Update V8 Version
openinterpreter/openinterpreter
Bumps the pinned v8 and rusty_v8 versions in Codex, validates the release-candidate path with the v8-canary check, and traces failures to upstream build changes.
Add or update an orderbook-rs hot-path latency benchmark that reports p50 / p99 / p99.9 / p99.99 via hdrhistogram, not the criterion default mean.
$ npx skills add joaquinbejar/OrderBook-rs --skill bench-hdr -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install joaquinbejar/OrderBook-rs bench-hdr --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/joaquinbejar/OrderBook-rs.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/bench-hdr .claude/skills/bench-hdr && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "bench-hdr" agent skill from https://github.com/joaquinbejar/OrderBook-rs/tree/main/.agents/skills/bench-hdr into .claude/skills/bench-hdr/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bench-hdr", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/joaquinbejar/OrderBook-rs/tree/main/.agents/skills/bench-hdrType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add joaquinbejar/OrderBook-rs --skill bench-hdr -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install joaquinbejar/OrderBook-rs bench-hdr --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/joaquinbejar/OrderBook-rs.git skills-src && mkdir -p .agents/skills && cp -r skills-src/.agents/skills/bench-hdr .agents/skills/bench-hdr && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "bench-hdr" agent skill from https://github.com/joaquinbejar/OrderBook-rs/tree/main/.agents/skills/bench-hdr into .agents/skills/bench-hdr/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bench-hdr", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add joaquinbejar/OrderBook-rs --skill bench-hdr -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install joaquinbejar/OrderBook-rs bench-hdr --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/joaquinbejar/OrderBook-rs.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/.agents/skills/bench-hdr .cursor/skills/bench-hdr && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "bench-hdr" agent skill from https://github.com/joaquinbejar/OrderBook-rs/tree/main/.agents/skills/bench-hdr into .cursor/skills/bench-hdr/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bench-hdr", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/joaquinbejar/OrderBook-rs.git --path .agents/skills/bench-hdr--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add joaquinbejar/OrderBook-rs --skill bench-hdr -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install joaquinbejar/OrderBook-rs bench-hdr --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/joaquinbejar/OrderBook-rs.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/.agents/skills/bench-hdr .gemini/skills/bench-hdr && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "bench-hdr" agent skill from https://github.com/joaquinbejar/OrderBook-rs/tree/main/.agents/skills/bench-hdr into .gemini/skills/bench-hdr/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bench-hdr", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install joaquinbejar/OrderBook-rs bench-hdrInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add joaquinbejar/OrderBook-rs --skill bench-hdr -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/joaquinbejar/OrderBook-rs.git skills-src && mkdir -p .github/skills && cp -r skills-src/.agents/skills/bench-hdr .github/skills/bench-hdr && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "bench-hdr" agent skill from https://github.com/joaquinbejar/OrderBook-rs/tree/main/.agents/skills/bench-hdr into .github/skills/bench-hdr/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bench-hdr", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add joaquinbejar/OrderBook-rs --skill bench-hdr -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install joaquinbejar/OrderBook-rs bench-hdr --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/joaquinbejar/OrderBook-rs.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/.agents/skills/bench-hdr .opencode/skills/bench-hdr && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "bench-hdr" agent skill from https://github.com/joaquinbejar/OrderBook-rs/tree/main/.agents/skills/bench-hdr into .opencode/skills/bench-hdr/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bench-hdr", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
bench-hdrAdd or update an orderbook-rs hot-path latency benchmark that reports p50 / p99 / p99.9 / p99.99 via hdrhistogram, not the criterion default mean.
Bench Hdr is an agent skill from joaquinbejar/OrderBook-rs. Add or update an orderbook-rs hot-path latency benchmark that reports p50 / p99 / p99.9 / p99.99 via hdrhistogram, not the criterion default mean. Use when adding a benchmark for an end-to-end scenario (add-only, cancel-only, aggressive walk, mixed 70/20/10, thin-book IOC sweep, mass-cancel burst, snapshot capture), or when updating an existing bench after a hot-path change. Handles warmup, coordinated-omission disclosure, and a short interpretation block for BENCH.md.
Its SKILL.md is about 2.9k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It works with Rust. The repository describes itself as: A high-performance, thread-safe limit order book implementation written in Rust. This project provides a comprehensive order matching engine designed for low-latency trading… The licence is MIT.
10 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit 54df8eb. It shows what the files ask for, not the result of running them.
Pre-approves these tools, so the agent can use them without asking each time:
ReadWriteEditGrepGlobBashFrom allowed-tools in the SKILL.md frontmatter.
Shell commands in SKILL.md call:
cargorgFrom the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Bench Hdr loads about 2.9k tokens when it runs. Until then it costs about 121 tokens; SKILL.md has 713 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check noted patterns worth knowing about, such as sudo or a known installer.
allowed-tools: Read, Write, Edit, Grep, Glob, BashAutomated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
The full file from joaquinbejar/OrderBook-rs at commit 54df8eb, republished under its MIT licence (© joaquinbejar). 713 words, ~2,854 tokens.
.claude/skills/bench-hdr/SKILL.md (or your agent's skills folder).Generates or updates a reproducible local benchmark for the orderbook-rs hot path.
Output is an HDR-histogram dump with tail quantiles and a short interpretive paragraph
that goes into BENCH.md.
Criterion ships with html_reports in this crate, but its default output is mean-centric
and drops the tails. The hot-path SLO for a matching engine is p99 / p99.9 / p99.99; mean
is a vanity metric. This skill adds a parallel hdrhistogram pipeline that lives alongside
the Criterion benches.
The crate already depends on criterion = { version = "0.8", features = ["html_reports"] }
in Cargo.toml and has a benches/ directory organized under benches/order_book/.
Before the first bench-hdr bench, add hdrhistogram as a dev-dependency:
cargo add --dev hdrhistogram@^7Confirm the addition with rg -n 'hdrhistogram' Cargo.toml.
Pick exactly one per bench file. Each lives under benches/order_book/ alongside the
existing Criterion benches.
| Scenario | Input profile |
|---|---|
add_only | Pure passive limit submissions, no crossings. Measures insert cost. |
cancel_only | Pre-loaded book, cancel workload. Measures DashMap lookup + unlink. |
aggressive_walk | Taker IOC sweeps across several levels. Measures fill-loop tail. |
mixed_70_20_10 | 70% submits, 20% cancels, 10% aggressive IOC. Most "realistic". |
thin_book_sweep | Book near-empty, IOC probing. Exercises partial-fill / reject path. |
mass_cancel_burst | Dense book, then one mass-cancel. Measures bulk-cancel worst case. |
snapshot_capture | Dense book, repeated snapshot() / snapshot_package() calls. |
benches/order_book/<scenario>_hdr.rs (the _hdr suffix keeps it visually
distinct from the existing Criterion benches; they coexist).benches/order_book/hdr_common.rs.record helper inline in hdr_common.rs.target/bench-hdr/<scenario>.hgrm (already
inside target/, so already gitignored).BENCH.md at the repo root (committed).Cargo.toml[[bench]]
name = "mixed_70_20_10_hdr"
path = "benches/order_book/mixed_70_20_10_hdr.rs"
harness = falseharness = false so the bench is a plain binary and we control the measurement loop. The
default Criterion harness is not suitable for tail latency — we need per-sample recording
into an HDR histogram, not a wall-clock-bound iteration count driven by statistical
convergence.
benches/order_book/hdr_common.rs)use hdrhistogram::Histogram;
use std::time::Instant;
/// Histogram sized for 1 ns .. 1 s with 3 significant figures.
pub fn new_histogram() -> Histogram<u64> {
Histogram::<u64>::new_with_bounds(1, 1_000_000_000, 3).expect("hist bounds")
}
/// Measure a closure once, record nanoseconds into the histogram.
#[inline(always)]
pub fn record<F, R>(h: &mut Histogram<u64>, f: F) -> R
where
F: FnOnce() -> R,
{
let t0 = Instant::now();
let r = std::hint::black_box(f());
let elapsed = t0.elapsed().as_nanos() as u64;
h.record(elapsed.max(1)).expect("record");
r
}
pub fn report(name: &str, h: &Histogram<u64>) {
println!("scenario : {}", name);
println!("samples : {}", h.len());
println!("p50 (ns) : {}", h.value_at_quantile(0.50));
println!("p99 (ns) : {}", h.value_at_quantile(0.99));
println!("p99.9 (ns) : {}", h.value_at_quantile(0.999));
println!("p99.99(ns) : {}", h.value_at_quantile(0.9999));
println!("max (ns) : {}", h.max());
println!("min (ns) : {}", h.min());
}
pub fn persist(name: &str, h: &Histogram<u64>) -> std::io::Result<()> {
use hdrhistogram::serialization::V2Serializer;
std::fs::create_dir_all("target/bench-hdr")?;
let path = format!("target/bench-hdr/{}.hgrm", name);
let mut f = std::fs::File::create(&path)?;
V2Serializer::new()
.serialize(h, &mut f)
.map_err(|e| std::io::Error::new(std::io::ErrorKind::Other, e))?;
eprintln!("wrote {}", path);
Ok(())
}Example benches/order_book/mixed_70_20_10_hdr.rs (adapt the exact call signatures to the
public API as it exists at the time the bench is written; verify with
rg -n 'pub fn submit|pub fn cancel|pub fn mass_cancel' src/orderbook/operations.rs src/orderbook/modifications.rs src/orderbook/mass_cancel.rs):
#[path = "hdr_common.rs"]
mod hdr_common;
use hdr_common::{new_histogram, persist, record, report};
use orderbook_rs::prelude::*;
const WARMUP_OPS: usize = 200_000;
const MEASURED_OPS: usize = 1_000_000;
const SEED: u64 = 0xA5A5_A5A5;
enum Op {
Submit { id: Id, owner: u64, side: Side, price: Price, qty: Quantity, tif: TimeInForce },
Cancel(Id),
Aggressive { id: Id, owner: u64, side: Side, qty: Quantity },
}
fn build_mixed_workload(n: usize, seed: u64) -> Vec<Op> {
// Deterministic PRNG from `seed`; no rand crate — use a small xorshift so the bench
// is self-contained and reproducible. Tight price band (99..=101) for frequent
// crossings on the aggressive slice.
let mut s = seed;
let mut next = || { s ^= s << 13; s ^= s >> 7; s ^= s << 17; s };
let mut ops = Vec::with_capacity(n);
for i in 0..n {
let bucket = next() % 100;
let id = Id::from_u64(i as u64 + 1);
let owner = (next() % 4) + 1;
let side = if next() % 2 == 0 { Side::Buy } else { Side::Sell };
let price = Price::from_u64(99 + (next() % 3));
let qty = Quantity::from_u64(1 + (next() % 100));
if bucket < 70 {
ops.push(Op::Submit { id, owner, side, price, qty, tif: TimeInForce::Gtc });
} else if bucket < 90 {
ops.push(Op::Cancel(Id::from_u64(1 + (next() % (i as u64).max(1)))));
} else {
ops.push(Op::Aggressive { id, owner, side, qty });
}
}
ops
}
fn apply(book: &OrderBook<()>, op: &Op) {
match op {
Op::Submit { id, owner, side, price, qty, tif } => {
let _ = book.submit_limit(*id, *owner, *side, *price, *qty, *tif);
}
Op::Cancel(id) => {
let _ = book.cancel(*id);
}
Op::Aggressive { id, owner, side, qty } => {
let _ = book.submit_market(*id, *owner, *side, *qty);
}
}
}
fn main() {
let workload = build_mixed_workload(WARMUP_OPS + MEASURED_OPS, SEED);
let book = OrderBook::<()>::new("BENCH");
let mut h = new_histogram();
// Warmup — discarded.
for op in &workload[..WARMUP_OPS] { apply(&book, op); }
// Measurement.
for op in &workload[WARMUP_OPS..] {
record(&mut h, || apply(&book, op));
}
report("mixed_70_20_10", &h);
persist("mixed_70_20_10", &h).expect("persist");
}The loop above is a closed-loop benchmark (the driver waits for each op to finish
before issuing the next). Under saturation this systematically under-reports tail
latency because coordinated omission hides queueing stalls. Two options; pick one and
document the choice in BENCH.md:
now - scheduled_arrival,
not now - ingest_start. Requires picking a target rate (e.g. 500k ops/s). CO is
handled by construction, no separate disclosure.BENCH.md that the numbers are pure service time, not tail under load. Useful as a
lower bound and a regression signal, but not a production SLO.For a regression-signal bench inside a lock-free crate, Option B is acceptable if you are explicit. Option A, done sloppily, is worse than Option B done honestly.
BENCH.mdMissing entries are a negative signal to any reviewer.
performance or powersave).taskset -c 2 cargo bench --bench mixed_70_20_10_hdr)
and, if so, which core and whether it was isolated.nohz_full, rcu_nocbs, SMT state.--release, LTO setting, RUSTFLAGS.BENCH.md template block## mixed_70_20_10
Workload: 70% submits, 20% cancels, 10% aggressive market (IOC-like). Seed 0xA5A5A5A5.
Samples: 1,000,000 after 200,000 warmup ops.
Loop: closed-loop. Reported numbers are pure service time; see Methodology §CO.
| Quantile | Latency |
|-----------|-------------|
| p50 | XXX ns |
| p99 | XXX ns |
| p99.9 | XXX ns |
| p99.99 | XXX ns |
**Where the tail comes from.**
[One honest paragraph. Acceptable content: cache miss on `SkipMap` price lookup beyond L2,
branch mispredict on the fill loop when the book is thin, allocator jitter from
`BookChangeEvent` emission when the outbound `Vec` resizes, `DashMap` shard contention on
the order-id index under concurrent writers. Do not write "probably jitter" — if you
don't know, say "the dominant contributor is not yet identified; next step is
`perf stat -e <events>` on the measured window."]cargo bench --bench <scenario>_hdr.BENCH.md table with the output.target/bench-hdr/*.hgrm is already under target/ so it is gitignored; do not commit
the histograms. Commit BENCH.md.bench: add <scenario> HDR histogram bench.The Criterion benches under benches/order_book/ (add_orders.rs, match_orders.rs,
mass_cancel.rs, matching.rs, mixed_operations.rs, replay.rs, snapshot.rs,
update_orders.rs) stay as they are — they provide the mean-centric statistical
comparison that Criterion does well and publish HTML reports to target/criterion/.
The _hdr benches coexist with them and are the source of truth for tail-latency claims
in BENCH.md and any release notes that quote p99 / p99.9 / p99.99 numbers.
© joaquinbejar, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Just SKILL.md in .agents/skills/bench-hdr of joaquinbejar/OrderBook-rs.
Open the folder on GitHubat commit 54df8eb
Bench Hdr next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Bench Hdr this skilljoaquinbejar/OrderBook-rs | 543 | — | ~2.9k | Automated safety check: Notes | MIT | |
| Update V8 Versionopeninterpreter/openinterpreter | 69k | 2 repos | ~845 | Automated safety check: Pass | Apache-2.0 | |
| Firecrawl Page Scrape Integrationfirecrawl/firecrawl | 190k | 1 repos | ~944 | Automated safety check: Pass | ISC | |
| Migrate Core Code to Submodulestinyhumansai/openhuman | 42k | — | ~2.6k | Automated safety check: Pass | GPL-3.0 | |
| Rust TDD Workflowrtk-ai/rtk | 83k | — | ~753 | Automated safety check: Notes | Apache-2.0 | |
| OpenLogi macOS Permissions TriageAprilNEA/OpenLogi | 23k | — | ~2.5k | Automated safety check: Notes | Apache-2.0 |
openinterpreter/openinterpreter
Bumps the pinned v8 and rusty_v8 versions in Codex, validates the release-candidate path with the v8-canary check, and traces failures to upstream build changes.
firecrawl/firecrawl
Adds Firecrawl's /scrape endpoint to application code to pull markdown, HTML, links, screenshots or structured data from a single known URL.
tinyhumansai/openhuman
Plans and carries out moving non-host-specific code and its tests from the OpenHuman core into vendored tiny submodule libraries, then releases the submodule and re-pins the host.
rtk-ai/rtk
Enforces red-green-refactor for Rust work, with idiomatic test patterns, a naming convention and a pre-commit gate of cargo fmt, clippy and test.
AprilNEA/OpenLogi
Decides whether an OpenLogi device problem on macOS is a privacy-permission (TCC) problem, using agent log lines, and says which identity needs which grant.
farm-fe/farm
Guide for writing idiomatic Rust code based on Apollo GraphQL's best practices handbook.
joaquinbejar/OrderBook-rs
Generate a proptest block for a named orderbook-rs matching-engine invariant.
joaquinbejar/OrderBook-rs
Autonomously implement every roadmap issue end-to-end (PR → Copilot review → respond → green CI → merge → roadmap update) until done
Works with
Add or update an orderbook-rs hot-path latency benchmark that reports p50 / p99 / p99.9 / p99.99 via hdrhistogram, not the criterion default mean. Bench Hdr is an agent skill from joaquinbejar/OrderBook-rs.99 via hdrhistogram, not the criterion default mean.
Bench Hdr fits situations like: adding a benchmark for an end-to-end scenario (add-only; aggressive walk; thin-book IOC sweep; mass-cancel burst.
Run `npx skills add joaquinbejar/OrderBook-rs --skill bench-hdr -a claude-code`. Or copy the skill folder (.agents/skills/bench-hdr in joaquinbejar/OrderBook-rs) into .claude/skills/bench-hdr in your project. Claude Code loads it when a task matches its description.
Run `npx skills add joaquinbejar/OrderBook-rs --skill bench-hdr -a codex`. Or copy the skill folder (.agents/skills/bench-hdr in joaquinbejar/OrderBook-rs) into .agents/skills/bench-hdr in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add joaquinbejar/OrderBook-rs --skill bench-hdr -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/bench-hdr, .gemini/skills/bench-hdr, .github/skills/bench-hdr and .opencode/skills/bench-hdr in your project.
Going by SKILL.md and its folder, Bench Hdr needs the command-line tools its instructions call (cargo and rg). Its frontmatter pre-approves these tools: Read, Write, Edit, Grep, Glob, Bash.
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found notes only (pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.
Bench Hdr is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 2.9k tokens (SKILL.md is roughly 11k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Bench Hdr: Update V8 Version (openinterpreter/openinterpreter, 69k stars), Firecrawl Page Scrape Integration (firecrawl/firecrawl, 190k stars), Migrate Core Code to Submodules (tinyhumansai/openhuman, 42k stars) and Rust TDD Workflow (rtk-ai/rtk, 83k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
joaquinbejar (a GitHub user) maintains it in joaquinbejar/OrderBook-rs, which has 543 GitHub stars. The repository holds 3 skills in this directory. The repository was last updated on October 5, 2026.
Source: joaquinbejar/OrderBook-rs on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.