Search Masoud's Persian writing/editing vault. Returns ranked notes with snippets.

Read a full note from the vault by its id (as returned by search).

The vault's section tree (numbered sections) with note counts.

Rebuild the search index (run after notes are added/edited).

Get the vault's short editorial ruling for a question/doubt.

Searches the vault, takes the best note, and returns its short ruling (پاسخ کوتاه / نکتهٔ ویرایشی / قاعده یا سازوکار) with the note id. Use this as the virtual editor's verdict before finalizing Persian text.

Build the compact «بستهٔ قاعده» checklist for a Persian writing task.

task_type is one of: گزارش رسمی، ایمیل اداری، لندینگ، مقاله، کپشن، نامهٔ اداری، پروپوزال، خبر، مصاحبه، متن وب، پست شبکهٔ اجتماعی، جواب چت. (Any other value is used as a free-form search query.) Runs seed queries through the vault, keeps only status=verified notes, and returns up to max_rules items as «عنوان: حکم کوتاه». This is step 2-3 of the «حالت کامل» pipeline in the Persian OS.

Build the full multi-dimensional checklist for deep Persian editing.

General-purpose (not n8n-specific): any agent calls this once, then runs the deep-edit loop itself —

1. edit the text against checklist (all rules),

2. review the edited text rule-by-rule, list remaining issues as JSON,

3. fix only those issues; repeat 2-3 until clean (max ~4 passes),

4. final read-through for rhythm, typos, native ear. Combines the task-type rule_pack with the five fixed editorial dimensions (نیم‌فاصله، نشانه‌گذاری، جمله، واژه، لحن); dedupes by note id. Returns checklist as a ready-to-paste string plus structured rules.

Diagnose Persian text, then build a tailored deep-edit checklist.

Smarter than deep_rules: instead of a fixed checklist, the text is first scanned for editorial risk signals (long sentences, bureaucratic fossils, Arabic chars, Latin punctuation, ZWNJ issues, quotes, numbers, cliches, repetition, ...). Rules are then pulled for the issues actually present, ordered by signal weight; every rule carries why (the evidence that selected it). Layer 1 is always the task-type rule_pack. General-purpose: any agent runs the edit -> review loop itself.

Deterministic mechanical fix + verification gate for Persian text.

Fixes what needs no judgment (Arabic ي/ك, Latin , ; ? %, straight quotes, ZWNJ on می/نمی, spacing) and proves the result: remaining lists any mechanical issue left, clean is true only when none remain. Use it as the first step (so the LLM works on a clean base) and as the final gate (so nothing ships with mechanical errors). Ambiguous cases (em/en dashes) are flagged in remaining, never guessed.

Objective regression gate: every problem signal diagnosed in the input text must be gone in the output text.

Pure diagnose() comparison, no LLM. Returns resolved/remaining lists and a boolean pass. This is the pipeline's proof of work — "everything we detected, we fixed."

Deterministic change ledger: every span the pipeline changed.

Word-level diff with context. Lets any human audit exactly what the system did to the text, without trusting the model's own account.