benchmark

A performance measurement and tracking workflow for recording how a framework behaves over time. It measures test results, memory counts, gate activity, hook timing, and session data.

In plain words
What is it for?
Use it for full or quick benchmark runs, timing tests, regression checks, and saving a new performance baseline.
Why use it?
It shows whether the framework has improved or regressed by comparing current measurements with earlier baselines, which are saved reference results.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/ozmasterai/torus-framework/benchmark
Any agent
npx skills add OZmasterAI/Torus-Framework --skill benchmark
Clone the repo
git clone --depth 1 https://github.com/OZmasterAI/Torus-Framework

Made for: Claude Code, Codex.

Per session 59 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,993 The whole file, excluding the scripts and references it only reads on demand.
Security scan B 2 findings. Scan, not verified.
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 $0.00059 $0.02993
Opus 5 $0.00030 $0.01496
Sonnet 5 $0.00012 $0.00599
Haiku 4.5 $0.00006 $0.00299

Measured 2d ago against content hash 6ed0b94a32c4, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade B, and why

benchmark scanned grade B with 2 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 2d 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.

Reads agent configuration directoriesmediumAgent snooping

.claude/, .codex/, .gemini/ hold keys, settings and other credentials a mod has no legitimate need for.

cat ~/.claude/stats-cache.json

Runs shell commandslowCapability

Expected in a hook, worth knowing in a rule or an instructions file.

subprocess.run(
dormant/skills/self-improve/benchmark/SKILL.md · 334 lines

How it starts

The opening of the file, as written. The whole thing — 334 lines — stays where its author put it; the contents beside it link to each section on GitHub.

/benchmark — Framework Performance Benchmarking

When to use

When the user says "benchmark", "measure performance", "track metrics", "how fast are the gates", "regression check", "baseline", or wants to quantify framework health over time.

Commands

  • /benchmark — Full benchmark run (measure + baseline + profile + analyze + report + save)
  • /benchmark --quick — Skip profiling (measure + baseline + report only, faster)
  • /benchmark --profile-only — Skip baseline comparison, run timing tests only
  • /benchmark --save-only — Save current state as a new baseline without running profiling

Steps

1. MEASURE — Collect current metrics

Gather the following metrics from live framework state:

Test suite:

python3 ~/.claude/hooks/test_framework.py 2>&1 | tail -10

Extract: total tests, pass count, fail count, pass rate %.

Memory system:

cat ~/.claude/stats-cache.json

Returns: {"ts": <epoch>, "mem_count": <N>}. Also note memory count from search_knowledge("*", top_k=1) result header (total_memories field).

Framework state:

python3 -c "
import json
with open('~/.claude/LIVE_STATE.json') as f:
    s = json.load(f)
print(json.dumps({
    'session_count': s.get('session_count'),
    'framework_version': s.get('framework_version'),
    'feature': s.get('feature'),
}, indent=2))
"

Gate fire rates from today's audit log:

python3 -c "
import json, os, collections
from datetime import date
log = os.path.expanduser(f'~/.claude/hooks/audit/{date.today()}.jsonl')
if not os.path.exists(log):
    print('No audit log for today')
else:
    entries = [json.loads(l) for l in open(log) if l.strip()]
    total = len(entries)
    by_gate = collections.Counter(e.get('gate','?') for e in entries)
    blocked = sum(1 for e in entries if e.get('decision') == 'block')
    warned = sum(1 for e in entries if e.get('decision') == 'warn')
    passed = sum(1 for e in entries if e.get('decision') == 'pass')
    print(json.dumps({
        'total_events': total,
        'passed': passed,
        'warned': warned,
        'blocked': blocked,
        'block_rate_pct': round(blocked/total*100, 1) if total else 0,
        'top_gates': dict(by_gate.most_common(5)),
    }, indent=2))
"

Read the full file on GitHub · 334 lines

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. 2d ago First seen · 334 lines · 59 tokens per session scan B 6ed0b94a32c4

Subscribe to this mod's changes

benchmark is a skill published in the GitHub repository OZmasterAI/Torus-Framework (5 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 59 tokens to every session and 2,993 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it B with 2 findings (reads agent configuration directories, runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

Related

Other skills, from other repositories

systematic-debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.

obra/superpowers · 21 tokens

next-cache-components-adoption

Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…

vercel/next.js · 95 tokens

babysit-pr

Babysit a GitHub pull request after creation by continuously polling review comments, CI checks/workflow runs, and mergeability state until the PR is merged/closed or user help is required. Diagnose failures, retry likely flaky failures up to 3 times, auto-fix/push branch-related issues when appropriate, and keep…

openai/codex · 114 tokens

imagegen

Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output…

openai/codex · 113 tokens

cpu-profile-analysis

Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…

microsoft/vscode · 71 tokens

next-cache-components-optimizer

Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…

vercel/next.js · 170 tokens