Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/nguyenyou/scalex/benchmarknpx skills add nguyenyou/scalex --skill benchmarkgit clone --depth 1 https://github.com/nguyenyou/scalexWrote 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.
[](https://agentmods.dev/skills/nguyenyou/scalex/benchmark)<a href="https://agentmods.dev/skills/nguyenyou/scalex/benchmark"><img src="https://agentmods.dev/badge/skills/nguyenyou/scalex/benchmark.svg" alt="Measured on agentmods" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00152 | $0.03526 |
| Opus 5 | $0.00076 | $0.01763 |
| Sonnet 5 | $0.00030 | $0.00705 |
| Haiku 4.5 | $0.00015 | $0.00353 |
Grade C, and why
benchmark scanned grade C with 1 finding 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 4d 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.
Recursive force deletehighDestructive command
rm -rf with a variable or a broad path is one typo away from removing the wrong tree.
rm -rf benchmark/scala3/.scalex How it starts
The opening of the file, as written. The whole thing — 367 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Overview
Scalex has a multi-layered profiling and benchmarking system. Pick the right layer for the situation:
| Layer | Tool | When to use | Works in native? |
|---|---|---|---|
1. --timings |
Built-in flag | Quick phase breakdown, first look at any perf question | Yes |
| 2. hyperfine | bench.sh |
Reproducible before/after comparison with statistics | Yes |
| 3. async-profiler | profiling/profile.sh |
Deep CPU/alloc/lock flame graphs to find hotspots | JVM only |
| 4. JFR | profiling/scalex.jfc |
GC pressure, file I/O patterns, thread utilization | JVM only |
| 5. Microbenchmarks | src/bench.scala |
Isolate per-function cost with warmup + statistics | JVM only |
| 6. Memory profiling | bench.sh memory |
Heap usage, GC pressure, peak memory across scenarios | JVM only |
Decision guide
"Where is time spent?" → Start with --timings (Layer 1)
"Is this change faster?" → Use hyperfine before/after (Layer 2), optionally with bench-compare.sh
"Why is parsing slow?" → async-profiler CPU flame graph (Layer 3)
"Why are allocations high?" → async-profiler alloc or JFR ObjectAllocationSample (Layer 3/4)
"Is there GC pressure?" → JFR (Layer 4)
"How fast is extractSymbols on one file?" → Microbenchmark (Layer 5)
"How much memory does indexing use?" → Memory profiling (Layer 6)
"Is there a memory leak or GC regression?" → Memory profiling before/after (Layer 6)
Layer 1: --timings flag
The fastest way to see where time goes. Works in both JVM and native image. Prints to stderr.
# Cold index phase breakdown
rm -rf benchmark/scala3/.scalex
./scalex index benchmark/scala3 --timings
# Warm index
./scalex index benchmark/scala3 --timings
# Query with bloom/text-search breakdown
./scalex refs benchmark/scala3 Compiler --timings
# JVM mode
scala-cli run src/ -- index benchmark/scala3 --timings
Phases reported
Index phases: git-ls-files, cache-load, oid-compare, parse, index-build, cache-save
What ships with it
2 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.
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.
- 4d ago First seen · 367 lines · 152 tokens per session scan C 667c86f31106
benchmark is a skill published in the GitHub repository nguyenyou/scalex (94 stars, last pushed 2mo ago), licensed MIT. It adds 152 tokens to every session and 3,526 once invoked, about $0.0008 per session on Opus 5. A static security scan graded it C with 1 finding (recursive force delete). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
projectatlas
Use ProjectAtlas as the atlas-first orientation layer before broad source reads, with MCP-first task startup, short-alias worktree registration and routing, safe targeted initialization, ranked navigation, exact or federated graph evidence, purpose curation, health, lint, and repository-wide token reporting.
codex-coding-plugin
Build, review, or fix ProjectAtlas plugin/runtime installer integration for Codex, Claude Code, and OpenCode, especially version convergence, stale ProjectAtlas cache repair, MCP config generation, skill artifacts, host smoke tests, and fake-host tests for ProjectAtlas releases.
research
Performs deep research on a topic via deepresearch. Simulates a multi-step research process and returns a comprehensive research result as a string.
octocode-chrome-devtools
Use when a live page needs Chrome DevTools/CDP evidence: network failures, console errors, performance, DOM/CSS actionability, screenshots/PDF, cookies/storage, click/fill/search, HAR, or auth-gated pages. Phrases like debug in Chrome, live page health, CDP snapshot, cookie bridge. Not for static crawl or bulk extract…
octocode-graph-eval
Use when you need a measurable keep/discard loop — goal→KPI, baseline vs target, held-out checks, eval suites, or don't-stop-till-done against a runnable sensor. Not for ordinary ship checks where 'tests passed' is enough.
octocode-subagent
Use when spawning workers or offloading work: Task/subagents, specialist handoffs, A2A peers, sealed packets, coordinate/synthesize, challenge techniques (rubber-duck, interview, mimic-flow, red-team, blind review, consensus), or local Ollama one-shots to save tokens. Measuring keep/discard KPIs → octocode-graph-eval.