lift

name

lift

description

Uber performance optimization skill: measurement-driven latency, throughput, memory/GC, tail-behavior, algorithmic, systems, and micro-architectural optimization with profile evidence, score-gated experiments, behavior proofs, golden-output oracles, and regression guards. Use when the user asks to optimize, speed up, reduce p95/p99 latency, increase throughput/QPS, lower CPU, memory, allocations, GC pauses, syscalls, round trips, or asks for profiling, bottleneck analysis, algorithmic improvement, or a benchmarked perf pass. If a runnable workload is unavailable, operate in explicitly labelled UNMEASURED mode with exact benchmark/profiling/proof commands. Zig-only CLI iteration for bench_stats/perf_report must be proven before shipping.

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 <primary metric> on <workload> versus baseline; report delta and do not regress <correctness + secondary metrics>.

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

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:

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:

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.

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
<change>	<profile/trace/counter>					accept/reject

Behavior Proof Gate

For every accepted change, document an isomorphism proof before claiming success. Use references/behavior-proof.md for full guidance.

## Behavior proof: <change>
- 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

Zig CLI Iteration Repos

When iterating on the Zig-backed bench_stats / perf_report helper CLI path, use these two repos:

• skills-zig ($HOME/workspace/tk/skills-zig): source for bench_stats and perf_report, build/test wiring, and release tags.
• homebrew-tap ($HOME/workspace/tk/homebrew-tap): Homebrew formula updates and checksum bumps for released lift binaries.

For Lift-owned CLIs, prove marker compatibility before use:

command -v bench_stats && bench_stats --help 2>&1 | grep -q bench_stats.zig
command -v perf_report && perf_report --help 2>&1 | grep -q perf_report.zig
bench_stats --input samples.txt --unit ms
perf_report --title "Perf pass" --owner "team" --system "service" --output /tmp/perf-report.md

Brew-aware Launcher Pattern

run_lift_tool() {
  local subcommand="${1:-}"
  if [ -z "$subcommand" ]; then
    echo "usage: run_lift_tool <bench-stats|perf-report> [args...]" >&2
    return 2
  fi
  shift || true

  local bin="" marker=""
  case "$subcommand" in
    bench-stats) bin="bench_stats"; marker="bench_stats.zig" ;;
    perf-report) bin="perf_report"; marker="perf_report.zig" ;;
    *) echo "unknown lift subcommand: $subcommand" >&2; return 2 ;;
  esac

  install_lift_direct() {
    local repo="${SKILLS_ZIG_REPO:-$HOME/workspace/tk/skills-zig}"
    if ! command -v zig >/dev/null 2>&1; then
      echo "zig not found. Install Zig and retry." >&2
      return 1
    fi
    if [ ! -d "$repo" ]; then
      echo "skills-zig repo not found at $repo." >&2
      echo "clone it with: git clone https://github.com/tkersey/skills-zig \"$repo\"" >&2
      return 1
    fi
    (cd "$repo" && zig build -Doptimize=ReleaseSafe) || return 1
    [ -x "$repo/zig-out/bin/$bin" ] || return 1
    mkdir -p "$HOME/.local/bin"
    install -m 0755 "$repo/zig-out/bin/$bin" "$HOME/.local/bin/$bin"
  }

  if command -v "$bin" >/dev/null 2>&1 && "$bin" --help 2>&1 | grep -q "$marker"; then
    "$bin" "$@"
    return
  fi

  if [ "$(uname -s)" = "Darwin" ]; then
    command -v brew >/dev/null 2>&1 || { echo "homebrew is required on macOS" >&2; return 1; }
    brew install tkersey/tap/lift || return 1
  else
    install_lift_direct || return 1
  fi

  if command -v "$bin" >/dev/null 2>&1 && "$bin" --help 2>&1 | grep -q "$marker"; then
    "$bin" "$@"
    return
  fi

  echo "missing compatible $bin binary after install attempt" >&2
  return 1
}

Deliverable Format (Chat)

If unmeasured, prefix the response with UNMEASURED: and fill the sections with an exact measurement/profiling/proof plan. Do not claim deltas.

Output these sections, numbers first:

Performance contract

• Metric + percentile:
• Workload command:
• Dataset:
• Environment:
• Constraints:

Baseline

• Samples + warmup:
• Results:
• Noise floor / variance:

Bottleneck evidence

• Tool + artifact:
• Hot path / contention / queue:
• Bound classification:

Opportunity matrix

• Candidate -> score -> decision:

Behavior proof

• Oracle:
• Invariants:
• Golden/differential/property check:

Experiments

• Hypothesis -> change -> measured delta -> decision:

Result

• Variant results:
• Delta vs baseline:
• Confidence:
• Trade-offs / regressions checked:

Regression guard

• Benchmark/budget/monitor:
• Threshold:

Validation

• Correctness command(s) -> pass/fail:
• Performance command(s) -> numbers:
• CLI proof if applicable:

Residual risks / next steps

lift_compliance: mode=<measured|unmeasured|audit>; workload=<cmd>; baseline=<yes/no>; after=<yes/no>; correctness=<yes/no>; bottleneck_evidence=<yes/no>; behavior_proof=<yes/no>; score_gate=<yes/no>

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.