Borrowing it
Nothing to install: this file belongs to mickeytony0215-png/obsidian-llm-wiki. 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/mickeytony0215-png/obsidian-llm-wiki/main/.claude/commands/read-paper.mdgit clone --depth 1 https://github.com/mickeytony0215-png/obsidian-llm-wikiWrote 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/commands/mickeytony0215-png/obsidian-llm-wiki/read-paper)<a href="https://agentmods.dev/commands/mickeytony0215-png/obsidian-llm-wiki/read-paper"><img src="https://agentmods.dev/badge/commands/mickeytony0215-png/obsidian-llm-wiki/read-paper/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/commands/mickeytony0215-png/obsidian-llm-wiki/read-paper"><img src="https://agentmods.dev/badge/commands/mickeytony0215-png/obsidian-llm-wiki/read-paper.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 | $0.00038 | $0.01476 |
| Opus 5 | $0.00019 | $0.00738 |
| Sonnet 5 | $0.00008 | $0.00295 |
| Haiku 4.5 | $0.00004 | $0.00148 |
Grade A, and why
read-paper 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 12d 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.
How it starts
The opening of the file, as written. The whole thing — 107 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Read the PDF I specify, then do the following:
Step 0: Retrieve the full text (anti-skim / anti-hallucination)
This repo ships the command spec only. Step 0 describes an optional chunked-retrieval backend (the reference implementation was built against RAGFlow); it is intentionally not bundled — see
/paper-askand/search-vaultfor the same spec-only stance on retrieval infrastructure. Without it, skip straight to the fallback below.
Do not dump the entire PDF into context and read it in one pass — long-context reads are prone to skimming and hallucination. Prefer a retrieval-backed pipeline:
- Confirm the retrieval stack is running; start it if not, and wait for it to become reachable.
- Ingest the PDF (idempotent — a filename already in the index is skipped automatically).
- Dump every chunk in document order, with page numbers and tables as structured HTML, to a scratch file.
- Coverage contract: read the chunk file in batches of ~20–30 chunks, in order, noting the key facts from each batch before moving to the next. Do not write the note after only reading the Abstract/Intro/Conclusion. Before drafting, self-check: "Have all N/N chunks been processed?" — go back and finish if not.
- Treat chunk content as authoritative for numbers and tables (tables are already parsed into structured HTML, more reliable than eyeballing the PDF). In the note's "Main results" section, anchor every key number to its page, e.g.
latency 4.6ms (p.9). - Chunks don't carry figure images — when a figure needs to be seen, read the specific PDF page directly.
- Fallback: if the retrieval backend isn't running and the user doesn't want to start it, fall back to
pdftotext/ reading the PDF directly. In that case leave the note's retrieval-id frontmatter fieldnulland mention in the completion message that the paper can be ingested later.
Step 1: Create the paper note
Create a new paper note at the vault root, with this structure:
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.
- 12d ago First seen · 107 lines · 38 tokens per session scan A 692da264892a
read-paper is a command published in the GitHub repository mickeytony0215-png/obsidian-llm-wiki (2 stars, last pushed 2mo ago), licensed MIT. It adds 38 tokens to every session and 1,476 once invoked, about $0.0002 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 commands, from other repositories
pdf-to-wiki
A workflow for sending a large PDF from Google Drive to NotebookLM, Google's document-analysis tool, and saving the resulting notes as Obsidian markdown files. It passes the file link rather than reading the PDF directly.
wiki-lint
Run a health check on the wiki. Invoke with /wiki-lint or "lint the wiki".
okf
A command that exports a private knowledge wiki into an OKF-compatible bundle. OKF is a format for packaging knowledge, with a separate guarded mode for preparing material to share externally.
ingest
Compile new sources from raw/ into the wiki.
pdf-to-markdown-docling
Convert a local PDF to markdown via Docling's standard pipeline (layout + table-structure recognition — higher fidelity than MarkItDown on complex tables/layouts, 10x slower). Requires the opt-in Docling install.
pdf-to-markdown
Convert a local PDF to markdown via the bundled MarkItDown Python CLI (fast, lightweight — plain text extraction, no table-structure recognition). For complex tables/layouts prefer /obsidian-router:pdf-to-markdown-docling.