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 agentmods add skills/bigrapb/concreto/skillnpx skills add Bigrapb/Concreto --skill skillgit clone --depth 1 https://github.com/Bigrapb/ConcretoWhat 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 | $0.00093 | $0.01509 |
| Opus 5 | $0.00046 | $0.00754 |
| Sonnet 5 | $0.00019 | $0.00302 |
| Haiku 4.5 | $0.00009 | $0.00151 |
Grade A, and why
concreto 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 2d 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 — 166 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Advanced design intelligence for production-grade frontend interfaces. Deterministic rules, ML-enhanced detection, and comprehensive design guidance.
Setup
REQUIRED BEFORE PROCEEDING:
- Run
node {{scripts_path}}/context.mjsonce per session. If you've seen its output, skip. The script outputs PRODUCT.md (and DESIGN.md when present). If output reportsNO_PRODUCT_MD, stop and runinitbefore continuing. - If user invoked a sub-command, read
reference/<command>.mdnext. Non-optional. The reference defines the command flow. - Read at least one project file (CSS/tokens/theme/component). Required even with a sub-command reference loaded. Use existing patterns.
- Read the matching register reference:
brand(marketing, landing, portfolio) →reference/brand.mdproduct(app UI, dashboard, tool) →reference/product.mdPick by: task cue → surface in focus →registerin PRODUCT.md.
- If no committed brand colors exist, run
node {{scripts_path}}/palette.mjsfor color seed and composition guidance. Use OKLCH throughout.
Core Principles
Production-Grade Output
Ship complete, polished implementations. Not prototypes. Not starting points. Every page, component, and interaction is battle-tested.
The Test: Would You Sign It?
If you wouldn't put your name on the code, it's not done. This means:
- Zero warnings in browser console
- No placeholder content
- Responsive at every breakpoint
- Keyboard navigable
- Screen reader tested
Design Rules
Color
[A01-CRITICAL] Verify contrast ≥4.5:1 for body text
[A02-CRITICAL] Verify contrast ≥3:1 for large text
[V01-HIGH] Never gray text on colored backgrounds
[V02-MEDIUM] Tint neutrals toward brand hue, not warmth-default
[V03-LOW] Document color strategy before picking colors
Color Strategy Commitment Axis:
- Restrained: Tinted neutrals + one accent ≤10%
- Committed: One color covers 30-60%
- Full Palette: 3-4 named roles
- Drenched: Surface IS the color
What ships with it
3 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.
- 2d ago First seen · 166 lines · 93 tokens per session scan A 7c4ca2e81ae7
concreto is a skill published in the GitHub repository Bigrapb/Concreto (2 stars, last pushed 2mo ago), licensed MIT. It adds 93 tokens to every session and 1,509 once invoked, about $0.0005 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.
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