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 bytesagain/ai-skills --skill ankigit clone --depth 1 https://github.com/bytesagain/ai-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/bytesagain/ai-skills/anki)<a href="https://agentmods.dev/skills/bytesagain/ai-skills/anki"><img src="https://agentmods.dev/badge/skills/bytesagain/ai-skills/anki/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/bytesagain/ai-skills/anki"><img src="https://agentmods.dev/badge/skills/bytesagain/ai-skills/anki.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 | $0.00053 | $0.00217 |
| Opus 5.5 | $0.00021 | $0.00087 |
| Sonnet 5.5 | $0.00011 | $0.00043 |
| Haiku 4.5 | $0.00005 | $0.00022 |
Grade A, and why
anki 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
No licence file
A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.
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.
- 1mo ago First seen · 29 lines · 53 tokens per session scan A ad7650e0a2fa
anki is a skill published in the GitHub repository bytesagain/ai-skills (15 stars, last pushed 5mo ago), with no licence file. It adds 53 tokens to every session and 217 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-08-30.
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deep-student-tauri-lab
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card-coordinator
A workflow coordinator for creating and checking a set of study cards through several specialised agents. It passes work from content selection to generation, quality checking, and corrections.
card-qa-checklist
A checklist of rules for checking catalogue cards, maintained for a card-quality review sub-agent. The input does not explain the individual checks.
card-outline
A method for turning learning material into a structured outline for study cards. It identifies the smallest useful pieces of knowledge, chooses card types, sets the right amount of detail, and orders priorities.
generate-topic
Generate a complete topic: research → lesson → post-process → verify. Fans out subagents for research and verification. Trigger: generate topic, generate lesson, complete topic, full generation.