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 HeshamFS/materials-simulation-skills --skill performance-profilinggit clone --depth 1 https://github.com/HeshamFS/materials-simulation-skillsWrote 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/heshamfs/materials-simulation-skills/performance-profiling)<a href="https://agentmods.dev/skills/heshamfs/materials-simulation-skills/performance-profiling"><img src="https://agentmods.dev/badge/skills/heshamfs/materials-simulation-skills/performance-profiling/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/heshamfs/materials-simulation-skills/performance-profiling"><img src="https://agentmods.dev/badge/skills/heshamfs/materials-simulation-skills/performance-profiling.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 | $0.00133 | $0.03709 |
| Opus 5.5 | $0.00053 | $0.01484 |
| Sonnet 5.5 | $0.00027 | $0.00742 |
| Haiku 4.5 | $0.00013 | $0.00371 |
Grade A, and why
performance-profiling 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 — 343 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Performance Profiling
Goal
Provide tools to analyze simulation performance, identify bottlenecks, and recommend optimization strategies for computational materials science simulations.
Requirements
- Python 3.10+
- No external dependencies (uses Python standard library only)
- Works on Linux, macOS, and Windows
Inputs to Gather
Before running profiling scripts, collect from the user:
| Input | Description | Example |
|---|---|---|
| Simulation log | Log file with timing information | simulation.log |
| Scaling data | JSON with multi-run performance data | scaling_data.json |
| Simulation parameters | JSON with mesh, fields, solver config | params.json |
| Available memory | System memory in GB (optional) | 16.0 |
Decision Guidance
When to Use Each Script
Need to identify slow phases?
├── YES → Use timing_analyzer.py
│ └── Parse simulation logs for timing data
│
Need to understand parallel performance?
├── YES → Use scaling_analyzer.py
│ └── Analyze strong or weak scaling efficiency
│
Need to estimate memory requirements?
├── YES → Use memory_profiler.py
│ └── Estimate memory from problem parameters
│
Need optimization recommendations?
└── YES → Use bottleneck_detector.py
└── Combine analyses and get actionable advice
Choosing Analysis Thresholds
| Metric | Good | Acceptable | Poor |
|---|---|---|---|
| Phase dominance | <30% | 30-50% | >50% |
| Parallel efficiency | >0.80 | 0.70-0.80 | <0.70 |
| Memory usage | <60% | 60-80% | >80% |
Script Outputs (JSON Fields)
All scripts wrap their payload in a top-level object with two keys: inputs and results. The fields below live under results.
| Script | Key Outputs (under results) |
|---|---|
timing_analyzer.py |
results.phases, results.slowest_phase, results.total_time |
scaling_analyzer.py |
results.results, results.efficiency_threshold_processors, results.average_efficiency, results.baseline |
memory_profiler.py |
results.total_memory_gb, results.per_process_gb, results.field_memory_gb, results.solver_workspace_gb, results.matrix_storage_gb, results.warnings |
bottleneck_detector.py |
results.bottlenecks, results.recommendations |
What ships with it
14 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.
- CHANGELOG.md 3.8 KB
- evals/evals.json 9.2 KB
- evals/files/custom_format.log 120 B
- evals/files/memory_params.json 321 B
- evals/files/scaling_results.json 211 B
- evals/files/scaling.json 809 B
- evals/files/simulation.log 113 B
- evals/files/timing.json 1019 B
- references/optimization_strategies.md 13 KB
- references/profiling_guide.md 9.7 KB
- scripts/bottleneck_detector.py 15 KB runs code
- scripts/memory_profiler.py 11 KB runs code
- scripts/scaling_analyzer.py 8.0 KB runs code
- scripts/timing_analyzer.py 8.5 KB runs code
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 · 343 lines · 133 tokens per session scan A 9d7e4a35d3e2
performance-profiling is a skill published in the GitHub repository HeshamFS/materials-simulation-skills (74 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 133 tokens to every session and 3,709 once invoked, about $0.0005 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 2026-08-30.
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