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/codekiln/logseq-encode-garden/logseq-flashcard-favoritenpx skills add codekiln/logseq-encode-garden --skill logseq-flashcard-favoritegit clone --depth 1 https://github.com/codekiln/logseq-encode-gardenWrote 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/codekiln/logseq-encode-garden/logseq-flashcard-favorite)<a href="https://agentmods.dev/skills/codekiln/logseq-encode-garden/logseq-flashcard-favorite"><img src="https://agentmods.dev/badge/skills/codekiln/logseq-encode-garden/logseq-flashcard-favorite.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 | $0.00145 | $0.01184 |
| Opus 5 | $0.00072 | $0.00592 |
| Sonnet 5 | $0.00029 | $0.00237 |
| Haiku 4.5 | $0.00015 | $0.00118 |
Grade A, and why
logseq-flashcard-favorite 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 5d 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
2 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.
- 5d ago First seen · 97 lines · 145 tokens per session scan A 91776e10d1e3
logseq-flashcard-favorite is a skill published in the GitHub repository codekiln/logseq-encode-garden (10 stars, last pushed today), with no licence file. It adds 145 tokens to every session and 1,184 once invoked, about $0.0007 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.
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