--- name: optimization-fe0427bb421c description: Use when improving performance, latency, throughput, memory usage, or general efficiency. Start by defining target metrics, measuring comprehensively, attributing bottlenecks, validating with static analysis, and prioritizing macro-optimizations before micro-optimizations. allowed-tools: bash, read, write, grep, agentgrep, batch, todo --- # Optimization Use this skill when the task is about making a system faster, lighter, more scalable, or otherwise more efficient. ## Core principle To optimize properly, you must know: 1. **What metrics you are chasing** 2. **What your real bottlenecks are** Do not optimize blindly. ## 1. Define the target metrics first Before changing code, make sure you have the right measurements. - Identify the exact metrics that matter: latency, throughput, memory, CPU, startup time, compile time, query count, token usage, cost, etc. - Measure **comprehensively**, not just a convenient subset. - Make sure the metrics are accurate and representative of the real workload. - Prefer measurements that are fast to run so you can iterate quickly. - If possible, create repeatable benchmarks or scripts so improvements are verifiable. ## 2. Get full bottleneck attribution You should have strong attribution for what each part of the system is doing. - Instrument the system so you can see where time and resources are going. - Prefer both: - **Ad hoc inspection** for quick debugging - **Logged measurements** for later analysis and comparison - Attribute work across the full path, not just the obviously slow component. - Make sure the data is detailed enough to explain where the cost comes from. If you can analyze runs after the fact with logs or traces, that is often much more powerful than relying only on live inspection. ## 3. Use static analysis too Not every optimization problem needs runtime profiling first. Often, code inspection reveals the issue. Check for: - Wrong asymptotic complexity - The wrong algorithm or data structure - Unnecessary repeated work - Work happening in the wrong layer - Inefficient architecture or control flow - Directionally incorrect approaches Make sure your asymptotics are right and the overall algorithm makes sense before tuning small details. ## 4. Macro-optimize before micro-optimizing Prioritize the largest wins first. - Remove whole classes of work before making existing work slightly cheaper. - Fix architecture, batching, caching, query patterns, algorithm choice, parallelism, and data movement before focusing on tiny low-level tweaks. - If you are very far from the expected metrics, spend more time on macro-optimization. Micro-optimizations matter most after the major inefficiencies are already addressed. ## Recommended workflow 1. Define success metrics. 2. Reproduce the current baseline. 3. Add measurement and attribution if missing. 4. Identify the top bottleneck. 5. Check for algorithmic or architectural issues. 6. Apply the highest-leverage fix first. 7. Re-measure. 8. Repeat until the target is met or tradeoffs stop being worth it. ## Guardrails - Do not claim an optimization without before/after evidence. - Be careful not to optimize the wrong metric. - Watch for regressions in correctness, reliability, maintainability, and security. - Prefer changes that are measurable, explainable, and reversible. --- Source attribution: 1jehuang Original project: https://github.com/1jehuang/jcode License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice MIT License Copyright (c) 2026 Gabriel Moreira Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Mega AI Hub resource metadata Creator / publisher: 1jehuang Original project: https://github.com/1jehuang/jcode Attribution basis: Upstream repository explicitly recorded in package mirror path Fields of work: Data & Analytics, Development & Engineering Roles: Backend Developer, Data Analyst, Data Scientist, DevOps / SRE, ML / AI Engineer Specialties: API, Backend, Cross Platform Tools: Agentgrep, Bash, Batch, Grep, Read, Todo, Write Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 950da402c4567bd523ea567971ed0f033630dcc488f796704ede6142db146d7d