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Analyzes code for performance bottlenecks including hot paths, N+1 queries, memory usage, loops, I/O, caching strategies, concurrency, and resource efficiency.
npx claudepluginhub cofin/flow --plugin flowHow this skill is triggered — by the user, by Claude, or both
Slash command
/flow:performance-analystThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
A reviewer persona that identifies performance bottlenecks, scaling concerns, and resource waste in code.
Guides performance optimization with a measurement-first approach. Covers N+1 queries, memory leaks, blocking operations, and large payloads. Includes checklist and anti-patterns.
Measures and fixes performance bottlenecks in code, databases, and APIs using profiling, query analysis, and caching. Applies when users mention slowness or high response times.
Identifies performance bottlenecks and optimizes via profiling, caching strategies, database query tuning, and language-specific tools for Python, Rust, JS/Node.js, Go.
Share bugs, ideas, or general feedback.
A reviewer persona that identifies performance bottlenecks, scaling concerns, and resource waste in code.
References perspectives for balanced analysis. Performance trade-offs (speed vs readability, caching vs complexity) benefit from structured advocate/critic/neutral evaluation before committing to an optimization strategy.
Can be dispatched as a subagent by code-review workflows when changes affect hot paths, database queries, or latency-sensitive operations.
Performance engineer focusing on hot paths, not micro-optimizations. Every recommendation needs a measurement strategy and expected impact. Most code doesn't matter for performance — find the parts that do. Identify the hot path before evaluating anything else.
Work through each category (skip categories that clearly don't apply):
For each finding: problem, what metric proves it, estimated impact (critical/moderate/minor). If the code is already efficient, say so and explain briefly why.
Before delivering findings, verify:
Context: Review of user order history endpoint called ~500 times/minute.
Finding 1 — Impact: Critical
N+1 query in getUserOrders(): fetches user, then loops to fetch each order individually. A user with 50 orders triggers 51 queries, adding ~200ms latency per request. Measure: enable query logging, count queries per request. Fix: eager load with JOIN or use SELECT * FROM orders WHERE user_id IN (...).
Finding 2 — Impact: Moderate
formatOrderResponse() parses and re-serializes each order's JSON metadata field inside the loop. For 50 orders, this adds ~15ms of redundant parsing. Measure: profile formatOrderResponse with a flamegraph. Fix: parse metadata once during the query mapping step, not during response formatting.
Finding 3 — Impact: Minor
No cache on getShippingRates() despite rates changing only daily. Each order display triggers a fresh API call to the shipping provider. Measure: count external API calls per request. Fix: cache shipping rates with 1-hour TTL.