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 Bilal140202/the-lord-of-the-skills --skill joonghyun-lee-frieren__oh-my-antigravitygit clone --depth 1 https://github.com/Bilal140202/the-lord-of-the-skillsWrote 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/bilal140202/the-lord-of-the-skills/joonghyun-lee-frieren__oh-my-antigravity)<a href="https://agentmods.dev/skills/bilal140202/the-lord-of-the-skills/joonghyun-lee-frieren__oh-my-antigravity"><img src="https://agentmods.dev/badge/skills/bilal140202/the-lord-of-the-skills/joonghyun-lee-frieren__oh-my-antigravity/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/bilal140202/the-lord-of-the-skills/joonghyun-lee-frieren__oh-my-antigravity"><img src="https://agentmods.dev/badge/skills/bilal140202/the-lord-of-the-skills/joonghyun-lee-frieren__oh-my-antigravity.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 | $0.00577 |
| Opus 5 | $0.00000 | $0.00289 |
| Sonnet 5 | $0.00000 | $0.00115 |
| Haiku 4.5 | $0.00000 | $0.00058 |
Grade A, and why
Joonghyun-Lee-Frieren__oh-my-antigravity 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 12d 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 — 74 lines — stays where its author put it; the contents beside it link to each section on GitHub.
REFACTORED: Integrated interactive selective save functionality into the learn skill.
The extraction process now prioritizes common reusable patterns over simple chat logs.
Default behavior is to save all identified patterns, allowing users to opt into selective saving.
name = "learn" description = "Automatically extract reusable patterns from sessions and save them as learned skills/rules for future use."
Learn Skill
Automatically evaluates OmA sessions to extract reusable patterns (error resolutions, workarounds, conventions) and save them to .omg/rules/learned/.
When to Activate
- Setting up automatic pattern extraction from OmA sessions.
- Configuring the
SessionEndhook for session evaluation. - Reviewing or curating learned skills in
.omg/rules/learned/. - Adjusting extraction thresholds or pattern categories.
How It Works
This skill runs as a SessionEnd hook at the end of each session:
- Session Evaluation: Checks if session has enough messages (default: 10+).
- Pattern Detection: Identifies extractable patterns (errors, workarounds, styles).
- Skill Extraction: Saves useful patterns as new rules in
.omg/rules/learned/.
Interactive Selective Save
When /oma:learn is run, the agent will:
- Identify high-signal reusable patterns.
- List these patterns with unique IDs.
- Ask the user whether to save all or specific ones.
- Default to saving all if not specified.
Extraction Focus
The learn skill focuses on reusable patterns rather than simple chat history:
- Common Error Resolutions: How recurring errors were fixed.
- Environment Workarounds: Fixes for tool or framework quirks.
- Style/Conventions: Project-specific rules identified during work.
- Corrected Behaviors: Mistakes the agent should avoid in the future.
Configuration
Edit .omg/rules/learn.json to customize:
{
"min_session_length": 10,
"extraction_threshold": "medium",
"auto_approve": false,
"learned_skills_path": ".omg/rules/learned/",
"patterns_to_detect": [
"error_resolution",
"user_corrections",
"workarounds",
"debugging_techniques",
"project_specific"
],
"ignore_patterns": [
"simple_typos",
"one_time_fixes",
"external_api_issues"
]
}
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.
- 12d ago First seen · 74 lines · 0 tokens per session scan A 8169706a87d6
Joonghyun-Lee-Frieren__oh-my-antigravity is a skill published in the GitHub repository Bilal140202/the-lord-of-the-skills (4 stars, last pushed 6d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 577 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 2026-08-31.
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