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/pre-review.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/pre-review)<a href="https://agentmods.dev/commands/mickeytony0215-png/obsidian-llm-wiki/pre-review"><img src="https://agentmods.dev/badge/commands/mickeytony0215-png/obsidian-llm-wiki/pre-review/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/pre-review"><img src="https://agentmods.dev/badge/commands/mickeytony0215-png/obsidian-llm-wiki/pre-review.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 | $0.00045 | $0.01383 |
| Opus 5 | $0.00023 | $0.00691 |
| Sonnet 5 | $0.00009 | $0.00277 |
| Haiku 4.5 | $0.00005 | $0.00138 |
Grade A, and why
pre-review 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 — 93 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/pre-review — batch pre-audit (a throughput part for the review gate)
Runs the automatable part of /review-note (load evidence, decompose claims, claim-by-claim PDF check, logical-consistency check, data-credibility red flags) ahead of time, in batch, and caches the result inside each note. When the user later runs /review-note, it starts directly from a confirmation checklist instead of from scratch — cutting the per-paper human review time from roughly 30–40 minutes down to roughly 10.
Hard rules (gate semantics never change)
- Never modify
review_status/reviewed_date/reviewed_by/review_notes. After/pre-reviewruns, the note is stillunreviewedand/ingest-to-wikistill hard-blocks it. - The pre-review report is draft evidence, not a review verdict. Only the user, confirming item-by-item in
/review-note, can changereview_status. - Do not touch any existing content in the note body — only add or update the
## Pre-review reportsection.
Triggers
/pre-review— auto-selects the top 3 notes (by mtime, newest first) from the core tier of the triage queue (see/read-paper's triage rules andwiki/pending-review.md)/pre-review <N>— take the top N from the core tier/pre-review <note> [<note>...]— target specific notes (any triage tier)
Procedure (process one note fully before starting the next)
Step 1: Load PDF evidence (same as /review-note step 1)
- Read the note and locate its paired PDF (via frontmatter or a filename match under
raw/papers/). - Load full-text evidence. If a chunk-level retrieval backend (see
/read-paperstep 0 //paper-ask) is available, prefer it — its evidence is page-anchored and its tables are pre-structured, which is more reliable than re-reading the PDF by eye.- Backend not available and the user doesn't want to bring it up: skip this note and report it (the value of pre-review is chunk-level comparison; a degraded pre-review defeats the purpose) — do not silently fall back to a raw PDF read.
- PDF not found: skip the note and report it under "could not pre-review."
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 · 93 lines · 45 tokens per session scan A 4fa58e9d3143
pre-review is a command published in the GitHub repository mickeytony0215-png/obsidian-llm-wiki (2 stars, last pushed 2mo ago), licensed MIT. It adds 45 tokens to every session and 1,383 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.