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 post-processinggit 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/post-processing)<a href="https://agentmods.dev/skills/heshamfs/materials-simulation-skills/post-processing"><img src="https://agentmods.dev/badge/skills/heshamfs/materials-simulation-skills/post-processing/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/post-processing"><img src="https://agentmods.dev/badge/skills/heshamfs/materials-simulation-skills/post-processing.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 | $0.00130 | $0.05116 |
| Opus 5.5 | $0.00052 | $0.02046 |
| Sonnet 5.5 | $0.00026 | $0.01023 |
| Haiku 4.5 | $0.00013 | $0.00512 |
Grade A, and why
post-processing 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 — 492 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Post-Processing Skill
Analyze and extract meaningful information from simulation output data.
Goal
Transform raw simulation output into actionable insights through field extraction, statistical analysis, derived quantities, visualizations, and comparison with reference data.
Inputs to Gather
Before running post-processing scripts, collect:
-
Output Data Location
- Path to simulation output files (JSON, CSV, HDF5, VTK)
- Time step/snapshot indices of interest
- Field names to extract
Read field names from the file, never assume them. Before extracting, open the output file (or run
field_extractor.py --input <file> --list --json) and use only the field names that actually appear underfields. Do not invent fields such astemperatureif they are not present, and do not assume a grid size — read the realshape/countfrom the data. -
Analysis Type
- Field extraction (spatial data at specific times)
- Time series (temporal evolution of quantities)
- Line profiles (1D cuts through domain)
- Statistical summary (mean, std, distributions)
- Derived quantities (gradients, integrals, fluxes)
- Comparison to reference data
-
Output Requirements
- Output format (JSON, CSV, tabular)
- Visualization needs
- Report format
Scripts
| Script | Purpose | Key Inputs |
|---|---|---|
field_extractor.py |
Extract field data from output files | --input, --field, --timestep |
time_series_analyzer.py |
Analyze temporal evolution | --input, --quantity, --window |
profile_extractor.py |
Extract line profiles | --input, --field, --start, --end |
statistical_analyzer.py |
Compute field statistics | --input, --field, --region |
derived_quantities.py |
Calculate derived quantities | --input, --quantity, --params |
comparison_tool.py |
Compare to reference data | --simulation, --reference, --metric |
report_generator.py |
Generate summary reports | --input, --template, --output |
What ships with it
17 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 4.0 KB
- evals/evals.json 11 KB
- evals/files/analytical.json 181 B
- evals/files/field_output.json 1.8 KB
- evals/files/history.json 553 B
- evals/files/profile.json 192 B
- references/comparison_metrics.md 4.0 KB
- references/data_formats.md 3.3 KB
- references/derived_quantities_guide.md 4.3 KB
- references/statistical_methods.md 4.1 KB
- scripts/comparison_tool.py 14 KB runs code
- scripts/derived_quantities.py 20 KB runs code
- scripts/field_extractor.py 13 KB runs code
- scripts/profile_extractor.py 15 KB runs code
- scripts/report_generator.py 19 KB runs code
- scripts/statistical_analyzer.py 22 KB runs code
- scripts/time_series_analyzer.py 17 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 · 492 lines · 130 tokens per session scan A 2281a65aa468
post-processing is a skill published in the GitHub repository HeshamFS/materials-simulation-skills (74 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 130 tokens to every session and 5,116 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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