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 skills add OZmasterAI/Torus-Framework --skill super-prof-optimizegit clone --depth 1 https://github.com/OZmasterAI/Torus-FrameworkWrote 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/ozmasterai/torus-framework/super-prof-optimize)<a href="https://agentmods.dev/skills/ozmasterai/torus-framework/super-prof-optimize"><img src="https://agentmods.dev/badge/skills/ozmasterai/torus-framework/super-prof-optimize/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.
<a href="https://agentmods.dev/skills/ozmasterai/torus-framework/super-prof-optimize"><img src="https://agentmods.dev/badge/skills/ozmasterai/torus-framework/super-prof-optimize.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00000 | $0.01603 |
| Opus 5.5 | $0.00000 | $0.00641 |
| Sonnet 5.5 | $0.00000 | $0.00321 |
| Haiku 4.5 | $0.00000 | $0.00160 |
Grade A, and why
super-prof-optimize 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.
How it starts
The opening of the file, as written. The whole thing — 168 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/super-prof-optimize — Performance Profiling & Optimization
Merged from: /profile + /optimize
When to use
When the user says "super-profile", "super-optimize", "deep profile", "full optimization", "performance analysis", or wants comprehensive profiling AND optimization in a single workflow covering both general code and Torus framework-specific bottlenecks.
Commands
/super-prof-optimize— Full profile + optimize cycle/super-prof-optimize --profile-only— Profile and analyze without making changes/super-prof-optimize hooks— Focus on hook/gate latency/super-prof-optimize memory— Focus on memory/ChromaDB performance/super-prof-optimize gates— Focus on gate execution time/super-prof-optimize <target>— Profile a specific file, function, or module
Steps
1. MEMORY CHECK
search_knowledge("[target function/module] performance")— check for prior profiling resultssearch_knowledge("tag:area:performance")— find historical performance datasearch_knowledge("optimization performance latency")— find prior optimization results- If prior benchmarks exist, use
get_memory(id)to retrieve baselines for comparison
2. DETECT TOOLING
Identify available profiling tools:
- Python:
cProfile,profile,timeit,line_profiler,py-spy,pytest-benchmark,memory_profiler - Node/JS:
--prof,clinic,0x,benchmark.js - General:
hyperfine(CLI benchmarking),time,strace,perf - Check
requirements.txt,pyproject.toml,package.jsonfor installed profilers - If no profiler is available, suggest installing the most appropriate one
3. FRAMEWORK-SPECIFIC PROFILING
For Torus framework components specifically:
- Read today's audit log for timing data
- Check state files for
gate_timingentries - Measure hook execution:
time python3 ~/.claude/hooks/enforcer.py - Count memory operations from audit log
- Find the 3 slowest gates by average execution time
- Find hooks that frequently timeout (>3s)
- Check for redundant memory queries (same query within 60s)
- Look for N+1 patterns in gate checks
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.
- 1mo ago First seen · 168 lines · 0 tokens per session scan A 891d2023894e
super-prof-optimize is a skill published in the GitHub repository OZmasterAI/Torus-Framework (5 stars, last pushed 5mo ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 1,603 tokens. 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 2026-08-31.
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