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Fair Simulation Packager

Community
HeshamFS
fair-simulation-packager

Create FAIR-minded reproducibility bundles for materials simulations by collecting input/output file inventories, hashes, units, engine versions, structure identifiers, provenance, and NOMAD/OPTIMADE/Materials Project friendly metadata. Use before publishing, sharing, archiving, or handing simulation results to another agent.

Overview

PublisherHeshamFS
Repositorymaterials-simulation-skills
Skill namefair-simulation-packager
Stars
83
Forks
6
Bundled files
9
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 9 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by HeshamFS on GitHub. Read the source before you install it.

Installation

Install the Fair Simulation Packager AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/HeshamFS/materials-simulation-skills/tree/main/skills/data-management/fair-simulation-packager
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/HeshamFS/materials-simulation-skills.git /tmp/materials-simulation-skills
mkdir -p .claude/skills
cp -r /tmp/materials-simulation-skills/skills/data-management/fair-simulation-packager .claude/skills/fair-simulation-packager
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Fair Simulation Packager in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Fair Simulation Packager on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Fair Simulation Packager is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

FAIR Simulation Packager

Goal

Build a minimal reproducibility manifest for materials simulation results so another person or agent can understand what was run, with which inputs, and how outputs should be interpreted.

Requirements

  • Python 3.10+
  • No external dependencies
  • Works on Linux, macOS, and Windows

Inputs to Gather

InputDescriptionExample
Project nameHuman-readable bundle nameal-cu-diffusion-study
EngineSimulation codeLAMMPS, VASP, MOOSE
Input filesFiles needed to rerunin.lammps,data.lmp
Output filesFiles needed to verify resultslog.lammps,traj.dump
Structure IDDatabase or local identifiermp-149
UnitsField/unit mappingenergy=eV,length=angstrom

Decision Guidance

  • Always include input files, output files, code version, and units.
  • Include hashes for every file that exists locally.
  • Include structure identifiers when using Materials Project, NOMAD, OPTIMADE, CIF, POSCAR, or internal database records.
  • Record missing files as warnings instead of silently dropping them.

Script Outputs

scripts/fair_packager.py --json prints an envelope with two top-level keys:

  • inputs: the parsed CLI arguments echoed back (note: inputs.inputs is the raw comma-separated string passed via --inputs, not the per-file records).
  • results: contains a single key manifest.

results.manifest contains these fields:

  • project_name, engine, engine_version, structure_id, units
  • file_inventory with inputs and outputs, each a list of per-file records (path, exists, and when the file exists, size_bytes and sha256)
  • missing_files: paths that do not exist on disk
  • provenance (working_directory, manifest_schema)
  • fair_checks: has_inputs, has_outputs, has_units, has_engine_version, and has_hashes_for_existing_files (true/false, or null when no files exist so the check is not applicable)
  • recommended_next_steps

Parse fields at results.manifest.<field>. When --out PATH is given, only the bare manifest object (not the inputs/results envelope) is written to PATH.

Workflow

bash
python3 skills/data-management/fair-simulation-packager/scripts/fair_packager.py \
  --project-name al-cu-diffusion \
  --engine LAMMPS \
  --inputs in.lammps,data.lmp \
  --outputs log.lammps,traj.dump \
  --units energy=eV,length=angstrom,time=ps \
  --structure-id local:alcu-cell-001 \
  --json

Use --out manifest.json only when the user wants a manifest file written.

Error Handling

Missing files are reported in missing_files (exit code 0). Invalid input stops with exit code 2: malformed unit entries, fields containing control characters, fields longer than 4096 characters, more than 1000 input or output entries, or a file larger than 500 MB. Note that ordinary file paths — including absolute paths and paths containing .. — are accepted and inventoried; the tool reads file contents only to compute metadata and SHA-256 hashes.

Limitations

This skill creates a metadata manifest. It does not upload to NOMAD, Materials Project, or an OPTIMADE provider.

Verification checklist

  • Confirmed fair_checks.has_hashes_for_existing_files is true (not null or false); a null means no listed file existed on disk, so re-run from the directory where the files actually live before trusting the manifest.
  • Reviewed missing_files and confirmed it is empty, or recorded an explicit reason for each entry — every missing path means an input/output was named but not hashed, so the bundle is not reproducible as-is.
  • Spot-checked at least one sha256 in results.manifest.file_inventory against an independent sha256sum/Get-FileHash of the same file to confirm the recorded digest matches the bytes on disk.
  • Confirmed engine_version is a real version string and not the default "unknown"; pass --engine-version so the bundle records the exact code build.
  • Verified units is non-empty and every reported quantity (energy, length, time, etc.) has an entry, so downstream consumers do not have to guess the unit system.
  • Confirmed structure_id is populated when a Materials Project / NOMAD / OPTIMADE / CIF / POSCAR structure was used, so the structure identity is recoverable.
  • When --out PATH was used, opened PATH and confirmed it holds the bare manifest object (not the inputs/results envelope) and that no unintended path outside the working directory was written.

Common pitfalls & rationalizations

Tempting shortcutWhy it's wrong / what to do
"It exited 0, so the bundle is complete."Exit 0 only means no validation error; missing files are reported in missing_files with exit 0. Inspect missing_files and fair_checks before trusting completeness.
"has_hashes_for_existing_files is null, close enough to true."null means no listed file existed on disk — nothing was hashed. Re-run from the correct directory so the files are found and actually digested.
"Skip --engine-version, the engine name is enough."The script defaults engine_version to "unknown", which silently breaks reproducibility. Always pass the concrete build/version.
"Parsing inputs.inputs from the JSON gives me the per-file records."inputs.inputs is the raw comma-separated CLI string echoed back, not the records. Read per-file data from results.manifest.file_inventory.inputs/outputs.
"The manifest captures the structure, so the run is fully reproducible."This skill only inventories files, hashes, units, and IDs. It does not record code commit, container digest, or parser versions — follow recommended_next_steps and add those yourself.
"Absolute paths or .. will be rejected, so the inventory is sandboxed."Paths are not sandboxed — absolute and .. paths are inventoried as given, and --out can write outside the working directory. Verify the paths you pass are the intended ones.

Security

Input Validation

  • --project-name and --engine are required and must be non-empty after stripping whitespace; an empty value stops with exit code 2.
  • --project-name, --engine, --structure-id (when given), and every file path are checked for control characters (ord < 32) and a 4096-character maximum length; either condition stops with exit code 2.
  • --inputs and --outputs are split on commas and capped at 1000 entries each; more entries stop with exit code 2.
  • --units entries must be key=value; both the key and the value must match the allowlist ^[A-Za-z0-9_.:/@+-]+$. A missing = or a non-matching key/value stops with exit code 2.
  • There are no numeric inputs, so no finite/positive numeric checks are performed.

File Access

  • The script reads each existing input/output file in 1 MB chunks to compute its size_bytes and SHA-256 hash; files that do not exist are recorded but not read.
  • A file larger than 500 MB stops with exit code 2 before it is hashed.
  • The script writes no files unless --out PATH is given, in which case it writes exactly one file (the bare manifest JSON) to that path; otherwise all output goes to stdout.
  • Paths are not sandboxed: absolute paths and paths containing .. are accepted for both the inventoried files and --out. If --out points outside the working directory, the file is written there as requested.

Tool Restrictions

  • Bash is used to run the bundled scripts/fair_packager.py.
  • Read, Write, Grep, and Glob are declared so the skill can inspect the working directory and read or write manifest/metadata files when guiding the user; they are not invoked by the script itself, which performs all of its own file I/O directly.

Safety Measures

  • No eval, exec, os.system, or subprocess calls; the script does not shell out and parses arguments with argparse.
  • Output is emitted as JSON via json.dumps (or a short plain-text summary without --json).
  • DoS caps bound resource use: 500 MB per file, 1000 entries per input/output list, and 4096 characters per field.

References

  • See references/fair_manifest.md for recommended manifest fields.

Version History

  • 1.2.0: Made eval cases discriminating by pinning the script's exact --json output (per-file sha256/size_bytes, units, structure_id, engine_version, missing_files, and the tri-state has_hashes_for_existing_files) against committed evals/files/ fixtures.
  • 1.1.0: Documented the real --json envelope shape; made has_hashes_for_existing_files tri-state (null when no files exist); added entry-count (1000) and field-length (4096) caps; corrected the Error Handling and Security wording to describe actual path behavior.
  • 1.0.0: Initial FAIR simulation packaging skill.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Fair Simulation Packager AI skill do?

Create FAIR-minded reproducibility bundles for materials simulations by collecting input/output file inventories, hashes, units, engine versions, structure identifiers, provenance, and NOMAD/OPTIMADE/Materials Project friendly metadata. Use before publishing, sharing, archiving, or handing simulation results to another agent.

Why use Fair Simulation Packager on TypingMind?

Because you install it once and use it with any model. Fair Simulation Packager is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Fair Simulation Packager in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/HeshamFS/materials-simulation-skills/tree/main/skills/data-management/fair-simulation-packager. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Fair Simulation Packager?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Fair Simulation Packager?

As many as you like. As long as a model supports skills, you can use Fair Simulation Packager with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Fair Simulation Packager AI skill free?

Yes. It is published on GitHub by HeshamFS under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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