Getting it into your agent
There is no command for this one: it runs only inside a plugin, and the catalogue could not identify which plugin ships it. The source is linked below.
Wrote 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/sfetni/deep-matter-chem-skills/aiida-workflow)<a href="https://agentmods.dev/skills/sfetni/deep-matter-chem-skills/aiida-workflow"><img src="https://agentmods.dev/badge/skills/sfetni/deep-matter-chem-skills/aiida-workflow/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/sfetni/deep-matter-chem-skills/aiida-workflow"><img src="https://agentmods.dev/badge/skills/sfetni/deep-matter-chem-skills/aiida-workflow.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 | $0.00004 | $0.14351 |
| Opus 5 | $0.00002 | $0.07175 |
| Sonnet 5 | $0.00001 | $0.02870 |
| Haiku 4.5 | $0.00000 | $0.01435 |
Grade C, and why
aiida-workflow scanned grade C with 1 finding 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 12d 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.
Reaches for credential fileshighPrivilege escalation
SSH keys, cloud credentials, git-credentials, .npmrc, /etc/shadow: reading these is how a config file becomes a credential leak.
# key_filename: /home/user/.ssh/id_rsa_hpc (leave blank for SSH agent) How it starts
The opening of the file, as written. The whole thing — 870 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AiiDA Workflow
Description
AiiDA (Automated Interactive Infrastructure and Database for Computational Science) is a Python-based, provenance-first workflow engine for computational materials science. It manages the complete lifecycle of calculations: input generation, remote HPC job submission via SSH, output retrieval and parsing, automatic error handling and restart, and immutable provenance recording in a directed acyclic graph (DAG) of data and process nodes. Invoke this skill when designing automated, reproducible DFT or AIMD workflows for long-term campaigns, when cross-institutional provenance is required, when workflows must survive HPC failures and restart automatically, or when producing data to be archived to external repositories (NOMAD, Materials Cloud) with machine-readable, verifiable calculation histories.
Domain Context
AiiDA's central design decision is that all data and all processes are nodes in a provenance graph. Every calculation input, every output, and every transformation is stored in a relational database (PostgreSQL or SQLite) as an immutable node. Directed links between nodes record who created what: a CalcJobNode that consumed a StructureData input carries an INPUT_CALC link recording that dependency. The resulting graph is the provenance record. It is impossible to run a calculation and lose its inputs, because all inputs must be stored as nodes before the calculation starts.
This architecture differs fundamentally from atomate2/jobflow and from ad hoc SLURM scripts:
- atomate2/jobflow: Stores results as MongoDB documents; provenance is recorded at the job level, but the full input–output DAG is not rigidly enforced. Setup is faster. Preferred for rapid internal campaigns where throughput matters more than deep provenance or cross-institutional sharing.
- Ad hoc SLURM scripts: No provenance. Results live wherever files land on disk. Restart after failure requires manual intervention. Querying results across a campaign requires writing custom parsers.
- AiiDA: The graph is the primary artifact. Every calculation is traceable to its input structure, pseudopotentials, k-mesh, cutoff, and code version. The archive (
.aiidaformat) is self-contained and importable on any AiiDA installation. The overhead is real: configuring profiles, computers, codes, and the daemon takes several hours for a new HPC resource.
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.
- 12d ago First seen · 870 lines · 4 tokens per session scan C 657b02d984be
aiida-workflow is a skill published in the GitHub repository SFETNI/Deep-Matter-Chem-Skills (6 stars, last pushed 1mo ago), licensed MIT. It adds 4 tokens to every session and 14,351 once invoked, about $0.0000 per session on Opus 5. A static security scan graded it C with 1 finding (reaches for credential files). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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