# Lift > Performance optimization with measurement-driven latency, throughput, memory/GC, tail, algorithmic, systems, and micro-architectural work; profile evidence, score-gated experiments, behavior proofs, golden oracles, and regression guards. Use for optimize, speed up, reduce p95/p99, increase throughput/QPS, lower CPU/memory/allocations/GC/syscalls/round trips, profiling, bottlenecks, algorithmic improvement, or benchmarked perf passes. Without a runnable workload, operate in labelled UNMEASURED mode with exact benchmark/profiling/proof commands. Prove Zig-only bench_stats/perf_report CLI iteration before shipping. - Skill: `tkersey/lift` (Agent Skill, multi-file: 21 files) - Install (CLI): `npx skillmds@latest add tkersey/lift` - Raw SKILL.md: https://api.skillmd.com/api/skills/tkersey/lift/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: tkersey (https://skillmd.com/u/tkersey) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tkersey/lift --- # Lift ## Intent Deliver aggressive performance improvements while preserving behavior, safety, determinism, and maintainability. Lift is the umbrella optimization skill for product workloads, service latency, batch/offline throughput, memory pressure, tail behavior, algorithmic complexity, data layout, concurrency, I/O, and runtime or compiler tuning. ## Prime Directive Profile first. Prove behavior unchanged. Change one lever at a time. Measure before and after on the same workload. Ship only with a regression guard. Every optimization pass must produce evidence for five questions: 1. What is the performance contract? 2. What does the baseline show? 3. What bottleneck did profiling identify? 4. Why is the proposed change behavior-preserving? 5. What measured delta and guard justify shipping? ## Double Diamond fit Lift lives in Define -> Deliver. - Define: write a performance contract, select a proof workload, and choose a correctness oracle. - Deliver: baseline, profile, score opportunities, run tight experiments, prove equivalence, verify the result, and install a guard. ## Hard Rules - Measure before and after every optimization: numbers, environment, command, workload, dataset, and sample count. - Optimize the current bottleneck, not the loudest hunch. Use a profiler, trace, counter, or workload-specific observation. - Require a correctness signal before and after. Never accept a perf win with a failing correctness gate. - Preserve semantics unless the user explicitly approves a semantic trade-off. - Change one lever per experiment and keep diffs reversible. - Reject wins smaller than the noise floor unless the result is explicitly labelled inconclusive. - Track second-order regressions: memory, tail latency, CPU, I/O, lock waits, cache size, and external cost. - Stop and ask before raising resource or cost ceilings, unless the user asked for that trade-off. - If no runnable proof workload exists, prefix the response with `UNMEASURED:` and provide exact commands. Do not claim wins. - For Lift-owned CLIs, use Zig binaries only (`bench_stats`, `perf_report`) and prove compatibility with marker checks before use. - After any Zig CLI contract change, update docs and release/tap propagation in the same pass so install guidance matches runtime behavior. ## Mode Selection Use measured mode whenever a proof workload can run. - **Measured mode:** run baseline and variant on the same workload. Include raw sample count, percentiles or throughput, profile evidence, correctness proof, and regression guard. - **Unmeasured mode:** start with `UNMEASURED:`. Provide hypotheses and the exact commands that would generate baseline, profile, correctness, and after data. - **Audit mode:** when the user only asks for a review, produce a ranked opportunity matrix and proof plan, but mark untested items as hypotheses. ## Contract Derivation If the user did not provide a numeric target, define the contract as: > Improve `` on `` versus baseline; report delta and do > not regress ``. Default primary metric: - Request-like/service code: latency p95; also report p50, p99, max, throughput, CPU, and memory when feasible. - Batch/offline code: throughput or wall-clock duration; also report CPU%, peak RSS, and I/O volume. - Memory/GC issue: peak RSS, allocation rate, and GC pause; also report latency or throughput. - Startup/cold path: cold-start wall time; separately measure steady-state. - Tail problem: p99/max and variance drivers; treat variance reduction as the primary goal. ## Workload Selection Pick the first representative runnable proof workload available: 1. User-provided reproduction command or production-like workload. 2. Existing repo benchmark, test harness, Makefile/justfile/taskfile, CI job, or README workflow. 3. A minimal harness around the hot path, paired with correctness checks. 4. If none can be created without product ambiguity, operate in `UNMEASURED` mode and specify the missing workload requirements. ## Mandatory Optimization Loop ```text 0. PREFLIGHT -> environment, workload, correctness oracle, warmup sanity 1. BASELINE -> repeated samples, p50/p95/p99/max or throughput/RSS/allocs 2. PROFILE -> CPU, allocation, I/O, lock, queue, or tail evidence 3. PROVE -> golden outputs, invariants, property tests, or differential run 4. SCORE -> opportunity matrix: Impact x Confidence / Effort 5. IMPLEMENT -> one lever only, smallest reversible diff 6. VERIFY -> correctness gate, golden checksum/diff, benchmark rerun 7. REPROFILE -> confirm bottleneck moved or score next opportunity 8. GUARD -> benchmark budget, CI gate, monitor, or perf report ``` Default benchmark examples: ```bash hyperfine --warmup 3 --runs 10 'command' hyperfine --warmup 3 --runs 30 --export-json baseline.json 'command' /usr/bin/time -v command 2>&1 | tee time.txt ``` Default behavior oracle examples: ```bash mkdir -p golden_outputs for input in test_inputs/*; do ./program "$input" > "golden_outputs/$(basename "$input").out"; done sha256sum golden_outputs/* > golden_checksums.txt sha256sum -c golden_checksums.txt ``` ## Opportunity Matrix Gate Only implement a candidate when the score is at least 2.0, unless the user explicitly requests exploratory work. ```text Score = (Impact x Confidence) / Effort Impact: 1=<5%, 2=5-10%, 3=10-25%, 4=25-50%, 5=>50% Confidence: 1=speculative, 3=plausible, 5=profile-confirmed Effort: 1=minutes, 3=hours, 5=>1 day or high complexity ``` | Opportunity | Hotspot evidence | Impact | Confidence | Effort | Score | Decision | |---|---:|---:|---:|---:|---:|---| | `` | `` | | | | | accept/reject | ## Behavior Proof Gate For every accepted change, document an isomorphism proof before claiming success. Use `references/behavior-proof.md` for full guidance. ```markdown ## Behavior proof: - Inputs covered: - Old behavior: - New behavior: - Ordering preserved: - Tie-breaking unchanged: - Floating-point semantics: - RNG/time/concurrency determinism: - Error handling and edge cases: - Golden outputs / differential check: - Correctness command(s): ``` Common proof obligations: - Batching: same operations, same effective order or explicitly stable reorder. - Hash/index lookup: same key equivalence, same missing-key behavior, order preserved if observable. - Memoization: function is pure for cache key, invalidation is correct, bounds are safe. - Parallelization: operation is associative/commutative or merge order is stable; no data races. - Approximation: bounded error is explicitly accepted by the user or product contract. ## Optimization Ladder Move down only after higher-leverage tiers are exhausted. 1. Delete work: skip unused computation, redundant parsing, duplicate I/O. 2. Change the algorithm: reduce complexity class or exploit monotonicity. 3. Change data structures/layout: indexes, maps, heaps, SoA, contiguous buffers. 4. Improve memory behavior: preallocation, pooling, arenas, allocation removal. 5. Improve concurrency: shard, pipeline, batch, reduce contention, bound queues. 6. Reduce I/O/serialization: fewer bytes, syscalls, round trips, and copies. 7. Improve tail behavior: backpressure, timeouts, cancellation, variance control. 8. Tune micro-architecture: branch predictability, SIMD, cache lines, prefetch. 9. Tune compiler/runtime: PGO/LTO/JIT warmup/GC flags/inlining. ## Round Escalation - **Round 0:** Measurement hygiene. Stabilize benchmark and correctness oracle. - **Round 1:** Standard wins: N+1 elimination, batching, indexing, memoization, preallocation, cache bounds, JSON/serialization cleanup, log formatting removal. - **Round 2:** Algorithmic and architectural wins: DP, graph reductions, streaming, partitioning, lock sharding, layout rewrites, queue/admission fixes. - **Round 3:** Advanced/exotic wins: convex/semiring recasts, FFT/NTT, suffix arrays, sketches, cache-oblivious recursion, meet-in-the-middle, specialized indexes, PGO/LTO/SIMD. Each round starts with a fresh profile because bottlenecks shift. ## Fast Pattern Tiers | Tier | Pattern | When | Proof concern | |---|---|---|---| | 1 | N+1 -> batch | Sequential external calls | Result ordering and retry semantics | | 1 | Linear scan -> index/hash | Repeated keyed lookup | Key equality and observable order | | 1 | Memoization | Repeated pure computation | Cache key, invalidation, bounds | | 1 | Buffer/prealloc reuse | Allocation in hot loop | Aliasing and lifetime safety | | 2 | Binary search/two-pointer | Sorted or monotone data | Precondition validation | | 2 | Prefix sums/sliding window | Repeated range queries | Static data or update semantics | | 2 | Priority queue/top-k | Scheduling or ranking | Tie-breaking and stability | | 3 | Arena/pool/SmallVec/SoA | Allocation or locality bound | Lifetime, ownership, memory cap | | 3 | Bloom/sketch/HLL | Membership/counting at scale | Error bound and acceptance | | 3 | Lock sharding/queues | Contention/tail bound | Races, fairness, backpressure | ## Language Triage Cheatsheet | Ecosystem | First profiler | Allocation/memory | Fast grep signals | |---|---|---|---| | Rust/Zig/C/C++ | `perf`, flamegraph, Instruments | `heaptrack`, DHAT, massif | clones/copies, boxes, formatting, allocs | | Go | `go tool pprof`, `go tool trace` | heap/alloc profiles, `GODEBUG=gctrace=1` | `interface{}`, `defer` in loops, `fmt.Sprintf` | | Node/TypeScript | `clinic flame`, `node --prof` | DevTools heap, event-loop delay | JSON parse/stringify, sync fs, await-in-loop | | Python | `py-spy`, `cProfile`, `scalene` | `memory_profiler`, `tracemalloc` | `iterrows`, string `+=`, list membership | | JVM | JFR, async-profiler | allocation/lock events, GC logs | boxing, reflection, synchronized hot path | ## Lift-owned CLI tools When using or changing `bench_stats` or `perf_report`, read [cli-tools.md](references/cli-tools.md) for the Zig marker compatibility checks, source repositories, and launcher. Reuse compatible installed tools; installation requires existing provisioning authority. ## Deliverable Format (Chat) Lead with the measured result and comparison baseline. Include the workload and sample count, bottleneck evidence, change, correctness and regression checks, and material uncertainty or trade-offs. Scale detail to the task; use the existing [report template](assets/perf-report-template.md) for a requested full report. Keep evidence needed to assess the performance claim; do not repeat it in a mandatory compliance footer. If unmeasured, prefix the response with `UNMEASURED:` and give the exact measurement, profiling, and proof commands. Do not claim deltas. ## Core References (Load on Demand) - `references/playbook.md` — master flow, doctrine, and loop. - `references/measurement.md` — benchmarking, statistics, noise, and reporting. - `references/profiling-tools.md` — tool matrix and evidence artifacts. - `references/behavior-proof.md` — golden outputs, invariants, isomorphism proof. - `references/opportunity-matrix.md` — impact/confidence/effort score gate. - `references/optimization-tactics.md` — tactical catalog by layer. - `references/algorithms-and-data-structures.md` — algorithmic and structural levers. - `references/systems-and-architecture.md` — CPU, memory, OS, network tactics. - `references/latency-throughput-tail.md` — queueing, variance, and backpressure. - `references/language-specific.md` — ecosystem-specific profilers and red flags. - `references/advanced-techniques.md` — round-2/round-3 advanced patterns. - `references/checklists.md` — fast triage and validation checklists. - `references/anti-patterns.md` — traps to reject. ## Assets - `assets/perf-report-template.md` — ready-to-edit measured or unmeasured report. - `assets/experiment-log-template.md` — one-variable experiment ledger. - `assets/isomorphism-proof-template.md` — per-change behavior proof. - `assets/opportunity-matrix-template.md` — score-gated opportunity table. - `assets/golden-output-manifest.md` — golden-output capture checklist.