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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.
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Zipをダウンロード ダウンロード中... このリポジトリの他の Skills Manage local Codex account switching with a metadata-only TOML config, safe auth.json vault backups, pending manual account activation, and weekly reset-cycle rotation. Use when the user asks to manage Codex accounts, switch Codex accounts, inspect account status, or rotate through accounts after weekly limits reset.
Turn accepted intent and review evidence into correct-by-construction software through Goal Contracts, Counterexample Sets, Construction Contracts, and an Evidence Ledger. Use bare $actuating for implementation, Ship publication, and review convergence; use explicit implement, triage, remediation-plan, or review-closeout for their bounded routes. Begin every architecture or abstraction decision with OPERATE ARCHITECTONICALLY, use $first-principles to establish the incumbent-independent Construction basis before $universalist nomination, and integrate one bounded $metanoetic pass when an initial high-regret nomination, accepted findings, causal recurrence, or cumulative review-path accretion makes abstraction change live. Before fresh review after repair, fold the cumulative changeset and review-induced delta so pointwise minimal repairs cannot compose into a dominated final Construction. Actuating alone selects the Construction and next action; Ledger is non-executing and Ship alone owns public effects.
Use the native CAS Codex control-plane CLI for app-server inspection and transport, account and goal facts, automations, smoke and conformance checks, instance fanout, tuple-bound reviews, and session inquiry. CAS owns execution and directly observed facts; callers retain semantic authority.
name lift description 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.
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:
What is the performance contract?
What does the baseline show?
What bottleneck did profiling identify?
Why is the proposed change behavior-preserving?
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>.
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:
User-provided reproduction command or production-like workload.
Existing repo benchmark, test harness, Makefile/justfile/taskfile, CI job, or
README workflow.
A minimal harness around the hot path, paired with correctness checks.
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.
Delete work: skip unused computation, redundant parsing, duplicate I/O.
Change the algorithm: reduce complexity class or exploit monotonicity.
Change data structures/layout: indexes, maps, heaps, SoA, contiguous buffers.
Improve memory behavior: preallocation, pooling, arenas, allocation removal.
Improve concurrency: shard, pipeline, batch, reduce contention, bound queues.
Reduce I/O/serialization: fewer bytes, syscalls, round trips, and copies.
Improve tail behavior: backpressure, timeouts, cancellation, variance control.
Tune micro-architecture: branch predictability, SIMD, cache lines, prefetch.
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, Instrumentsheaptrack, DHAT, massifclones/copies, boxes, formatting, allocs Go go tool pprof, go tool traceheap/alloc profiles, GODEBUG=gctrace=1 interface{}, defer in loops, fmt.SprintfNode/TypeScript clinic flame, node --profDevTools heap, event-loop delay JSON parse/stringify, sync fs, await-in-loop Python py-spy, cProfile, scalenememory_profiler, tracemallociterrows, string +=, list membershipJVM 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:
Metric + percentile:
Workload command:
Dataset:
Environment:
Constraints:
Samples + warmup:
Results:
Noise floor / variance:
Tool + artifact:
Hot path / contention / queue:
Bound classification:
Candidate -> score -> decision:
Oracle:
Invariants:
Golden/differential/property check:
Hypothesis -> change -> measured delta -> decision:
Variant results:
Delta vs baseline:
Confidence:
Trade-offs / regressions checked:
Benchmark/budget/monitor:
Threshold:
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. 