does Interval Profiling Performance Analyzer do?
Profile programs at the function/method level to identify performance hotspots, bottlenecks, and optimization opportunities. Interval Profiling Performance Analyzer is an agent skill from ArabelaTso/Skills-4-SE. Profile programs at the function/method level to identify performance hotspots, bottlenecks, and optimization opportunities.
When should I use Interval Profiling Performance Analyzer?
Interval Profiling Performance Analyzer fits situations like: analyze program performance; identify slow functions; optimize execution time; C/C++ programs with test cases.
How do I install Interval Profiling Performance Analyzer in Claude Code?
Run `npx skills add ArabelaTso/Skills-4-SE --skill interval-profiling-performance-analyzer -a claude-code`. Or copy the skill folder (skills/interval-profiling-performance-analyzer in ArabelaTso/Skills-4-SE) into .claude/skills/interval-profiling-performance-analyzer in your project. Claude Code loads it when a task matches its description.
How do I install Interval Profiling Performance Analyzer in Codex?
Run `npx skills add ArabelaTso/Skills-4-SE --skill interval-profiling-performance-analyzer -a codex`. Or copy the skill folder (skills/interval-profiling-performance-analyzer in ArabelaTso/Skills-4-SE) into .agents/skills/interval-profiling-performance-analyzer in your project. Codex loads it when a task matches its description.
Can I use Interval Profiling Performance Analyzer in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add ArabelaTso/Skills-4-SE --skill interval-profiling-performance-analyzer -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/interval-profiling-performance-analyzer, .gemini/skills/interval-profiling-performance-analyzer, .github/skills/interval-profiling-performance-analyzer and .opencode/skills/interval-profiling-performance-analyzer in your project.
What does Interval Profiling Performance Analyzer need to run?
Going by SKILL.md and its folder, Interval Profiling Performance Analyzer needs Python for the scripts in its folder and the command-line tools its instructions call (python and apt-get). Our summary lists: Python 3.
Does Interval Profiling Performance Analyzer access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Interval Profiling Performance Analyzer safe to install?
Our automated static check of SKILL.md found notes only (runs commands with sudo), nothing it rates as a warning. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
What licence does Interval Profiling Performance Analyzer use?
Interval Profiling Performance Analyzer is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Interval Profiling Performance Analyzer use?
About 1.7k tokens (SKILL.md is roughly 6.7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 3.1k tokens, read only when the agent opens those files.
What are the alternatives to Interval Profiling Performance Analyzer?
Skills that share tags, products or a category with Interval Profiling Performance Analyzer: Fory Performance Optimization (apache/fory, 4.6k stars), MCP Debugger (debugmcp/mcp-debugger, 171 stars), Dbg (theodo-group/debug-that, 158 stars) and Climber Step Minimization (ben-manes/caffeine, 18k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Interval Profiling Performance Analyzer?
ArabelaTso (a GitHub user) maintains it in ArabelaTso/Skills-4-SE, which has 253 GitHub stars. The repository holds 150 skills in this directory. The repository was last updated on August 21, 2026.
Source: ArabelaTso/Skills-4-SE on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.