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 workflow-engine-mappergit 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/workflow-engine-mapper)<a href="https://agentmods.dev/skills/heshamfs/materials-simulation-skills/workflow-engine-mapper"><img src="https://agentmods.dev/badge/skills/heshamfs/materials-simulation-skills/workflow-engine-mapper/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/workflow-engine-mapper"><img src="https://agentmods.dev/badge/skills/heshamfs/materials-simulation-skills/workflow-engine-mapper.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 | $0.00068 | $0.02617 |
| Opus 5.5 | $0.00027 | $0.01047 |
| Sonnet 5.5 | $0.00014 | $0.00523 |
| Haiku 4.5 | $0.00007 | $0.00262 |
Grade A, and why
workflow-engine-mapper 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 2d 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 — 156 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Workflow Engine Mapper
Goal
Choose the smallest workflow structure that preserves reproducibility, restartability, and provenance for a materials simulation task.
Requirements
- Python 3.10+
- No external dependencies
- Works on Linux, macOS, and Windows
Inputs to Gather
| Input | Description | Example |
|---|---|---|
| Task | Workflow purpose | VASP relax-static-DOS for 200 structures |
| Code | Main simulation engine | vasp, qe, lammps, ase |
| Runs | Approximate number of calculations | 200 |
| Provenance | Whether audit trail matters | true |
| Restart | Whether jobs may resume after failure | true |
| HPC | Whether remote scheduler is required | true |
Decision Guidance
- Use one-off scripts for fewer than 5 local exploratory runs (no provenance, no HPC).
- Use jobflow/atomate2 when the workflow is Python-native and Materials Project style input sets are useful.
- Use AiiDA when provenance-critical work is also remote (HPC) or large (>= 50 runs) — i.e. long-lived, database-backed campaigns. For smaller local provenance needs, atomate2 (Materials Project codes, >= 10 runs) or jobflow stores already capture inputs, outputs, code version, and environment, so the mapper recommends those instead of the heavier AiiDA stack.
- Use pyiron when interactive atomistic workflows, notebooks, and job management are the primary user surface (ASE/LAMMPS without strict provenance).
The recommendations are emitted in a fixed precedence so the prose and the implemented thresholds agree: an explicit --preferred engine overrides everything; otherwise one-off (small local, no provenance/HPC) -> AiiDA (provenance AND remote/large) -> atomate2 (VASP/QE/CP2K/force-field, >= 10 runs) -> pyiron (ASE/LAMMPS, no provenance) -> jobflow (fallback).
Script Outputs
scripts/workflow_engine_mapper.py emits:
recommended_enginedag_patternprovenance_requirementsrestart_strategystorage_layoutmigration_triggersnotes
What ships with it
4 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.
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.
- 2d ago Changed · +2 lines 595024c4bdcf
- 1mo ago First seen · 154 lines · 68 tokens per session scan A bbed5bf05c7c
workflow-engine-mapper is a skill published in the GitHub repository HeshamFS/materials-simulation-skills (83 stars, last pushed 3d ago), licensed Apache-2.0. It adds 68 tokens to every session and 2,617 once invoked, about $0.0003 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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