Borrowing it
Nothing to install: this file belongs to codekiln/logseq-encode-garden. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/codekiln/logseq-encode-garden/main/.agents/skills/logseq-import-chatgpt-deep-research/SKILL.mdgit 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-import-chatgpt-deep-research)<a href="https://agentmods.dev/skills/codekiln/logseq-encode-garden/logseq-import-chatgpt-deep-research"><img src="https://agentmods.dev/badge/skills/codekiln/logseq-encode-garden/logseq-import-chatgpt-deep-research/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/codekiln/logseq-encode-garden/logseq-import-chatgpt-deep-research"><img src="https://agentmods.dev/badge/skills/codekiln/logseq-encode-garden/logseq-import-chatgpt-deep-research.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 | $0.00112 | $0.00592 |
| Opus 5.5 | $0.00045 | $0.00237 |
| Sonnet 5.5 | $0.00022 | $0.00118 |
| Haiku 4.5 | $0.00011 | $0.00059 |
Grade A, and why
logseq-import-chatgpt-deep-research 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
3 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.
- 1mo ago Changed · +9 lines · +28 tokens per session a514f5c25b5c
- 1mo ago First seen · 41 lines · 84 tokens per session scan A 099c9c5861d2
logseq-import-chatgpt-deep-research is a skill published in the GitHub repository codekiln/logseq-encode-garden (11 stars, last pushed today), with no licence file. It adds 112 tokens to every session and 592 once invoked, about $0.0004 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 2026-08-31.
Other skills, from other repositories
gpu-document-processing
Use when processing large PDFs, document collections, or bulk text extraction tasks that benefit from GPU-accelerated processing. Triggers when the user provides large documents or needs bulk document analysis.
csv-workbench
Analyze CSV files in /mnt/data and return concise numeric summaries.
Create polished PDF files for requests such as PDFs, resumes, CVs, reports, letters, one-pagers, and printable documents.
xberg
Extract text, tables, metadata, and images from 107 document formats (PDF, Office, images, HTML, email, archives, academic) using Xberg. Use when writing code that calls Xberg APIs in Python, Node.js/TypeScript, Rust, or CLI. Covers installation, extraction (sync/async), configuration (OCR, chunking, output format)…
xberg-extracting-tables
Use when extracting tabular data from PDFs, spreadsheets, or images. Covers layout-aware table detection, table model selection, output formats (markdown / JSON cells), and known limits.
format-specific-extraction
Format-specific document extraction workflows.