palot: Skill for Codex

.agents/skills/palot-performance/SKILL.md

palot-performance is a skill for Codex from ItsWendell/palot. It costs 78 tokens per session (2,476 once invoked), scanned A, original, MIT.

A performance-investigation skill for Palot's desktop application. It measures CPU, memory, long tasks, screen updates, layout and painting work, graphics compositing, and streaming delay using documented checks and Electron tests.

In plain words
What is it for?
Use it for reported slowdowns or regressions, performance profiling, performance-harness changes, and checks involving React rendering or compiler compatibility.
Why use it?
It replaces guesswork about slowness with measurements from the relevant part of the desktop app. It helps distinguish problems in rendering, input handling, resource use, or streaming.

Skill for Codex

Written for Codex: agents/openai.yaml present. Also seen: mentions subagents; installed under .agents/ (shared by several agents); mentions OpenCode.

This is ItsWendell/palot's own configuration. It tells Codex how to work on palot itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything palot configures →

Reuse

Borrowing it

Nothing to install: this file belongs to ItsWendell/palot. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/ItsWendell/palot/main/.agents/skills/palot-performance/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/ItsWendell/palot

Made for: Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for palot-performance

README.md
[![agentmods](https://agentmods.dev/badge/skills/itswendell/palot/palot-performance/github.svg)](https://agentmods.dev/skills/itswendell/palot/palot-performance)
Your own site
<a href="https://agentmods.dev/skills/itswendell/palot/palot-performance"><img src="https://agentmods.dev/badge/skills/itswendell/palot/palot-performance/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for palot-performance

Your own site · 80×15
<a href="https://agentmods.dev/skills/itswendell/palot/palot-performance"><img src="https://agentmods.dev/badge/skills/itswendell/palot/palot-performance.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 78 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,476 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00078 $0.02476
Opus 5.5 $0.00031 $0.00990
Sonnet 5.5 $0.00016 $0.00495
Haiku 4.5 $0.00008 $0.00248

Measured 1mo ago against content hash 10e963d73f64, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

palot-performance scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.agents/skills/palot-performance/SKILL.md · 93 lines

How it starts

The opening of the file, as written. The whole thing — 93 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Palot Performance

Use the smallest loop that can prove or disprove the suspected cost. Preserve UX, motion, and native/CSS material unless measurements show a concrete problem and the cheaper rendering path still looks right.

Read repository-root ../../../docs/desktop-testing.md for harness commands and measurement contracts. Read references/main-thread-budget.md for current main-thread investigation rules; historical measurements are not evidence for the current checkout.

Choose The Loop

  • Static React health: run bun run perf:doctor for changed code and bun run perf:react-check for React Compiler compatibility diagnostics.
  • Full static baseline: run bun run perf:doctor:full. Treat it as advisory until the documented baseline has been paid down.
  • Deterministic native measurement: run bun run perf:e2e. It builds an isolated Electron app, warms a long transcript switch, records frame intervals, long tasks, CDP metrics, Electron process CPU/memory, and streaming latency, then retains private artifacts.
  • Input contention: run bun run perf:input. It types a real draft while the visible session and background sessions stream. Use it to evaluate responsiveness, not only throughput.
  • Batch-size contention: bun run perf:batch:steady and bun run perf:batch:burst use identical numbered text with different provider arrival clustering. Inspect actual renderer batch sizes and synchronous callback durations alongside typing, freshness, and throughput; the transport timer is not a processing budget. These are different workloads, not a product A/B comparison.
  • Navigation/cancellation contention: run bun run perf:interactions. It checks trusted session switches, wheel-up, a stable visible reading anchor, return to latest, and Stop under four continuous streams. Per-action DOM milestones include existing fades and are not compositor presentation.
  • Visual recording: bun run perf:input:video or add --video to a matching E2E command. Requires ffmpeg; retains renderer-only video.mp4 and capture metadata. Recording adds observer overhead, is not presented-frame/FPS evidence, and must not be compared to unrecorded benchmarks.
  • Streaming geometry: bun run perf:stability (or perf:stability:video) adds bounded active-turn/composer/scroll geometry sampling. Use it for bottom-follow jumps, not as a clean input benchmark. Inspect rAF and resize-observer phases separately; transient DOM measurements are not presented-frame proof.
  • Streaming throughput and scroll correctness: run bun run perf:parallel; use bun run perf:parallel:trace for whole-app attribution. bun run perf:parallel:memory repeats accumulating workloads with forced GC to investigate retention, not independent latency samples.
  • Style attribution: run bun run perf:selectors. Phase-separated React render attribution: run bun run perf:scan:render. Both add observer overhead and are not neutral benchmarks.
  • Repeated comparison: bun run perf:compare <baseline.json...> -- <candidate.json...> checks matched report metadata and summarizes run-level medians/ranges. It requires at least two independent runs per group (five recommended), rejects legacy/memory-cycle reports, and does not launch tests or declare regression budgets.
  • Attribution trace: run bun run perf:trace. It repeats the deterministic scenario with React's profiling build and writes performance-trace.json beside performance.json.
  • Render overlay: enable React Scan in Settings > Diagnostics or run bun run dev:scan. The dev client starts hidden; use PALOT_REACT_SCAN=1 bun run dev:visible for visible inspection without taking focus, and PALOT_REACT_SCAN=1 bun run dev:focus only when focus behavior matters. Palot reloads so React Scan can install before React. Use it to find candidate rerenders, then disable it before measuring.
  • Compiler experiment: run bun run perf:compiler for whole-renderer infer mode or bun run perf:compiler:annotation to validate opt-in mode. The normal build does not enable React Compiler.

Read the full file on GitHub · 93 lines

Files

What ships with it

4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 1mo ago First seen · 93 lines · 78 tokens per session scan A 10e963d73f64

Subscribe to this mod's changes

palot-performance is a skill published in the GitHub repository ItsWendell/palot (194 stars, last pushed 2d ago), licensed MIT. It adds 78 tokens to every session and 2,476 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-13.

Related

palot-performance compared with skills from other repositories

palot-performance next to 6 skills from other repositories
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palot-performanceItsWendell/palotSkill1942d agoMITA78Codex
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sandbox-unblockFlorianBruniaux/claude-code-ultimate-guideSkill6.4k2d agoCC-BY-SA-4.0A65Claude Code
investigateFlorianBruniaux/claude-code-ultimate-guideSkill6.4k2d agoCC-BY-SA-4.0A17Claude Code