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 materials-lcagit 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/materials-lca)<a href="https://agentmods.dev/skills/sfetni/deep-matter-chem-skills/materials-lca"><img src="https://agentmods.dev/badge/skills/sfetni/deep-matter-chem-skills/materials-lca/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/materials-lca"><img src="https://agentmods.dev/badge/skills/sfetni/deep-matter-chem-skills/materials-lca.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 | $0.00003 | $0.03554 |
| Opus 5 | $0.00002 | $0.01777 |
| Sonnet 5 | $0.00001 | $0.00711 |
| Haiku 4.5 | $0.00000 | $0.00355 |
Grade A, and why
materials-lca 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 — 272 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Materials Life-Cycle Assessment
Description
This skill covers life-cycle assessment (LCA) for materials, chemicals, and manufacturing routes: goal and scope definition, functional units, system boundaries, inventory construction, allocation, impact assessment, uncertainty, scenario analysis, database provenance, prospective LCA, and sustainability-aware materials design. Invoke this skill when comparing environmental burdens of materials or processes, screening candidate routes, or coupling sustainability metrics to materials databases, high-throughput screening, small-data ML, Bayesian optimization, or thermodynamic design workflows.
Domain Context
Life-cycle assessment estimates environmental impacts associated with a product, process, material, or service across a defined life cycle. It is not a single number. An LCA result depends on the goal and scope, functional unit, system boundary, allocation choices, life-cycle inventory, background database, impact assessment method, geography, electricity mix, technology maturity, and assumptions about use phase, end of life, recycling, and co-products.
For materials and chemicals, the functional unit is often the most important modeling decision. "1 kg of material" is rarely enough when materials differ in lifetime, strength, conductivity, catalytic activity, purity, embodied function, or replacement rate. A battery cathode, catalyst, membrane, structural alloy, polymer, solvent, or precursor should be compared on a unit of service whenever possible. The wrong functional unit can reverse conclusions.
LCA is appropriate for structured environmental comparison, hotspot analysis, prospective technology assessment, and sustainability-aware screening. It is not a substitute for techno-economic analysis, process safety analysis, toxicity testing, policy modeling, or full social/environmental justice assessment, although it may provide inputs to those workflows. Impact categories are model-dependent and region-dependent; global warming potential is only one dimension of sustainability. [EXPERT REVIEW NEEDED]
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 · 272 lines · 3 tokens per session scan A 52a1dc8316ad
materials-lca is a skill published in the GitHub repository SFETNI/Deep-Matter-Chem-Skills (6 stars, last pushed 1mo ago), licensed MIT. It adds 3 tokens to every session and 3,554 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-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.
patsnap-current-awareness
Patsnap Current Awareness MCP for AI agents. Tracking system for pharmaceutical industry dynamics and cutting-edge news, covering global medical news search and in-depth news detail mining.