.codex/skills/performance-analyzer/SKILL.md
Analyze Rust code for performance issues, allocation hot spots, and optimization opportunities
npx skillsauth add ahrav/gossip-rs performance-analyzerInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Analyze performance-critical code in the gossip-rs workspace (coordination, scanner engine, data structures).
crates/scanner-engine/src/engine/ modulescrates/gossip-stdx/src/with_capacity() for known-size collections#[inline(always)] on hot functionsstd::mem::size_of)Arc when Rc or ownership would workget_unchecked with proof)&str vs String in function signaturesThis codebase uses these performance patterns:
NONE_U32 = u32::MAX as sentinel (avoid Option overhead)#[inline(always)] on hot-path functionsdebug_assert! for invariant checks (zero cost in release)G) for compile-time granularity selection## Performance Analysis: [file/function]
### Findings
| Severity | Issue | Location | Impact |
|----------|-------|----------|--------|
| HIGH | Allocation in hot loop | line:XX | ~Xns per call |
| MEDIUM | Missing inline hint | line:XX | Potential call overhead |
### Recommendations
1. **[Issue]**: [Specific fix with code example]
```rust
// Before
// After
Run these benchmarks to verify:
cargo bench --bench <relevant_bench>
## Related Skills
- `/bench-compare` - Before/after measurement
- `/asm-forge` - ASM-guided optimization
- `/perf-regression` - Full regression workflow
development
Deep first-principles code explanation that builds real understanding through phased walkthroughs with diagrams. Covers algorithms, data structures, memory layout, concurrency patterns, and performance tricks — especially for systems code in Rust. Use whenever the user asks to explain, walk through, break down, deep dive into, or understand code. Trigger on "how does this work", "what's happening here", "teach me about this", "why is it done this way", or when the user references a file with @ and wants to understand it. Proactively use when examining code involving lock-free algorithms, atomics/CAS, memory ordering,
development
Use when creating implementation-ready beads tasks that need testing strategy, optimal implementation approach, and documentation requirements baked in — composes /create-task with parallel enrichment agents that analyze the codebase and produce concrete test specifications, algorithm/data-structure guidance, and doc quality standards so implementing agents don't need to re-research
development
--- name: autoresearch description: Autonomous Goal-directed Iteration. Apply Karpathy's autoresearch principles to ANY task. Loops autonomously — modify, verify, keep/discard, repeat. Supports bounded iteration via Iterations: N inline config. version: 1.9.11 --- # Claude Autoresearch — Autonomous Goal-directed Iteration Inspired by [Karpathy's autoresearch](https://github.com/karpathy/autoresearch). Applies constraint-driven autonomous iteration to ANY work — not just ML research. **Core id
development
Use when implementing a new feature and assessing coverage gaps, during periodic test hygiene, when test suites feel bloated, or before merging code that changes coordination or hot paths. Two-phase assess-then-improve testing pipeline.