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 beita6969/ScienceClaw --skill post-processinggit clone --depth 1 https://github.com/beita6969/ScienceClawWrote 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/beita6969/scienceclaw/post-processing)<a href="https://agentmods.dev/skills/beita6969/scienceclaw/post-processing"><img src="https://agentmods.dev/badge/skills/beita6969/scienceclaw/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/beita6969/scienceclaw/post-processing"><img src="https://agentmods.dev/badge/skills/beita6969/scienceclaw/post-processing.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00045 | $0.02072 |
| Opus 5 | $0.00023 | $0.01036 |
| Sonnet 5 | $0.00009 | $0.00414 |
| Haiku 4.5 | $0.00005 | $0.00207 |
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 9d 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 — 339 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
-
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 |
Workflow
1. Data Inventory
First, understand what data is available:
# List available fields and timesteps
python scripts/field_extractor.py --input results/ --list --json
2. Field Extraction
Extract spatial field data at specific timesteps:
# Extract concentration field at timestep 100
python scripts/field_extractor.py \
--input results/field_0100.json \
--field concentration \
--json
# Extract multiple fields
python scripts/field_extractor.py \
--input results/field_0100.json \
--field "phi,concentration,temperature" \
--json
What ships with it
11 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.
- references/comparison_metrics.md 4.0 KB
- references/data_formats.md 2.3 KB
- references/derived_quantities_guide.md 4.3 KB
- references/statistical_methods.md 3.3 KB
- scripts/comparison_tool.py 14 KB runs code
- scripts/derived_quantities.py 18 KB runs code
- scripts/field_extractor.py 12 KB runs code
- scripts/profile_extractor.py 14 KB runs code
- scripts/report_generator.py 16 KB runs code
- scripts/statistical_analyzer.py 15 KB runs code
- scripts/time_series_analyzer.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.
- 9d ago First seen · 339 lines · 45 tokens per session scan A 36f948528a4a
post-processing is a skill published in the GitHub repository beita6969/ScienceClaw (898 stars, last pushed 3mo ago), licensed MIT. It adds 45 tokens to every session and 2,072 once invoked, about $0.0002 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 2026-09-03.
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