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/slbug/claude-ruby-grape-rails/challengenpx skills add slbug/claude-ruby-grape-rails --skill challengegit clone --depth 1 https://github.com/slbug/claude-ruby-grape-railsWrote 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/slbug/claude-ruby-grape-rails/challenge)<a href="https://agentmods.dev/skills/slbug/claude-ruby-grape-rails/challenge"><img src="https://agentmods.dev/badge/skills/slbug/claude-ruby-grape-rails/challenge.svg" alt="Measured on agentmods" 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 | $0.00033 | $0.01412 |
| Opus 5 | $0.00016 | $0.00706 |
| Sonnet 5 | $0.00007 | $0.00282 |
| Haiku 4.5 | $0.00003 | $0.00141 |
Grade A, and why
rb:challenge 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 3d 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 — 176 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Challenge Mode Reviews
Rigorous, critical review patterns. Push beyond first solutions to ensure quality.
Iron Laws - Never Violate These
- No approval without verification - Don't approve until all concerns addressed
- Assume bugs exist - Look for edge cases, race conditions, missing handlers
- Question everything - Even "obvious" code can hide issues
- Demand proof - Ask for tests, show state transitions, verify behavior
Adversarial Lenses (Apply to ALL Modes)
Before diving into mode-specific checks, apply these four lenses:
- "What Would Break This?" — Describe realistic scenarios where this code fails catastrophically. Not edge cases — production failure modes under load, during deploys, with unexpected data.
- "Assumption Stress Test" — List every assumption this code relies on. Which are most fragile? (e.g., "assumes user always has an email", "assumes this query returns < 1000 rows")
- "Contradictions Finder" — Find contradictions between tests and implementation, docs and behavior, or between different parts of the code changes.
- "Overcomplicated?" — For every block of code, ask: would a senior engineer say this is overcomplicated? If you write 200 lines and it could be 50, that's a finding.
Challenge Modes
Active Record Challenge (/rb:challenge active record)
Grill the developer on database changes:
Migration Safety
- Will this migration lock the table in production?
- What happens to existing records without the new field?
- Is the migration reversible?
- Are there any unsafe operations (column removal, type change)?
Query Performance
- Have you introduced any N+1 queries?
- Are there missing indexes for new WHERE clauses?
- Will this query scale with data growth?
Schema Integrity
- Are all constraints enforced at database level?
- What happens during rolling deployment (old code, new schema)?
- Are foreign key cascades correct?
Backward Compatibility
- Will old code work during deployment?
- Are there any breaking changes to the model API?
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.
- 3d ago First seen · 176 lines · 33 tokens per session scan A 5fa18451c510
rb:challenge is a skill published in the GitHub repository slbug/claude-ruby-grape-rails (7 stars, last pushed 4d ago), licensed MIT. It adds 33 tokens to every session and 1,412 once invoked, about $0.0002 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
idea-discovery
Workflow 1: Full idea discovery pipeline to go from a broad research direction to validated, pilot-tested ideas. Use when user says "找idea全流程", "idea discovery pipeline", "从零开始找方向", or wants the complete idea exploration workflow.
goai
GoAI is a Go SDK for AI applications. One unified API across 25+ LLM providers. Inspired by the Vercel AI SDK, adapted to Go idioms (generics, interfaces, channels).
rust-project-validate
运行 cargo fmt/clippy/build/e2e/test 验证项目规范;在每次任务结束前必须调用,重要的是必须在端到端测试之后执行,为提交代码做最后准备.
python-services
Python patterns for CLI tools, async concurrency, and backend services. Use when working with Python code, building CLI apps, FastAPI services, async with asyncio, background jobs, or configuring uv, ruff, ty, pytest, or pyproject.toml.
ts-review
TypeScript monorepo 审查:XSS、SQL 注入、密钥、any、console.log.
ai-launch-playbook
Launch your AI product to global attention — the playbook behind Manus, Devin, and AFFiNE's breakout launches. Covers AI-specific GTM strategy, hype cycle management, waitlist tactics, and multi-market rollout for maximum day-one impact.