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 Abhinavbwj/Skills-Architects --skill material-selectiongit clone --depth 1 https://github.com/Abhinavbwj/Skills-ArchitectsWrote 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/abhinavbwj/skills-architects/material-selection)<a href="https://agentmods.dev/skills/abhinavbwj/skills-architects/material-selection"><img src="https://agentmods.dev/badge/skills/abhinavbwj/skills-architects/material-selection/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/abhinavbwj/skills-architects/material-selection"><img src="https://agentmods.dev/badge/skills/abhinavbwj/skills-architects/material-selection.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 | $0.00026 | $0.09680 |
| Opus 5 | $0.00013 | $0.04840 |
| Sonnet 5 | $0.00005 | $0.01936 |
| Haiku 4.5 | $0.00003 | $0.00968 |
Grade A, and why
material-selection 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 10d 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 — 649 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Material Selection
Comprehensive knowledge base for architectural material selection covering structural performance, durability, thermal and acoustic properties, fire resistance, embodied carbon, cost, and aesthetic quality. Invoke this skill when addressing questions about material specification, material comparison, life-cycle assessment, embodied carbon targets, material detailing, finish selection, or material-appropriate design strategies.
Section 1: Material Selection Methodology
1.1 Eight-Criteria Evaluation Framework
Every architectural material decision should be evaluated against eight performance criteria. Weight each criterion according to project priorities (structural warehouse vs. cultural institution vs. social housing).
Criterion 1: Structural Performance
- Compressive strength (MPa)
- Tensile strength (MPa)
- Elastic modulus (GPa)
- Yield strength and ductility
- Fatigue resistance for dynamic loads
- Span-to-depth ratio capability
- Connection and jointing methods
Criterion 2: Durability and Weathering
- Design life expectation (25, 50, 60, 100+ years)
- Resistance to moisture, freeze-thaw, UV, pollution, biological attack
- Maintenance frequency and cost
- Patina and aging character (graceful vs. degrading)
- EN 206 exposure classes (concrete); EN 350 durability classes (timber)
- Corrosion resistance (metals); efflorescence (masonry)
Criterion 3: Thermal Properties
- Thermal conductivity (λ, W/mK)
- Specific heat capacity (c, J/kgK)
- Thermal mass (decrement delay, admittance)
- Contribution to U-value calculation
- Thermal bridging potential (ψ-values at junctions)
Criterion 4: Acoustic Properties
- Sound absorption coefficient (α) and NRC
- Sound reduction index (Rw, dB)
- Impact sound insulation (Ln,w)
- Flanking transmission paths
- Resonant frequency (for panel absorbers)
Criterion 5: Fire Performance
- Euroclass rating (A1 to F) / ASTM E84 (Class A, B, C)
- Fire resistance period (REI 30 to REI 240)
- Reaction to fire (ignitability, flame spread, smoke production)
- Charring rate (timber: 0.65 mm/min softwood, 0.50 mm/min hardwood)
- Concrete cover requirements for fire rating
- Intumescent and board-based fire protection for steel
What ships with it
2 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.
- 10d ago First seen · 649 lines · 26 tokens per session scan A f9fd5de53bd8
material-selection is a skill published in the GitHub repository Abhinavbwj/Skills-Architects (285 stars, last pushed 3mo ago), licensed MIT. It adds 26 tokens to every session and 9,680 once invoked, about $0.0001 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 2026-08-30.
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