Process a journal page photo: run OCR, parse the template, extract schema
tags, store the capture, copy the image to the knowledge base, and detect
connections to existing captures.

Args:
    image_path: Absolute path to the image file (JPG, PNG, TIFF, etc.)
    force:      Set to True to overwrite an existing capture with the same
                template ID (default False — warns instead).

Returns a summary of what was found and stored, including the strongest
connection detected.

Process all journal page photos in a folder at once.

Finds every image file (JPG, PNG, TIFF, BMP, WebP) in the folder and runs
the full upload pipeline on each one. Non-image files are skipped silently.

Args:
    folder_path: Absolute path to the folder containing journal photos.
    force:       Set to True to overwrite existing captures with matching
                 template IDs (default False — skips duplicates with a warning).

Returns a summary table of all processed images.

Search all journal entries by concept, keyword, or phrase — across every
template type (RC, SYN, REV, DC) at once.

This is the primary way to find entries by idea rather than tag. Use it
whenever the user asks to find notes, recall something they wrote, or
explore a topic. Natural language queries work well.

Examples:
  "neural networks"        → entries mentioning neural networks
  "why does attention"     → entries with that question or phrase
  "spaced repetition"      → concept search across all templates
  "dream flying"           → DC entries with flying imagery

Args:
    query:      The concept, keyword, or phrase to search for.
    tag_filter: Optional tag value to narrow results (e.g. "machine-learning").
    date_from:  Optional ISO date lower bound (e.g. "2025-09-01").
    date_to:    Optional ISO date upper bound (e.g. "2025-12-31").

Note: search matches terms that appear in the journal text. For tag-only
browsing without a text query, use list_by_tag instead.

Show all connections for a specific capture (tag overlap and @-references).

Args:
    capture_id: The numeric ID returned by upload_capture or search_captures.

Return an overview of your knowledge base: capture counts, top tags,
open questions, key insights, and date range.

Export all captures (or a tag-filtered subset) as Markdown or JSON.

Args:
    format:     "markdown" (default) or "json"
    tag_filter: Optional tag value — only include captures with this tag
                (e.g. "machine-learning")

Returns the full export as a string (no file is written).

Scan your Rapid Capture entries and identify topic clusters ready to be
synthesized into a SYN page.

Args:
    min_captures: Minimum number of RC entries on a topic to flag it
                  (default 3).

Export your open questions as a portable study deck (tab-separated CSV).

Turns every ? question in your journal into a flashcard:
  Front — the question (from the ? tag)
  Back  — connected $ insight captures; falls back to the capture summary
  Tags  — the # topic tags on that capture

The output is a standard tab-separated CSV compatible with:
  - Anki (File → Import → Tab-separated)
  - Quizlet (Import → Tab between terms, newline between cards)
  - Obsidian, Notion, Google Sheets, or any CSV-aware tool
  - Print as a plain study sheet — no app required

Args:
    tag_filter: Optional # topic tag to limit the export
                (e.g. "machine-learning"). Leave blank for all questions.

Returns a tab-separated text block. No file is written to disk.

KPI dashboard and coaching recommendations for your journal practice.

Tracks:
  - Capture velocity (captures/week over last 4 weeks)
  - Insight velocity ($ insights/week)
  - Days since last Review entry
  - Unanswered open questions and their age
  - Synthesis ratio (RC entries per SYN page — target ~4:1)
  - Template balance (which template types are unused)

Returns a health score and specific, actionable recommendations.

Browse all captures that carry a specific tag — no text query required.

Use this to find every note related to a topic, source, question, or insight:
  list_by_tag("machine-learning")           → all captures with that tag
  list_by_tag("machine-learning", prefix="#") → only # topic tags
  list_by_tag("RC-012", prefix="@")          → captures referencing @RC-012
  list_by_tag("deadline", prefix="!")        → priority items
  list_by_tag("attention-mechanism", prefix="?") → that open question

Args:
    tag:    Tag value to look up (without the prefix character).
    prefix: Optional prefix to narrow the search: #  @  !  ?  $  ->
            Leave blank to match the tag across all prefix types.

Track Knowledge Status progression across your Review (REV) entries.

Shows how topics move through Needs Work → Solid → Mastered over time,
based on the Knowledge Status field on each REV page.

Args:
    topic: Optional # topic tag to filter (e.g. "calculus"). Leave blank
           to show all topics across all REV entries.