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 TheBeardedBearSAS/claude-craft --skill kiss-dry-yagnigit clone --depth 1 https://github.com/TheBeardedBearSAS/claude-craftWrote 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/thebeardedbearsas/claude-craft/kiss-dry-yagni)<a href="https://agentmods.dev/skills/thebeardedbearsas/claude-craft/kiss-dry-yagni"><img src="https://agentmods.dev/badge/skills/thebeardedbearsas/claude-craft/kiss-dry-yagni/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/thebeardedbearsas/claude-craft/kiss-dry-yagni"><img src="https://agentmods.dev/badge/skills/thebeardedbearsas/claude-craft/kiss-dry-yagni.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 | $0.00000 | $0.00151 |
| Opus 5 | $0.00000 | $0.00076 |
| Sonnet 5 | $0.00000 | $0.00030 |
| Haiku 4.5 | $0.00000 | $0.00015 |
Grade A, and why
kiss-dry-yagni 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 8d 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.
What it actually says
Principes KISS, DRY, YAGNI
This skill provides simplicity and code quality guidelines.
See @REFERENCE.md for detailed documentation.
Quick Reference
- KISS: Methods < 20 lines, complexity < 10, indent < 3 levels
- DRY: Abstract after 3 occurrences, single source of truth
- YAGNI: Only build what's explicitly required NOW
- Early returns: Prefer guard clauses over nested else
- Composition: Prefer over inheritance
What ships with it
1 file 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.
- 8d ago First seen · 20 lines · 0 tokens per session scan A fb2e86418b91
kiss-dry-yagni is a skill published in the GitHub repository TheBeardedBearSAS/claude-craft (105 stars, last pushed yesterday), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 151 tokens. 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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Review static site code for bugs, security issues, performance problems, accessibility gaps, and CLAUDE.md compliance. Enforces pure HTML/CSS/JS standards, minimal page weight, mobile-first design. Use when completing features, before commits, or reviewing changes.
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Review code for quality, bugs, and best practices. Use when asked to review or audit code.
tldr
TLDR code analysis — call graphs, semantic search, impact, dataflow, for far fewer tokens than reading the files raw. Triggers "who calls X", "what affects X", "blast radius", before large file reads or refactors.