Agent-Skills.md

Agent Skills: Simulation Orchestrator

Orchestrate multi-simulation campaigns including parameter sweeps, batch jobs, and result aggregation. Use for running parameter studies, managing simulation batches, tracking job status, combining results from multiple runs, or automating simulation workflows.

UncategorizedID: HeshamFS/materials-simulation-skills/simulation-orchestrator

Repository

HeshamFS/materials-simulation-skills
HeshamFSLicense: Apache-2.0
262

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pnpm dlx add-skill https://github.com/HeshamFS/materials-simulation-skills/tree/HEAD/skills/simulation-workflow/simulation-orchestrator

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skills/simulation-workflow/simulation-orchestrator/SKILL.md

Skill Metadata

Name
simulation-orchestrator
Description
>

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 |

Note on swept parameter names: sweep_generator.py writes each swept value into the base config by key path. A bare name (e.g. kappa) overwrites a top-level key; a dot-notation name (e.g. parameters.kappa) targets a nested key. The swept key path must match where the solver reads the value — sweeping kappa against a config that nests parameters.kappa would add an unused top-level key and silently leave the base value in place. See references/sweep_strategies.md.

Workflow

Step 1: Generate Parameter Sweep

Create configurations for all parameter combinations:

python3 scripts/sweep_generator.py \
    --base-config base_config.json \
    --params "dt:1e-4:1e-2:5,kappa:0.1:1.0:3" \
    --method linspace \
    --output-dir ./campaign_001 \
    --json

Step 2: Initialize Campaign

Create campaign tracking structure:

python3 scripts/campaign_manager.py \
    --action init \
    --config-dir ./campaign_001 \
    --command "python sim.py --config {config}" \
    --json

Step 3: Track Job Status

Monitor running jobs:

python3 scripts/job_tracker.py \
    --campaign-dir ./campaign_001 \
    --update \
    --json

Step 4: Aggregate Results

Combine results from completed runs:

python3 scripts/result_aggregator.py \
    --campaign-dir ./campaign_001 \
    --metric final_energy \
    --json

result_aggregator.py minimizes by default: best_run is the run with the lowest metric value (and summary.minimize is true). If higher is better (e.g. yield, accuracy, throughput), pass --maximize so best_run becomes the highest value:

# Higher is better -> select the maximum
python3 scripts/result_aggregator.py \
    --campaign-dir ./campaign_001 \
    --metric yield \
    --maximize \
    --json

Decision guidance: If higher is better (yield, accuracy, throughput), pass --maximize; otherwise the reported best_run is the minimum.

CLI Examples

# Generate 5x3=15 runs varying dt (5 values) and kappa (3 values)
python3 scripts/sweep_generator.py \
    --base-config sim.json \
    --params "dt:1e-4:1e-2:5,kappa:0.1:1.0:3" \
    --method linspace \
    --output-dir ./sweep_001 \
    --json

# Generate LHS samples for 4 parameters with budget of 20 runs
python3 scripts/sweep_generator.py \
    --base-config sim.json \
    --params "dt:1e-4:1e-2,kappa:0.1:1.0,M:1e-6:1e-4,W:0.5:2.0" \
    --method lhs \
    --samples 20 \
    --output-dir ./lhs_001 \
    --json

# Check campaign status
python3 scripts/campaign_manager.py \
    --action status \
    --config-dir ./sweep_001 \
    --json

# List jobs (read-only), optionally filtered by status
python3 scripts/campaign_manager.py \
    --action list \
    --config-dir ./sweep_001 \
    --status-filter failed \
    --json

# Get summary statistics from completed runs (minimize: best = lowest)
python3 scripts/result_aggregator.py \
    --campaign-dir ./sweep_001 \
    --metric final_energy \
    --json

# Maximization metric: best = highest value (yield, accuracy, throughput)
python3 scripts/result_aggregator.py \
    --campaign-dir ./sweep_001 \
    --metric yield \
    --maximize \
    --json

Conversational Workflow Example

User: I want to run a parameter sweep on dt and kappa for my phase-field simulation. I want to try 5 values of dt between 1e-4 and 1e-2, and 4 values of kappa between 0.1 and 1.0.

Agent workflow:

  1. Calculate total runs: 5 x 4 = 20 runs
  2. Generate sweep configurations: 
    python3 scripts/sweep_generator.py \
    --base-config simulation.json \
    --params "dt:1e-4:1e-2:5,kappa:0.1:1.0:4" \
    --method linspace \
    --output-dir ./dt_kappa_sweep \
    --json
  3. Initialize campaign: 
    python3 scripts/campaign_manager.py \
    --action init \
    --config-dir ./dt_kappa_sweep \
    --command "python phase_field.py --config {config}" \
    --json
  4. After user runs simulations, aggregate results: 
    python3 scripts/result_aggregator.py \
    --campaign-dir ./dt_kappa_sweep \
    --metric interface_width \
    --json

Error Handling

| Error | Cause | Resolution | |-------|-------|------------| | Base config not found | Invalid file path | Verify base config file exists | | Invalid parameter format | Malformed param string | Use format name:min:max:count or name:min:max | | Output directory exists | Would overwrite | Use --force or choose new directory | | No completed jobs | No results to aggregate | Wait for jobs to complete or check for failures | | Metric not found | Result files missing field | Verify metric name in result JSON |

Integration with Other Skills

The simulation-orchestrator works with other simulation-workflow skills:

parameter-optimization          simulation-orchestrator
        │                              │
        │ DOE samples ────────────────>│ Generate configs
        │                              │
        │                              │ Run simulations
        │                              │
        │<──────────────────────────── │ Aggregate results
        │                              │
        │ Sensitivity analysis         │
        │ Optimizer selection          │

Typical Combined Workflow

  1. Use parameter-optimization/doe_generator.py to get sample points
  2. Use simulation-orchestrator/sweep_generator.py to create configs
  3. Run simulations (user's responsibility)
  4. Use simulation-orchestrator/result_aggregator.py to collect results
  5. Use parameter-optimization/sensitivity_summary.py to analyze

Verification checklist

Before trusting a campaign's best_run or summary statistics, record concrete evidence for each item:

  • [ ] Confirmed the swept key path actually changed the value the solver reads: opened at least one generated config_NNNN.json and verified the swept parameter (e.g. parameters.kappa) holds the expected value at the expected nesting level, not a duplicate unused top-level key (sweep_generator.py writes by key path).
  • [ ] Reconciled job accounting from result_aggregator.py --json: recorded summary.total_jobs, summary.completed, and summary.failed, and confirmed completed + failed == total_jobs. Any shortfall means runs were silently skipped (missing result file or extract_metric returned None) and must be investigated, not ignored.
  • [ ] Confirmed completed > 0 and that the recorded summary.metric matches the field the solver actually writes. A typo'd or absent metric makes extract_metric return None, yielding zero completed runs with no error.
  • [ ] Recorded summary.minimize and confirmed it matches the intended direction (default minimize; --maximize for yield/accuracy/throughput) before quoting best_run.
  • [ ] Did NOT treat job_tracker.py "completed" as physical success: it flags a job completed purely from a result-file's existence and stamps exit_code 0 — independently checked the run's real exit status / solver logs for non-zero codes or NaN/Inf output.
  • [ ] Applied an outlier/sanity check to the metric values (e.g. Tukey 1.5x IQR from references/aggregation_methods.md) and confirmed best_run.value is physically plausible, not a crashed run that emitted a spurious extremum.
  • [ ] For LHS sweeps, recorded the --seed used and saved manifest.json (parameter bounds, total_runs, parameter_space) so the sample set is reproducible.

Common pitfalls & rationalizations

| Tempting shortcut | Why it's wrong / what to do | |-------------------|------------------------------| | "The job tracker says completed, so the run succeeded." | job_tracker.py marks "completed" whenever a result file exists and hard-codes exit_code 0 — it never reads the actual exit code. A crashed run that wrote a partial result file looks identical to a clean one. Check the solver's real exit status and output validity. | | "completed is high, so I have all my results." | Jobs with a missing result file or a metric that extract_metric can't read are silently skipped — neither counted as completed nor failed. Reconcile completed + failed against total_jobs; a gap means lost runs. | | "Aggregation returned a best_run, so that's the optimum." | By default the aggregator minimizes. If higher is better you must pass --maximize, or best_run is the worst point. Always record summary.minimize and confirm the direction. | | "I swept kappa, so the runs vary." | sweep_generator.py writes by key path. If the base config nests the value under parameters.kappa but you sweep the bare name kappa, every config keeps the original nested value and gains an unused top-level key — the sweep is scientifically meaningless. Sweep the exact dotted path the solver reads. | | "The metric name is close enough." | A misspelled or absent metric makes extract_metric return None for every run, so completed is 0 and statistics are empty — with no error raised. Verify the metric matches the solver's output field exactly. | | "Grid covers everything, so use it for all my parameters." | Grid is n^d — it explodes exponentially (4 params x 10 = 10,000 runs). For 4+ dimensions use lhs with a deliberate budget; reserve grid for 1-3 parameters. | | "LHS is random, so I don't need to record anything." | LHS is reproducible only with a fixed --seed. Without recording the seed (and manifest.json), the sample set cannot be regenerated or defended. |

Security

Input Validation

  • Metric names (result_aggregator.py --metric) are validated against [a-zA-Z_][a-zA-Z0-9_.]* to prevent traversal or injection via crafted keys
  • Swept parameter names (sweep_generator.py --params) are validated against [a-zA-Z_][a-zA-Z0-9_]*(.[a-zA-Z_][a-zA-Z0-9_]*)* (dot notation for nested keys); invalid names are rejected
  • campaign_manager.py validates command templates to reject shell chaining operators (;, |, &, backticks, $)
  • --params format strings are parsed and validated (name:min:max:count with finite numeric bounds — NaN/Inf rejected — min < max, and positive integer counts capped at 100,000); at most 32 parameters per sweep
  • --method is validated against a fixed allowlist (grid, linspace, lhs)
  • --samples is validated as a positive integer with an upper bound (max 1,000,000)
  • --action is validated against a fixed allowlist (init, status, list); for the read-only list action, --status-filter is validated against pending, running, completed, failed

File Access

  • sweep_generator.py reads a single base config file (JSON) specified by --base-config and writes generated configs to --output-dir
  • result_aggregator.py enforces a 10 MB file-size limit per result file, maximum JSON nesting depth, and strict numeric type checking (rejects bool, NaN, Inf)
  • All string values from result files are sanitized (truncated, control characters stripped) before surfacing them
  • Config paths interpolated into shell commands are validated against a safe-character allowlist and escaped with shlex.quote()

Tool Restrictions

  • Read: Used to inspect script source, references, base configs, and campaign status files
  • Write: Used to save generated sweep configs, campaign manifests, and aggregated results; writes are scoped to the user's working directory
  • Grep/Glob: Used to locate campaign files, result files, and search references
  • The skill's allowed-tools excludes Bash to prevent the agent from executing arbitrary commands when processing untrusted simulation outputs

Safety Measures

  • No eval(), exec(), or dynamic code generation
  • All subprocess calls use explicit argument lists (no shell=True)
  • Reduced tool surface (no Bash) limits the agent to read/write operations only
  • Command templates are validated but never executed by the skill itself; execution is the user's responsibility

Limitations

  • Not a job scheduler: Does not submit jobs to SLURM/PBS; generates configs and tracks status
  • No parallel execution: User must run simulations externally (can use GNU parallel, SLURM, etc.)
  • File-based tracking: Status tracked via files; no database or real-time monitoring
  • Local filesystem: Assumes all files accessible from local machine

References

  • references/campaign_patterns.md - Common campaign structures
  • references/sweep_strategies.md - Parameter sweep design guidance
  • references/aggregation_methods.md - Result aggregation techniques

Version History

See CHANGELOG.md for the authoritative, dated history. Summary:

  • v1.1.3 (2026-06-24): Added a Verification checklist and a Common pitfalls & rationalizations section grounded in the scripts' real behavior (result-file-only "completed" detection, silent skip of unreadable metrics, minimize-by-default direction, key-path merge semantics)
  • v1.1.1 (2026-06-23): Dot-notation nested overrides in sweep_generator.py, input-validation hardening (--params name/finite/count caps, --samples bounds), documented --maximize and the list action, corrected Script Outputs table and worked-example numbers
  • v1.1.0 (2026-03-26): Standardized metadata, evaluation suite, security review, CHANGELOG
  • v1.0.0 (2026-02-25): Initial release with sweep, campaign, tracking, and aggregation