Jpa Patterns
decebals/claude-code-java
JPA/Hibernate patterns and common pitfalls (N+1, lazy loading, transactions, queries).
A skill your agent uses when performance is a concern - sluggish pages, slow queries, bloated bundles, high-latency APIs, or whenever someone says "optimize" or "make it faster"
Install Performance Tuning
$ npx skills add NoobyGains/godmode --skill performance-tuning -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install NoobyGains/godmode performance-tuning --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/NoobyGains/godmode.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/performance-tuning .claude/skills/performance-tuning && 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 "performance-tuning" agent skill from https://github.com/NoobyGains/godmode/tree/master/skills/performance-tuning into .claude/skills/performance-tuning/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "performance-tuning", 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/NoobyGains/godmode/tree/master/skills/performance-tuningType 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 NoobyGains/godmode --skill performance-tuning -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install NoobyGains/godmode performance-tuning --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NoobyGains/godmode.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/performance-tuning .agents/skills/performance-tuning && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "performance-tuning" agent skill from https://github.com/NoobyGains/godmode/tree/master/skills/performance-tuning into .agents/skills/performance-tuning/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "performance-tuning", 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 NoobyGains/godmode --skill performance-tuning -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install NoobyGains/godmode performance-tuning --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NoobyGains/godmode.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/performance-tuning .cursor/skills/performance-tuning && 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 "performance-tuning" agent skill from https://github.com/NoobyGains/godmode/tree/master/skills/performance-tuning into .cursor/skills/performance-tuning/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "performance-tuning", 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/NoobyGains/godmode.git --path skills/performance-tuning--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 NoobyGains/godmode --skill performance-tuning -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install NoobyGains/godmode performance-tuning --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NoobyGains/godmode.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/performance-tuning .gemini/skills/performance-tuning && 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 "performance-tuning" agent skill from https://github.com/NoobyGains/godmode/tree/master/skills/performance-tuning into .gemini/skills/performance-tuning/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "performance-tuning", 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 NoobyGains/godmode performance-tuningInstalls 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 NoobyGains/godmode --skill performance-tuning -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/NoobyGains/godmode.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/performance-tuning .github/skills/performance-tuning && 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 "performance-tuning" agent skill from https://github.com/NoobyGains/godmode/tree/master/skills/performance-tuning into .github/skills/performance-tuning/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "performance-tuning", 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 NoobyGains/godmode --skill performance-tuning -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install NoobyGains/godmode performance-tuning --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NoobyGains/godmode.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/performance-tuning .opencode/skills/performance-tuning && 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 "performance-tuning" agent skill from https://github.com/NoobyGains/godmode/tree/master/skills/performance-tuning into .opencode/skills/performance-tuning/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "performance-tuning", 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
performance-tuning- GitHub stars
- 107
- Token cost
- ~2.3k tokens
- SKILL.md length
- 835 words
- Files
- 1
- Skills in repo
- 34
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when performance is a concern - sluggish pages, slow queries, bloated bundles, high-latency APIs, or whenever someone says "optimize" or "make it faster"
- Works in 4 steps: Establish a Baseline → Locate the Bottleneck → Resolve the Bottleneck → …
- Performance is a concern - sluggish pages
- SKILL.md covers Overview, The Prime Directive, When to Use and The Entry Protocol, plus 6 more sections
- Calls curl
What it does
Performance Tuning is an agent skill from NoobyGains/godmode. Use when performance is a concern - sluggish pages, slow queries, bloated bundles, high-latency APIs, or whenever someone says "optimize" or "make it faster"
Its SKILL.md is about 2.3k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Databases, covering Query optimization and Web performance. The repository describes itself as: The AI development framework that thinks before it builds. 36 composable skills for Claude Code, Cursor, Codex, and OpenCode. The licence is MIT.
When your agent uses it
- Performance is a concern - sluggish pages
- Bloated bundles
- High-latency APIs
- Whenever someone says optimize
Example prompts
- “optimize”
- “make it faster”
- “/performance-tuning”
Workflow steps
4 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 441103a. 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
Shell commands in SKILL.md call:
curl
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md. Its commands use curl, which can reach the network depending on how they are called.
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
Performance Tuning loads about 2.3k tokens when it runs. Until then it costs about 44 tokens; SKILL.md has 835 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 passed
The automated check found no risky patterns in SKILL.md.
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); files beside SKILL.md are not scanned.
SKILL.md
The full file from NoobyGains/godmode at commit 441103a, republished under its MIT licence (© NoobyGains). 835 words, ~2,270 tokens.
Download SKILL.mdSave it as
.claude/skills/performance-tuning/SKILL.md (or your agent's skills folder).- name
- performance-tuning
- description
- Use when performance is a concern - sluggish pages, slow queries, bloated bundles, high-latency APIs, or whenever someone says "optimize" or "make it faster"
Performance Tuning
Overview
Blind optimization is the root of wasted effort. Measure, pinpoint the bottleneck, fix that specific thing.
Core principle: No optimization without measurement. No measurement without a demonstrated performance problem.
No exceptions. No workarounds. No shortcuts.
The Prime Directive
NO OPTIMIZATION WITHOUT A MEASUREMENT PROVING THE PROBLEMIf you have not profiled it, you are not qualified to optimize it. Intuitions about performance are reliably wrong.
When to Use
digraph perf_gate {
problem [label="Is there a\nmeasurable\nperformance deficit?", shape=diamond];
measure [label="MEASURE\nProfile and locate\nthe bottleneck", shape=box, style=filled, fillcolor="#ccffcc"];
halt [label="HALT\nDo not optimize", shape=box, style=filled, fillcolor="#ffcccc"];
found [label="Bottleneck\npinpointed?", shape=diamond];
fix [label="FIX\nthat specific thing", shape=box, style=filled, fillcolor="#ccccff"];
dig [label="Investigate further\nor accept current\nperformance", shape=box];
problem -> measure [label="yes"];
problem -> halt [label="no"];
measure -> found;
found -> fix [label="yes"];
found -> dig [label="no"];
fix -> measure [label="re-measure"];
}Engage when:
- Users report perceptible slowness
- Telemetry shows regression (response time, page load, throughput)
- Performance budgets are breached (bundle size, Core Web Vitals)
- Database queries exceed 100ms for routine operations
- API responses exceed 500ms for typical requests
Do not engage when:
- "It might be slow someday" (measure when it actually is)
- "Best practice recommends optimizing X" (is X actually slow?)
- Current performance satisfies current requirements
- The feature does not yet work correctly (correctness first)
The Entry Protocol
BEFORE any optimization effort:
1. MEASURE: What is the current performance? (Numbers, not hunches)
2. TARGET: What performance level is required? (Specific threshold)
3. PINPOINT: Where is the bottleneck? (Profiler data, not speculation)
4. FIX: Address that specific bottleneck
5. VERIFY: Did the measurement improve? By how much?
Omit any step = premature optimizationThe Methodology
Phase 1: Establish a Baseline
You must have numbers before changing anything.
| Dimension | How to Measure |
|---|---|
| Page load latency | Lighthouse, WebPageTest, browser DevTools Performance panel |
| API response time | Server logs, APM instrumentation, time curl |
| Query execution time | EXPLAIN ANALYZE, slow query log, ORM query logging |
| Bundle weight | webpack-bundle-analyzer, source-map-explorer |
| Memory consumption | Heap snapshots, process.memoryUsage() |
| CPU utilization | Flame charts via profiler, perf, py-spy |
Record the baseline. You need it to prove the optimization was effective.
Phase 2: Locate the Bottleneck
The bottleneck is almost never where you expect it.
Profile -> identify the function/query/resource consuming the most time
|
That is your optimization target
|
Everything else is a distractionCheck these locations in order (most common first):
- Database queries -- N+1 patterns, absent indexes, full table scans
- Network calls -- Sequential when parallelizable, no caching layer
- Serialization -- Oversized payloads, unnecessary nested data
- Computation -- Suboptimal algorithms, redundant processing
- I/O operations -- File system access, disk reads, external API latency
Phase 3: Resolve the Bottleneck
Fix only what the profiler revealed. Change one variable at a time.
Database Tuning
| Symptom | Remedy |
|---|---|
| N+1 queries | Eager loading / JOIN / batched query |
| Missing index | Add index on columns in WHERE/JOIN/ORDER BY clauses |
| Full table scan | Add appropriate index; constrain result set |
| Oversized result sets | Cursor-based pagination for large datasets |
| Expensive aggregations | Materialized views or pre-computed summaries |
| Lock contention | Tighten transaction scope; introduce read replicas |
-- BEFORE: Diagnose the problem
EXPLAIN ANALYZE SELECT * FROM transactions WHERE account_id = 789;
-- Look for: Seq Scan (missing index), high cost, slow execution
-- AFTER: Add index, re-run EXPLAIN ANALYZE, compare numbersFrontend Tuning (Core Web Vitals)
| Metric | Threshold | Typical Remedies |
|---|---|---|
| LCP (Largest Contentful Paint) | < 2.5s | Optimize hero images, preload critical resources, enable SSR |
| INP (Interaction to Next Paint) | < 200ms | Break long tasks, defer non-critical JS, offload to web workers |
| CLS (Cumulative Layout Shift) | < 0.1 | Set explicit dimensions on media, avoid dynamic content insertion above fold |
Bundle weight reduction:
1. Audit: what occupies space in the bundle?
2. Remove unused dependencies
3. Code-split by route (lazy loading)
4. Ensure ESM imports for tree-shaking
5. Enable compression (gzip/brotli)API Tuning
| Symptom | Remedy |
|---|---|
| Over-fetching | Return only requested fields; support sparse fieldsets |
| Under-fetching | Batch endpoints; return compound documents |
| No caching | Add Cache-Control headers and ETags |
| Synchronous heavy processing | Return 202 Accepted with async processing + polling |
| Oversized responses | Paginate, compress, or stream |
| Slow serialization | Profile the serializer; reduce nesting depth |
Algorithm Tuning
Only when the profiler points to computation as the bottleneck:
| From | To | When Applicable |
|---|---|---|
| O(n^2) nested loops | Hash map lookup O(n) | Large input sets |
| Repeated computation | Memoization or caching | Same inputs, expensive function |
| Synchronous blocking | Async / parallel execution | I/O-bound work |
| Full recomputation | Incremental update | Small mutations to large datasets |
Show full SKILL.md (327 more words)Show less
Phase 4: Confirm the Improvement
Re-run the identical measurement. Compare the numbers.
Baseline: API response 920ms
After optimization: API response 145ms
Improvement: 84% reduction
Required threshold: < 500ms -- ACHIEVEDIf the improvement is not measurable, revert the change. An optimization that cannot be measured is not an optimization.
Anti-Patterns to Avoid
| Anti-Pattern | Why It Fails | Better Approach |
|---|---|---|
| Premature caching | Adds complexity and stale-data risks | Optimize the query first |
| Premature indexing | Indexes degrade write throughput and consume storage | Add only when a query is demonstrably slow |
| Micro-optimizing tight loops | Saves nanoseconds, destroys readability | Profile first; only touch what the profiler flags |
| "Async all the things" | Adds cognitive complexity, harder debugging | Apply async only to I/O-bound operations |
| Optimizing in dev environment | Dev performance diverges from production | Profile in a production-like environment |
| Caching without invalidation | Stale data, consistency bugs | Design invalidation strategy before adding cache |
Cognitive Traps
| Rationalization | Truth |
|---|---|
| "This will be slow at scale" | Is it slow NOW? Optimize when evidence arrives. |
| "Best practice says to add an index" | Is the query actually slow? Indexes impose write overhead. |
| "Caching will speed everything up" | Have you measured what is actually slow? Caching adds complexity. |
| "Async will make this faster" | Is this I/O-bound? Async adds mental overhead for no gain on CPU-bound work. |
| "I know where the bottleneck is" | Profilers exist because human intuition about performance is unreliable. Measure. |
| "Quick optimization while I am in here" | Unplanned optimizations are premature by definition. |
Guardrails -- HALT and Measure
- Optimizing without profiler output on hand
- "While I am here, let me tune this..."
- Adding a cache without measuring what is slow
- Optimizing code that runs once (startup routines, one-time migrations)
- Sacrificing readability for unmeasured performance gains
- Solving scaling problems that do not yet exist
- Applying multiple optimizations simultaneously (isolate impact per change)
Every item on this list means: HALT. Measure first. Optimize only the measured bottleneck.
Integration
Complementary skills:
- godmode:system-design -- Architectural choices that influence performance characteristics
- godmode:completion-gate -- Validate optimization with measurements
- godmode:quality-enforcement -- Performance budgets as automated quality gates
The Bottom Line
Measure -> Pinpoint bottleneck -> Fix that one thing -> Confirm improvementEverything else is speculation. Speculation about performance is always wrong.
© NoobyGains, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in skills/performance-tuning of NoobyGains/godmode.
Open the folder on GitHubat commit 441103a
Compare with similar skills
Performance Tuning 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 |
|---|---|---|---|---|---|---|
| Performance Tuning this skillNoobyGains/godmode | 107 | — | ~2.3k | Automated safety check: Pass | MIT | |
| Jpa Patternsdecebals/claude-code-java | 751 | 1 repos | ~4k | Automated safety check: Pass | MIT | |
| Performance OptimizationHack23/cia | 239 | — | ~1.9k | Automated safety check: Pass | Apache-2.0 | |
| Performanceericrisco/rsc-harness | 167 | — | ~4k | Automated safety check: Pass | MIT | |
| Doctrine Fetch Modesdev-toolings/superpowers-symfony | 222 | — | ~560 | Automated safety check: Notes | MIT | |
| SQL Optimization Patternsynulihao/AgentSkillOS | 617 | 11 repos | ~3.3k | Automated safety check: Pass | None |
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Questions about Performance Tuning
What does Performance Tuning do?
A skill your agent uses when performance is a concern - sluggish pages, slow queries, bloated bundles, high-latency APIs, or whenever someone says "optimize" or "make it faster". Performance Tuning is an agent skill from NoobyGains/godmode.
When should I use Performance Tuning?
Performance Tuning fits situations like: performance is a concern - sluggish pages; bloated bundles; high-latency APIs; whenever someone says optimize.
How do I install Performance Tuning in Claude Code?
Run `npx skills add NoobyGains/godmode --skill performance-tuning -a claude-code`. Or copy the skill folder (skills/performance-tuning in NoobyGains/godmode) into .claude/skills/performance-tuning in your project. Claude Code loads it when a task matches its description.
How do I install Performance Tuning in Codex?
Run `npx skills add NoobyGains/godmode --skill performance-tuning -a codex`. Or copy the skill folder (skills/performance-tuning in NoobyGains/godmode) into .agents/skills/performance-tuning in your project. Codex loads it when a task matches its description.
Can I use Performance Tuning 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 NoobyGains/godmode --skill performance-tuning -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/performance-tuning, .gemini/skills/performance-tuning, .github/skills/performance-tuning and .opencode/skills/performance-tuning in your project.
What does Performance Tuning need to run?
Going by SKILL.md and its folder, Performance Tuning needs the command-line tools its instructions call (curl).
Does Performance Tuning access the network?
SKILL.md contains no URLs. Its commands use curl, which can reach the network depending on how they are called. This is read from the text; nothing was executed.
Is Performance Tuning safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Performance Tuning use?
Performance Tuning is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Performance Tuning use?
About 2.3k tokens (SKILL.md is roughly 9.1k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Performance Tuning?
Skills that share tags, products or a category with Performance Tuning: Jpa Patterns (decebals/claude-code-java, 751 stars), Performance Optimization (Hack23/cia, 239 stars), Performance (ericrisco/rsc-harness, 167 stars) and Doctrine Fetch Modes (dev-toolings/superpowers-symfony, 222 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Performance Tuning?
NoobyGains (a GitHub user) maintains it in NoobyGains/godmode, which has 107 GitHub stars. The repository holds 34 skills in this directory. The repository was last updated on March 9, 2026.
Source: NoobyGains/godmode on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.