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 beita6969/ScienceClaw --skill materials-screeninggit clone --depth 1 https://github.com/beita6969/ScienceClawWrote 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/beita6969/scienceclaw/materials-screening)<a href="https://agentmods.dev/skills/beita6969/scienceclaw/materials-screening"><img src="https://agentmods.dev/badge/skills/beita6969/scienceclaw/materials-screening/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/beita6969/scienceclaw/materials-screening"><img src="https://agentmods.dev/badge/skills/beita6969/scienceclaw/materials-screening.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00050 | $0.00922 |
| Opus 5 | $0.00025 | $0.00461 |
| Sonnet 5 | $0.00010 | $0.00184 |
| Haiku 4.5 | $0.00005 | $0.00092 |
Grade A, and why
materials-screening 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 9d 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 — 97 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Materials Screening (Meta Skill)
This meta-skill orchestrates a computational materials screening pipeline by combining database querying, property-based filtering, structural analysis, and multi-criteria ranking. It coordinates three specialized skills to systematically identify and evaluate candidate materials for target applications.
Workflow
Step 1: Database Search and Candidate Retrieval
Query the Materials Project API to build an initial candidate pool based on application-specific criteria:
- Chemical system constraints (e.g., Li-containing oxides for battery cathodes)
- Space group or crystal system requirements
- Elemental composition filters (include/exclude specific elements)
- Property range pre-filters (band gap, formation energy, density)
Retrieve structural data (CIF files), computed properties, and literature references for each candidate material.
Step 2: Property-Based Filtering
Apply quantitative property thresholds to narrow the candidate pool:
- Electronic: Band gap range for semiconductors, metals, or insulators
- Thermodynamic: Formation energy cutoffs for synthesizability
- Mechanical: Bulk/shear modulus for structural applications
- Physical: Density, volume per atom, coordination preferences
- Magnetic: Magnetic ordering for spintronic applications
Define application-specific filter chains (e.g., for photovoltaics: band gap 1.0-1.8 eV, direct gap preferred, low effective mass).
Step 3: Structure Analysis with Pymatgen
Perform detailed structural characterization on filtered candidates:
- Symmetry analysis: space group verification, site symmetries
- Bonding analysis: coordination environments, bond lengths and angles
- Defect tolerance: vacancy formation energies, anti-site energies
- Surface analysis: slab models, surface energy estimation
- Structural similarity: comparison across candidates using fingerprints
Step 4: Stability Assessment
Evaluate thermodynamic and dynamic stability of remaining candidates:
- Thermodynamic: Energy above the convex hull (Ehull < 25 meV/atom typical)
- Phase stability: Competing phases, decomposition products
- Phonon stability: Check for imaginary frequencies indicating dynamic instability
- Aqueous stability: Pourbaix diagram analysis for electrochemical applications
- Thermal stability: Estimated decomposition temperatures
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.
- 9d ago First seen · 97 lines · 50 tokens per session scan A cb62cdb8b42d
materials-screening is a skill published in the GitHub repository beita6969/ScienceClaw (898 stars, last pushed 3mo ago), licensed MIT. It adds 50 tokens to every session and 922 once invoked, about $0.0003 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 2026-09-03.
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