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 simulation-orchestratorgit 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/simulation-orchestrator)<a href="https://agentmods.dev/skills/heshamfs/materials-simulation-skills/simulation-orchestrator"><img src="https://agentmods.dev/badge/skills/heshamfs/materials-simulation-skills/simulation-orchestrator.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 5.1 | $0.00125 | $0.04245 |
| Opus 5 | $0.00063 | $0.02122 |
| Sonnet 5 | $0.00025 | $0.00849 |
| Haiku 4.5 | $0.00013 | $0.00424 |
Grade A, and why
simulation-orchestrator 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 8d 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 — 350 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Simulation Orchestrator
Goal
Provide tools to manage multi-simulation campaigns: generate parameter sweeps, track job execution status, and aggregate results from completed runs.
Requirements
- Python 3.10+
- No external dependencies (uses Python standard library only)
- Works on Linux, macOS, and Windows
Inputs to Gather
Before running orchestration scripts, collect from the user:
| Input | Description | Example |
|---|---|---|
| Base config | Template simulation configuration | base_config.json |
| Parameter ranges | Parameters to sweep with bounds | dt:[1e-4,1e-2],kappa:[0.1,1.0] |
| Sweep method | How to sample parameter space | grid, lhs, linspace |
| Output directory | Where to store campaign files | ./campaign_001 |
| Simulation command | Command to run each simulation | python sim.py --config {config} |
Decision Guidance
Choosing a Sweep Method
Need every combination (full factorial)?
├── YES → Use grid (warning: exponential growth with parameters)
└── NO → Is space-filling coverage needed?
├── YES → Use lhs (Latin Hypercube Sampling)
└── NO → Use linspace for uniform sampling per parameter
| Method | Best For | Sample Count |
|---|---|---|
grid |
Low dimensions (1-3), need exact corners | n^d (exponential) |
linspace |
1D sweeps, uniform spacing | n per parameter |
lhs |
High dimensions, space-filling | user-specified budget |
Campaign Size Guidelines
| Parameters | Grid Points Each | Total Runs | Recommendation |
|---|---|---|---|
| 1 | 10 | 10 | Grid is fine |
| 2 | 10 | 100 | Grid acceptable |
| 3 | 10 | 1,000 | Consider LHS |
| 4+ | 10 | 10,000+ | Use LHS or DOE |
Script Outputs (JSON Fields)
| Script | Output Fields |
|---|---|
scripts/sweep_generator.py |
configs, parameter_space, sweep_method, total_runs |
scripts/campaign_manager.py --action init |
campaign_id, total_jobs, config_dir, command_template |
scripts/campaign_manager.py --action status |
campaign_id, status, jobs, progress, total_jobs, created_at |
scripts/campaign_manager.py --action list |
jobs (array of job records) |
scripts/job_tracker.py |
job_id, status, start_time, end_time, exit_code |
scripts/result_aggregator.py |
summary (incl. minimize), statistics, best_run, failed_runs |
What ships with it
24 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 3.1 KB
- evals/evals.json 11 KB
- evals/files/case3_init/config_0000.json 92 B
- evals/files/case3_init/config_0001.json 92 B
- evals/files/case3_init/config_0002.json 90 B
- evals/files/case3_init/config_0003.json 90 B
- evals/files/case3_init/manifest.json 481 B
- evals/files/case3_status/campaign.json 1.7 KB
- evals/files/case4_campaign/campaign.json 1.8 KB
- evals/files/case4_campaign/config_0000.json 44 B
- evals/files/case4_campaign/config_0001.json 44 B
- evals/files/case4_campaign/config_0002.json 42 B
- evals/files/case4_campaign/result_job_0000.json 69 B
- evals/files/case4_campaign/result_job_0001.json 69 B
- evals/files/case4_campaign/result_job_0002.json 69 B
- evals/files/config.json 74 B
- evals/files/simulation.json 91 B
- references/aggregation_methods.md 4.0 KB
- references/campaign_patterns.md 3.8 KB
- references/sweep_strategies.md 4.4 KB
- scripts/campaign_manager.py 9.5 KB runs code
- scripts/job_tracker.py 9.4 KB runs code
- scripts/result_aggregator.py 12 KB runs code
- scripts/sweep_generator.py 14 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.
- 8d ago First seen · 350 lines · 125 tokens per session scan A 7a39500b28eb
simulation-orchestrator is a skill published in the GitHub repository HeshamFS/materials-simulation-skills (66 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 125 tokens to every session and 4,245 once invoked, about $0.0006 per session on Opus 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.
Other skills, from other repositories
search-math-results
Find program-conditioned math results, constructions, examples, counterexamples, analogies, and background references. Use when the current active program needs repair, mutation, analogy, a program shift, or carefully gated obstruction search.
check-referenced-statements
Validate externally referenced theorems by querying arXiv theorem search first and Codex's built-in web search second. Use when a markdown proof cites statements from external papers.
verify-sequential-statements
Verify a markdown proof in the order it is written. Use when the task is to check local correctness, theorem applicability, and reasoning gaps statement by statement through a paper-style proof.
construct-counterexamples
Construct candidate counterexamples to test a proposed conjecture, lemma, or intermediate claim by keeping the assumptions true while making the claimed conclusion fail. Use when a proposed conjecture/claim feels fragile or unproved, or when you are stuck in reasoning and want to see where the assumptions take effect…
construct-toy-examples
Generate and analyze simpler examples that satisfy both the assumptions and the conclusion of a theorem statement or subgoal. Use when you are stuck in reasoning and need simpler examples to regain traction, or when you want to see where the assumptions take effect and gain intuition.
obtain-immediate-conclusions
Derive immediate mathematical consequences from a theorem statement or subgoal. Use when starting a new problem, branch, or subgoal, or when cheap progress or a cleaner reformulation is needed before deeper proof search.