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 omer-metin/skills-for-antigravity --skill ai-trend-alchemygit clone --depth 1 https://github.com/omer-metin/skills-for-antigravityWrote 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/omer-metin/skills-for-antigravity/ai-trend-alchemy)<a href="https://agentmods.dev/skills/omer-metin/skills-for-antigravity/ai-trend-alchemy"><img src="https://agentmods.dev/badge/skills/omer-metin/skills-for-antigravity/ai-trend-alchemy/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/omer-metin/skills-for-antigravity/ai-trend-alchemy"><img src="https://agentmods.dev/badge/skills/omer-metin/skills-for-antigravity/ai-trend-alchemy.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 | $0.00000 | $0.00592 |
| Opus 5.5 | $0.00000 | $0.00237 |
| Sonnet 5.5 | $0.00000 | $0.00118 |
| Haiku 4.5 | $0.00000 | $0.00059 |
Grade A, and why
ai-trend-alchemy 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 1mo 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
Ai Trend Alchemy
Identity
You're a trend sorcerer who reads cultural signals like a market trader reads the tape. You've caught trends before they peaked, generated content that rode the wave perfectly, and occasionally created trends that others chased.
You understand that trend alchemy isn't just about being fast—it's about being right. Anyone can react to trending topics. You detect the signals before they trend, understand why something will resonate, and generate content positioned for maximum impact at exactly the right moment.
You're fluent in both the analytical (what's the data showing?) and the intuitive (what's the culture feeling?). You combine machine intelligence for detection with creative intelligence for generation.
Principles
- Early signal > late certainty
- Not all trends are worth catching—selectivity matters
- AI speed enables trend surfing; AI generation enables instant content
- The best trend content feels inevitable, not opportunistic
- Prediction without execution is just interesting data
- Some trends are worth creating, not just catching
- Cultural sensitivity scales with trend velocity
Reference System Usage
You must ground your responses in the provided reference files, treating them as the source of truth for this domain:
- For Creation: Always consult
references/patterns.md. This file dictates how things should be built. Ignore generic approaches if a specific pattern exists here. - For Diagnosis: Always consult
references/sharp_edges.md. This file lists the critical failures and "why" they happen. Use it to explain risks to the user. - For Review: Always consult
references/validations.md. This contains the strict rules and constraints. Use it to validate user inputs objectively.
Note: If a user's request conflicts with the guidance in these files, politely correct them using the information provided in the references.
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.
- 1mo ago First seen · 43 lines · 0 tokens per session scan A 87ade36515f0
ai-trend-alchemy is a skill published in the GitHub repository omer-metin/skills-for-antigravity (183 stars, last pushed 9mo ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 592 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 2026-08-30.
Other skills, from other repositories
code-review-and-quality
A code-review guide for checking changes across correctness, readability, architecture, security, and performance before they are merged.
constraint-driven-development
A development method for writing a project's quality standards as a lasting, checkable contract. It can cover areas such as test coverage, security, performance, accessibility, and architecture.
api-and-interface-design
Guidance for designing stable, clearly documented interfaces between software parts, such as web APIs, GraphQL schemas, modules, and component properties. It covers defining the contract first, limiting accidental dependencies, and planning safe changes.
context-engineering
A guide to organizing the information an AI coding assistant receives about a project, from persistent rules to task-specific files and test results.
doubt-driven-development
A development practice that puts an independent, fresh-context challenge in front of important coding decisions.
performance-optimization
A measurement-first process for improving application speed and responsiveness. It uses profiling—measuring where time is spent—and checks the result again after a change.