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/paper-ask.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/paper-ask)<a href="https://agentmods.dev/commands/mickeytony0215-png/obsidian-llm-wiki/paper-ask"><img src="https://agentmods.dev/badge/commands/mickeytony0215-png/obsidian-llm-wiki/paper-ask/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/paper-ask"><img src="https://agentmods.dev/badge/commands/mickeytony0215-png/obsidian-llm-wiki/paper-ask.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 | $0.00033 | $0.00700 |
| Opus 5 | $0.00016 | $0.00350 |
| Sonnet 5 | $0.00007 | $0.00140 |
| Haiku 4.5 | $0.00003 | $0.00070 |
Grade B, and why
paper-ask scanned grade B with 1 finding 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.
Reads agent configuration directoriesmediumAgent snooping
.claude/, .codex/, .gemini/ hold keys, settings and other credentials a mod has no legitimate need for.
> **This repo ships the command spec only.** The retrieval backend is intentionally not bundled — see [RAGFlow](https://github.com/infiniflow/ragflow) for the reference implementation this spec was extracted from. Fork u How it starts
The opening of the file, as written. The whole thing — 32 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/paper-ask — ask a question of the whole paper library
This repo ships the command spec only. The retrieval backend is intentionally not bundled — see RAGFlow for the reference implementation this spec was extracted from. Fork users either stand up their own self-hosted retrieval stack (RAGFlow or an equivalent chunked-PDF index) or swap this command for a simpler
pdftotext+ grep flow..claude/scripts/ragflow_*.py(ingest / chunk-dump helpers) are local infrastructure and are not shipped, consistent with this repo's spec-only stance on scripts (see/search-vault).
Grounded Q&A over the full-text index of every PDF under raw/papers/. Use it for:
- Related-work drafting: "Which papers use method X for problem Y? How do their designs differ?"
- Finding a specific number: "Which papers report a latency measurement for Z?"
- Cross-checking: "Do these three papers' baselines overlap?"
- Recall by content, not by filename: "Which paper discussed the multi-objective optimization for RSU placement?"
Procedure
- Retrieve. Query the corpus-wide retrieval tool (e.g. a RAGFlow MCP
retrievalcall, or the equivalent HTTP/CLI endpoint of whatever local stack is standing in for it). If one phrasing doesn't surface enough evidence, retry with 2–3 different phrasings (synonyms, alternate terminology, another language) before concluding the corpus has nothing.- Retrieval backend not running: tell the user how to start it, then stop — do not silently fall back to un-grounded answers.
- Answer, grounded only in retrieved evidence:
- Answer strictly from what the retrieved chunks contain. If they don't cover the question, say so explicitly ("not found in the corpus") — never fill the gap from the model's own training knowledge without clearly labeling it as "supplementary, outside the corpus."
- Every claim carries its source: paper filename/slug + page number, taken from the retrieval evidence's own page metadata.
- Quote numeric data as it appears in the evidence — do not round or paraphrase figures.
- Scope reminder. The corpus only covers PDFs that have actually been ingested. If the user asks about a paper that clearly isn't indexed (no hits, and no matching file under
raw/papers/), suggest/read-paperor the ingest script to add it first.
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 · 32 lines · 33 tokens per session scan B e65db94b4883
paper-ask is a command published in the GitHub repository mickeytony0215-png/obsidian-llm-wiki (2 stars, last pushed 2mo ago), licensed MIT. It adds 33 tokens to every session and 700 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it B with 1 finding (reads agent configuration directories). 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.