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 ariffazil/AAA --skill apex-theorygit clone --depth 1 https://github.com/ariffazil/AAAWrote 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/ariffazil/aaa/apex-theory)<a href="https://agentmods.dev/skills/ariffazil/aaa/apex-theory"><img src="https://agentmods.dev/badge/skills/ariffazil/aaa/apex-theory/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/ariffazil/aaa/apex-theory"><img src="https://agentmods.dev/badge/skills/ariffazil/aaa/apex-theory.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 | $0.03124 |
| Opus 5.5 | $0.00000 | $0.01250 |
| Sonnet 5.5 | $0.00000 | $0.00625 |
| Haiku 4.5 | $0.00000 | $0.00312 |
Grade A, and why
apex-theory 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.
The source is not reproduced here
Licensed AGPL-3.0
The repository is licensed AGPL-3.0, which this catalogue does not treat as permission to reproduce the file. Read it at the source.
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 · 316 lines · 0 tokens per session scan A 8f6175fbfa83
apex-theory is a skill published in the GitHub repository ariffazil/AAA (0 stars, last pushed 23d ago), licensed AGPL-3.0. It costs nothing until one of its globs matches a file; then it loads 3,124 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.
Other skills, from other repositories
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omni-compression
Configure RTK (command output), Caveman (prose), and stacked compression modes. Manage language packs, custom rules, and test prompt compression reducing tokens by 60–90%.
cli-compression
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kermt-pretrain-scratch
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llm-runtime
Integrate, stream, and orchestrate frontier LLM APIs with robust structured outputs, tool call schemas, prompt caching, and resilient backoff handling. Use when implementing AI agent runtimes, tool-calling loops, streaming SSE endpoints, or optimizing token usage and model latency.
prompt-engineering
Use when one prompt must give the same right answer across reruns, models, and pasted-in hostile input: forcing a fixed schema, picking the few-shot set, ordering the prompt blocks, or the inline cases you run while tuning. NOT the agent loop, tools, or retrieval (that is building-agents), NOT a standing CI eval…