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 SFETNI/Deep-Matter-Chem-Skills --skill calphad-workflowgit clone --depth 1 https://github.com/SFETNI/Deep-Matter-Chem-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/sfetni/deep-matter-chem-skills/calphad-workflow)<a href="https://agentmods.dev/skills/sfetni/deep-matter-chem-skills/calphad-workflow"><img src="https://agentmods.dev/badge/skills/sfetni/deep-matter-chem-skills/calphad-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/calphad-workflow"><img src="https://agentmods.dev/badge/skills/sfetni/deep-matter-chem-skills/calphad-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 | $0.00005 | $0.03917 |
| Opus 5 | $0.00003 | $0.01959 |
| Sonnet 5 | $0.00001 | $0.00783 |
| Haiku 4.5 | $0.00001 | $0.00392 |
Grade A, and why
calphad-workflow 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 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.
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 — 274 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CALPHAD Workflow
Description
This skill covers CALPHAD workflows for computational thermodynamics and materials design: thermodynamic database selection, equilibrium and metastable calculations, binary and ternary phase diagrams, Scheil solidification, chemical potentials, activities, driving forces, parameter assessment, uncertainty, and reproducible coupling to DFT, experiments, materials databases, precipitation, solidification, diffusion, and phase-field models. Invoke this skill when an agent needs phase stability or thermodynamic inputs from assessed Gibbs-energy models rather than from a single DFT calculation or empirical rule.
Domain Context
CALPHAD, short for CALculation of PHAse Diagrams, represents the Gibbs energy of each phase as a function of temperature, pressure, composition, and internal sublattice degrees of freedom. Equilibria are obtained by minimizing the total Gibbs energy subject to mass balance and phase constraints. A CALPHAD database is not just a table of phase boundaries; it is a set of model functions, parameters, reference states, phase descriptions, and assessed experimental or first-principles data.
The validity of a CALPHAD result is therefore the validity of the database assessment. Results are strongest inside the assessed composition, temperature, pressure, and phase-space domain. Extrapolated ternaries, quaternaries, high-temperature liquids, metastable phases, magnetic transitions, order-disorder models, and non-stoichiometric compounds can be useful but must be treated as database-dependent predictions. [EXPERT REVIEW NEEDED]
CALPHAD complements DFT rather than replacing it. DFT gives 0 K or finite-temperature free-energy contributions for specific configurations; CALPHAD integrates experimental phase equilibria, calorimetry, activities, DFT formation energies, magnetic terms, solution models, and compound-energy formalism into thermodynamic descriptions usable across composition and temperature. Combining them requires consistent reference states, phases, and provenance.
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 · 274 lines · 5 tokens per session scan A c1a7a5af3321
calphad-workflow is a skill published in the GitHub repository SFETNI/Deep-Matter-Chem-Skills (6 stars, last pushed 1mo ago), licensed MIT. It adds 5 tokens to every session and 3,917 once invoked, about $0.0000 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 2026-08-31.
Other skills, from other repositories
datamol
Pythonic wrapper around RDKit with simplified interface and sensible defaults. Preferred for standard drug discovery including SMILES parsing, standardization, descriptors, fingerprints, clustering, 3D conformers, parallel processing. Returns native rdkit.Chem.Mol objects. For advanced control or custom parameters…
smiles-validation
Strict SMILES validation, structural comparison, and modification verification. Catches invalid LLM-generated molecules.
patsnap-biological-modality
Biological sequence and modality intelligence via Patsnap MCP.
patsnap-scientific-translational-evidence
Patsnap Scientific & Translational Evidence MCP for AI agents. Retrieval platform focusing on scientific literature and translational outcomes, covering academic publication queries and translational medicine record tracking.
patsnap-target-disease
Patsnap Target & Disease MCP for AI agents. Target and disease profiling tool, covering target characterization, disease profiling, and epidemiology evidence retrieval.
patsnap-solution-engine
Patsnap TRIZ Concept Solution Engine MCP for AI agents. Generates innovation or product cost-reduction concepts through asynchronous TRIZ and TRIZ/DFMA workflows. Use for engineering problem solving, concept alternatives, cost-reduction analysis, task-progress retrieval, and selected-solution details.