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/review-note.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/review-note)<a href="https://agentmods.dev/commands/mickeytony0215-png/obsidian-llm-wiki/review-note"><img src="https://agentmods.dev/badge/commands/mickeytony0215-png/obsidian-llm-wiki/review-note/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/review-note"><img src="https://agentmods.dev/badge/commands/mickeytony0215-png/obsidian-llm-wiki/review-note.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 | $0.00034 | $0.02139 |
| Opus 5 | $0.00017 | $0.01069 |
| Sonnet 5 | $0.00007 | $0.00428 |
| Haiku 4.5 | $0.00003 | $0.00214 |
Grade A, and why
review-note 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 — 194 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/review-note — paper-note audit
Audit whether a paper note is consistent with its PDF, so the note can be safely cited by the wiki.
When to use
- Right after
/read-paper, to upgrade the note fromreview_status: unreviewed - When
/ingest-to-wiki's gate blocks you - For re-auditing an existing note after a concern arises
Review depth: full
Not a skim. Every claim is checked against the PDF, with particular attention to:
- Every quantitative datum (table cells, percentages, ms / Mbps / iteration counts)
- Core-claim consistency (motivation, problem, method, results, conclusion)
- Logical consistency (motivation → methodology → experiment → results → conclusion)
- Data credibility (perfect arithmetic progressions, in-text/table contradictions, "every metric is optimal" anti-patterns)
Procedure
Fast path: if the note already has a
## Pre-review reportsection (produced by the optional/pre-reviewbatch command), skip straight to Step 6 and build the Review Checklist from that report — but first spot-check 2–3 of its "verified consistent" items against the evidence (failed spot-check → rerun the pre-review). Also rerun the pre-review if it is more than 30 days old or the note was edited since. After the user confirms, rewrite the## Pre-review reportsection as## Review logper Step 7's format.
Step 1: Load sources
- Read the target paper note (usually a root-level
.md) - Load the PDF's evidence. Prefer a chunk-level retrieval backend if one is available (see
/read-paperStep 0 //paper-ask— not bundled with this repo, spec-only): its evidence is page-anchored and its tables are pre-structured, which is more reliable than a raw re-read. Without one, read the PDF directly (follow thePDF sourcelink in frontmatter or the top-of-note link) and note in the review record that it was "not corroborated by chunk-level retrieval." - If the PDF path is missing, ask the user to confirm the actual location
Step 2: Decompose claims
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 · 194 lines · 34 tokens per session scan A d2dbe3cbae1a
review-note is a command published in the GitHub repository mickeytony0215-png/obsidian-llm-wiki (2 stars, last pushed 2mo ago), licensed MIT. It adds 34 tokens to every session and 2,139 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.