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-ai-modelnpx skills add codekiln/logseq-encode-garden --skill logseq-ai-modelgit clone --depth 1 https://github.com/codekiln/logseq-encode-gardenWhat 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 | $0.00094 | $0.00473 |
| Opus 5 | $0.00047 | $0.00236 |
| Sonnet 5 | $0.00019 | $0.00095 |
| Haiku 4.5 | $0.00009 | $0.00047 |
Grade A, and why
logseq-ai-model 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 3d 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.
- 3d ago First seen · 33 lines · 94 tokens per session scan A a37eef270be3
logseq-ai-model is a skill published in the GitHub repository codekiln/logseq-encode-garden (10 stars, last pushed 6d ago), with no licence file. It adds 94 tokens to every session and 473 once invoked, about $0.0005 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.
Other skills, from other repositories
query
Search compiled LLM Wiki pages, synthesize a cited answer, save substantial results, and feed durable gaps back into the Wiki; also responds to /query and 위키 질의.
prompts
Manage dormant atomic prompt pages — search, promote to active slash commands, and demote back to dormant. USE WHEN search prompts, find a prompt, promote prompt, activate prompt, demote prompt, deactivate prompt, browse prompt library, show me prompts.
llm-wiki
Build and maintain a Karpathy-style LLM knowledge wiki. Run INGEST (turn a raw note, forwarded post, or URL into a detailed source file plus a cross-linked knowledge page), QUERY (answer from the wiki and save good answers as new pages), and LINT (health-check the wiki for broken links, orphans, contradictions, stale…
bigquery-ai-ml
Leverages BigQuery's built-in machine learning and GenAI capabilities for advanced data analytics. Use when you need to write SQL queries that perform time-series forecasting, predict values, detect outliers or anomalies, find key drivers, perform semantic search or vector search, classify text, calculate similarity…
ondb
A logical analysis and reasoning tool for AI. Use when decomposing documents into structured knowledge, querying entities and relations, validating consistency, or indexing files. Trigger on "remember", "what do I know about", "link X to Y", "show dependencies", "analyze this document", entity CRUD, or cross-skill…
neuron-evaluation-engineer
Create and run AI evaluations with datasets, assertions, and output drivers in Neuron AI. Use this skill whenever the user mentions evaluation, testing AI systems, creating evaluators, dataset-driven testing, assertion-based validation, or wants to measure AI system performance. Also trigger for tasks involving…