Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add decebals/claude-code-java --skill performance-smell-detectiongit clone --depth 1 https://github.com/decebals/claude-code-javaWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/decebals/claude-code-java/performance-smell-detection)<a href="https://agentmods.dev/skills/decebals/claude-code-java/performance-smell-detection"><img src="https://agentmods.dev/badge/skills/decebals/claude-code-java/performance-smell-detection/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/decebals/claude-code-java/performance-smell-detection"><img src="https://agentmods.dev/badge/skills/decebals/claude-code-java/performance-smell-detection.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 311 Skill allows unbounded resource consumption (API calls, storage, compute). Without rate limits or quotas, a compromised or misbehaving agent can cause denial-of-service or cost overruns.Fix: Set explicit rate limits, timeouts, and resource quotas for API calls, file operations, and compute. Implement circuit breakers for runaway loops.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00051 | $0.02303 |
| Opus 5.5 | $0.00020 | $0.00921 |
| Sonnet 5.5 | $0.00010 | $0.00461 |
| Haiku 4.5 | $0.00005 | $0.00230 |
Grade A, and why
performance-smell-detection scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 351 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Performance Smell Detection Skill
Identify potential code-level performance issues in Java code.
Philosophy
"Premature optimization is the root of all evil" - Donald Knuth
This skill helps you notice potential performance smells, not blindly "fix" them. Modern JVMs (Java 21/25) are highly optimized. Always:
- Measure first - Use JMH, profilers, or production metrics
- Focus on hot paths - 90% of time spent in 10% of code
- Consider readability - Clear code often matters more than micro-optimizations
When to Use
- Reviewing performance-critical code paths
- Investigating measured performance issues
- Learning about Java performance patterns
- Code review with performance awareness
Scope
This skill: Code-level performance (streams, collections, objects)
For database: Use jpa-patterns skill (N+1, lazy loading, pagination)
For architecture: Use architecture-review skill
Quick Reference: Potential Smells
| Smell | Severity | Context |
|---|---|---|
| Regex compile in loop | 🔴 High | Always worth fixing |
| String concat in loop | 🟡 Medium | Still valid in Java 21/25 |
| Stream in tight loop | 🟡 Medium | Depends on collection size |
| Boxing in hot path | 🟡 Medium | Measure first |
| Unbounded collection | 🔴 High | Memory risk |
| Missing collection capacity | 🟢 Low | Minor, measure if critical |
String Operations (Java 9+ / 21 / 25)
What Changed
Since Java 9 (JEP 280), string concatenation with + uses invokedynamic, not StringBuilder. The JVM optimizes simple concatenation well.
Java 25 adds String::hashCode constant folding for additional optimization in Map lookups with String keys.
Still Valid: StringBuilder in Loops
// 🔴 Still problematic - new String each iteration
String result = "";
for (String s : items) {
result += s; // O(n²) - creates n strings
}
// ✅ StringBuilder for loops
StringBuilder sb = new StringBuilder();
for (String s : items) {
sb.append(s);
}
String result = sb.toString();
// ✅ Or use String.join / Collectors.joining
String result = String.join("", items);
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 351 lines · 51 tokens per session scan A a43c8936af13
performance-smell-detection is a skill published in the GitHub repository decebals/claude-code-java (754 stars, last pushed 1mo ago), licensed MIT. It adds 51 tokens to every session and 2,303 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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