Slate v2 performance lane for Codex Autoresearch. Delegates generic loop mechanics to slate-ar/codex-autoresearch and adds target registry, fastest-safe stop rules, exactness gates, and pagination/virtualization defaults.
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Slate v2 performance lane for Codex Autoresearch. Delegates generic loop mechanics to slate-ar/codex-autoresearch and adds target registry, fastest-safe stop rules, exactness gates, and pagination/virtualization defaults.
Use this for Slate v2 performance work where "try another optimization" needs a
measured loop, not another plan essay. This is the performance lane on top of
slate-ar, which wraps codex-autoresearch:codex-autoresearch.
Do not duplicate generic packet/dashboard/finalization mechanics here. Load
slate-ar for Slate wrapper behavior and codex-autoresearch:codex-autoresearch
for the underlying Autoresearch state machine.
Use When
The user invokes slate-ar-perf.
The user says fast, fastest, max perf, ,
, , or asks to make a Slate v2 surface faster.
pagination
virtualization
benchmark
Pagination, virtualization, huge-document, table, render, layout, selection,
typing, paste, scroll, or mount performance needs iterative optimization.
There is, or should be, a benchmark command that prints METRIC name=value.
The next move depends on measured results, not just architecture judgment.
Do Not Use When
The bug is primarily correctness and needs a direct fix. Use slate-patch.
The target is too vague to infer a benchmark and correctness contract. Use
slate-ar-recipe for target discovery or slate-plan for architecture
framing.
The output is an architecture/API proposal for user review. Use slate-plan.
The target is Plate product code instead of raw Slate v2.
Natural Modes
fast, fastest, max perf, make it fastest: fastest-safe mode. Pick or
resume the matching target and keep running packets until target parity,
plateau, correctness blocker, architecture blocker, unsafe
finalization/dirty-tree boundary, or user interruption.
pagination, virtualization: use the pagination default contract unless the
user gives a sharper target.
continue, resume, status, dashboard, finalize: delegate to
slate-ar operator modes, then apply perf policy to any next packet.
Plateau means three consecutive valid correctness-green packets improve the
primary metric by less than 5% and no safe P0/P1 profiler hypothesis remains.
Do not stop at the first win.
Target Registry
Use benchmarks/targets/slate-v2.json as the migration spine when it exists.
It is the source of truth for benchmark questions, cohorts, commands, metrics,
correctness checks, artifacts, and supporting docs.
Default path:
list targets with pnpm bench:targets:list;
check registry health with pnpm bench:targets:check;
generate or check target reports with pnpm bench:targets:report;
dry-run the target with pnpm bench:targets:dry-run -- <target-id>;
initialize the real .tmp/slate-v2/autoresearch.* session only when needed:
node tooling/scripts/bench-targets.mjs autoresearch-init <target-id>;
use slate-ar / Codex Autoresearch for setup inspection, benchmark lint,
checks inspection, packets, stale-run detection,
ASI, dashboard, keep/discard decisions, and final evidence.
Missing Target Policy
A specific target id is enough instruction. Do not force the user to write a
long prompt like "create target if missing".
If <target-id> is missing from benchmarks/targets/slate-v2.json and the name
is specific enough to infer the surface, create the first-class target contract
in the same pass. Examples of specific-enough names:
react-huge-document-select-all
core-observation-compare
pagination-virtualized-fast-scroll
history-fragment-undo-redo
When creating a target:
add the registry entry before initializing Autoresearch;
reuse or extend the nearest existing benchmark script when possible;
create a small benchmark owner only when no existing command can expose the
metric honestly;
make the benchmark print METRIC lines from the start;
use a primary metric that names the real surface, not generic
benchmark_seconds;
compare against ../slate/../../../slate when legacy parity is the claim;
add a correctness command that covers the native editor behavior at risk;
dry-run the target and run pnpm bench:targets:check before AR init.
If the target name is ambiguous, stop and recommend one or two concrete target
ids. Do not create another slate-ar-* wrapper skill for a missing benchmark
target. slate-ar-perf owns perf-target bootstrapping.
The old Slate v2 bench:* package scripts remain workload owners during
migration. The clean split is: target registry owns the decision contract,
benchmark scripts own runtime workload, Autoresearch owns active optimization
state, and target reports/history own historical status.
Exactness Gate
Performance wins do not count when the editor is less correct.
Before pagination, virtualization, hidden DOM, model-backed selection, or
staged-render optimization:
identify the exact correctness oracle or browser proof command;
if no oracle exists, add it first with slate-patch or tdd;
classify each native behavior as preserved, intentionally degraded, or out of
scope before using it as a benchmark cohort;
keep cold-path estimates as scaffold hints only, not authoritative layout or
selection truth;
if a packet improves speed but breaks selection, input ordering, IME, copy,
paste, undo, focus, or follow-up typing, log checks_failed or discard,
never keep.
Pagination Default Contract
For pagination or page-level virtualization, start from this contract unless the
user gives a sharper one.
For react-huge-document-select-all, create or use a target with this contract.
Required cohorts:
5k blocks against legacy Slate;
the main Slate v2 huge-document React surface;
staged/DOM-present or virtualized surface when that is the product path;
native keyboard select-all (Mod+A), not only programmatic model selection.
Primary metrics, lower is better:
react_huge_doc_select_all_p95_ms;
react_huge_doc_select_all_worst_p95_ratio versus legacy;
react_huge_doc_select_all_failure_count.
Secondary metrics:
DOM node count after select-all;
React commit count when cheap to collect;
selection export/import time when visible separately;
copy latency for the selected document when cheap to collect.
Correctness checks:
Mod+A selects the full editor document;
typing after select-all replaces the selected content once;
undo restores the previous document and selection coherently;
copy returns complete plain text for the selected document;
broad selection stays valid in staged, partial-DOM, or virtualized rendering;
no hidden debounce or delayed correctness.
Promotion target:
first pass: worst p95 ratio <=1.5 with failure count 0;
final target: worst p95 ratio <=1.0 or plateau after three
correctness-green packets with less than 5% gain and no safe P0/P1 profiler
hypothesis left.
Handoff
Report:
benchmark command and primary metric;
baseline, latest, and best values;
kept, discarded, crashed, and checks-failed packets;
