// Analyze a complete literary work into a structured Basic Memory knowledge graph. Covers schema design, entity seeding, chapter-by-chapter processing, cross-referencing, validation, and visualization.
Structured metadata search for Basic Memory: query notes by custom frontmatter fields using equality, range, array, and nested filters. Use when finding notes by status, priority, confidence, or any custom YAML field rather than free-text content.
Defragment and reorganize agent memory files: split bloated files, merge duplicates, remove stale information, and restructure the memory hierarchy. Use when memory files have grown unwieldy, contain redundancies, or need reorganization. Run periodically (weekly) or on demand.
Structured metadata search for Basic Memory: query notes by custom frontmatter fields using equality, range, array, and nested filters. Use when finding notes by status, priority, confidence, or any custom YAML field rather than free-text content.
Sleep-time memory reflection: review recent conversations and daily notes, extract insights, and consolidate into long-term memory. Use when triggered by cron, heartbeat, or explicit request to reflect on recent activity. Runs as background processing to improve memory quality over time.
Schema lifecycle management for Basic Memory: discover unschemaed notes, infer schemas, create and edit schema definitions, validate notes, and detect drift. Use when working with structured note types (Task, Person, Meeting, etc.) to maintain consistency across the knowledge graph.
Task management via Basic Memory schemas: create, track, and resume structured tasks that survive context compaction. Uses BM's schema system for uniform notes queryable through the knowledge graph.
| name | memory-literary-analysis |
| description | Analyze a complete literary work into a structured Basic Memory knowledge graph. Covers schema design, entity seeding, chapter-by-chapter processing, cross-referencing, validation, and visualization. |
Transform a complete literary work into a structured knowledge graph. Characters, themes, chapters, locations, symbols, and literary devices become interconnected notes — searchable, validatable, and visualizable.
Phase 0: Setup → project, schemas, directory structure
Phase 1: Seed → stub notes for known major entities
Phase 2: Process → chapter-by-chapter notes in batches
Phase 3: Cross-ref → enrich arcs, add parallels, write analysis
Phase 4: Validate → schema checks, drift detection, consistency
Phase 5: Visualize → canvas files for character webs, timelines
create_memory_project(name="<work-name>", path="~/basic-memory/<work-name>")
Use a kebab-case slug of the work's title (e.g., great-gatsby, hamlet, beloved).
Write 6 schema notes to schema/. Each schema defines the entity type's fields, observation categories, and relation types. Adapt fields to fit the work — the schemas below are starting points, not rigid templates.
write_note(
title="Character",
directory="schema",
note_type="schema",
metadata={
"entity": "Character",
"version": 1,
"schema": {
"role(enum)": "[protagonist, antagonist, supporting, minor], character's narrative role",
"description": "string, brief character description",
"first_appearance?": "string, chapter or scene of first appearance",
"status?(enum)": "[alive, dead, unknown, transformed], character status at end of work"
},
"settings": {"validation": "warn"}
},
content="""# Character
Schema for character entity notes.
## Observations
- [convention] Major characters in characters/major/, minor in characters/minor/
- [convention] Observation categories: trait, motivation, arc, quote, appearance, relationship, symbolism, fate
- [convention] Relations: appears_in, contrasts_with, allied_with, commands, symbolizes, associated_with"""
)
Add work-specific fields as needed — e.g., rank for military fiction, house for family sagas, species for fantasy.
write_note(
title="Theme",
directory="schema",
note_type="schema",
metadata={
"entity": "Theme",
"version": 1,
"schema": {
"description": "string, what this theme explores",
"prevalence(enum)": "[major, minor], how central to the work",
"first_introduced?": "string, where theme first appears"
},
"settings": {"validation": "warn"}
},
content="""# Theme
Schema for thematic analysis notes.
## Observations
- [convention] Observation categories: definition, manifestation, evolution, counterpoint, quote, interpretation
- [convention] Relations: embodied_by, contrasts_with, reinforced_by, explored_in, expressed_through"""
)
write_note(
title="Chapter",
directory="schema",
note_type="schema",
metadata={
"entity": "Chapter",
"version": 1,
"schema": {
"chapter_number": "integer, sequential chapter number",
"pov?": "string, point-of-view character or narrator mode",
"setting?": "string, primary location",
"narrative_mode?(enum)": "[dramatic, expository, reflective, epistolary, mixed], chapter's primary mode"
},
"settings": {"validation": "warn"}
},
content="""# Chapter
Schema for chapter-level analysis notes.
## Observations
- [convention] Chapters stored in chapters/ directory
- [convention] Observation categories: summary, event, tone, technique, quote, significance, foreshadowing
- [convention] Relations: features, set_in, explores, contains, employs, follows, precedes, parallels"""
)
write_note(
title="Location",
directory="schema",
note_type="schema",
metadata={
"entity": "Location",
"version": 1,
"schema": {
"description": "string, what this place is",
"location_type(enum)": "[city, building, landscape, body_of_water, region, fictional, vehicle], type of place",
"real_or_fictional(enum)": "[real, fictional, both], whether the place exists"
},
"settings": {"validation": "warn"}
},
content="""# Location
Schema for location and setting notes.
## Observations
- [convention] Observation categories: description, atmosphere, symbolism, significance, geography
- [convention] Relations: setting_for, associated_with, symbolizes, contains, part_of"""
)
write_note(
title="Symbol",
directory="schema",
note_type="schema",
metadata={
"entity": "Symbol",
"version": 1,
"schema": {
"description": "string, what the symbol is literally",
"symbol_type(enum)": "[object, animal, color, action, natural_phenomenon, body_part], category of symbol",
"primary_meaning": "string, most common interpretation"
},
"settings": {"validation": "warn"}
},
content="""# Symbol
Schema for symbolic element notes.
## Observations
- [convention] Observation categories: meaning, appearance, ambiguity, interpretation, quote, evolution
- [convention] Relations: represents, associated_with, appears_in, contrasts_with, located_at"""
)
write_note(
title="LiteraryDevice",
directory="schema",
note_type="schema",
metadata={
"entity": "LiteraryDevice",
"version": 1,
"schema": {
"description": "string, what the device is",
"device_type(enum)": "[rhetorical, structural, figurative, narrative, dramatic], category",
"frequency(enum)": "[pervasive, frequent, occasional, rare], how often used"
},
"settings": {"validation": "warn"}
},
content="""# LiteraryDevice
Schema for literary technique and device notes.
## Observations
- [convention] Observation categories: definition, usage, effect, example, significance
- [convention] Relations: used_in, characterizes, expresses, related_to"""
)
<project>/
schema/ # 6 schema definitions
chapters/ # one note per chapter/section + prologue/epilogue
characters/
major/ # protagonist, antagonist, key supporting
minor/ # named characters with limited roles
themes/ # thematic analysis notes
locations/ # settings and places
symbols/ # symbolic elements
literary-devices/ # techniques and devices
analysis/ # cross-cutting synthesis
tasks/ # processing tracker
Before processing chapters, create stub notes for major entities so [[wiki-links]] resolve from the start.
For each major character, create a stub with known metadata:
write_note(
title="<Character Name>",
directory="characters/major",
note_type="Character",
tags=["character", "major", "<role>"],
metadata={"role": "<role>", "description": "<brief description>"},
content="""# <Character Name>
## Observations
- [role] <Character's role in the work>
- [appearance] <Key physical description>
## Relations
- associated_with [[<Related Character>]]
- appears_in [[<Key Location>]]"""
)
Identify the work's major entities before you start reading. A good starting inventory:
| Type | Typical Count | What to Include |
|---|---|---|
| Characters (major) | 8-20 | Protagonist, antagonist, key supporting cast |
| Themes | 5-12 | Central concerns the work explores |
| Locations | 4-10 | Primary settings, symbolically significant places |
| Symbols | 4-10 | Recurring objects, images, or motifs with layered meaning |
Stubs don't need to be complete — they give [[wiki-link]] targets and will be enriched during chapter processing.
Obtain the full text and identify chapter/section boundaries. For public domain works, Project Gutenberg is a good source. For copyrighted works, work from a physical or licensed digital copy.
Process ~10 chapters per batch to balance depth with progress. Group by narrative arc or thematic focus:
| Batch | Typical Content |
|---|---|
| 1 | Opening: setting, character introductions, world-building |
| 2-3 | Rising action: conflicts established, relationships develop |
| 4-6 | Middle: complications, turning points, thematic deepening |
| 7-8 | Climax approach: escalation, revelations, crises |
| Final | Climax, resolution, epilogue |
Adjust batch size based on chapter length and density. Short, action-heavy chapters can be batched in larger groups; long, philosophically dense chapters may need smaller batches.
For each chapter:
1. Read the chapter carefully. If working from a source text file, read the relevant section.
2. Create the chapter note:
write_note(
title="Chapter <N> - <Title>",
directory="chapters",
note_type="Chapter",
tags=["chapter", "<arc-phase>"],
metadata={
"chapter_number": <N>,
"pov": "<narrator or POV character>",
"setting": "<primary location>",
"narrative_mode": "<mode>"
},
content="""# Chapter <N> - <Title>
## Observations
- [summary] <1-2 sentence synopsis>
- [event] <Key plot events>
- [tone] <Emotional and stylistic atmosphere>
- [technique] <Notable narrative techniques>
- [quote] "<Significant passage>"
- [significance] <Why this chapter matters to the whole>
- [foreshadowing] <Hints at future events>
## Relations
- features [[<Character>]]
- set_in [[<Location>]]
- explores [[<Theme>]]
- contains [[<Symbol>]]
- employs [[<Literary Device>]]
- follows [[Chapter <N-1> - <Previous Title>]]
- precedes [[Chapter <N+1> - <Next Title>]]"""
)
3. Enrich related entities:
edit_note(
identifier="characters/major/<character-slug>",
operation="append",
heading="Observations",
content="""- [arc] Ch.<N>: <What happens to this character>
- [quote] "<Attributed quote>" (Ch.<N>)"""
)
4. Track progress using the memory-tasks skill to create a processing task that survives context compaction.
| Category | What to Look For |
|---|---|
[summary] | 1-2 sentence chapter synopsis |
[event] | Key plot events (actions, revelations, arrivals) |
[tone] | Emotional and stylistic atmosphere |
[technique] | Narrative innovations (POV shifts, structural experiments, genre blending) |
[quote] | Memorable or thematically significant passages |
[significance] | Why this chapter matters to the whole |
[foreshadowing] | Hints at future events |
As each chapter is processed, append observations to relevant entities:
[arc] moments, new [trait] revelations, [quote] attributions[manifestation] in this chapter, [evolution] shifts[appearance] with context, new [interpretation] angles[atmosphere] as described, [significance] in scene[example] from this chapterAfter the structured observations are in place, consider adding interpretive prose to major entity notes. Prepend 2-4 paragraphs of critical essay before the Observations section using edit_note(operation="prepend"). This prose should:
The prose adds the interpretive texture that structured observations alone cannot capture.
After all chapters are processed:
For each major character, write a full [arc] summary observation covering their trajectory across the work.
For each theme, add [evolution] observations tracing how it develops from introduction to resolution.
Add parallels and contrasts_with relations between structurally similar chapters (e.g., mirrored scenes, repeated settings, thematic echoes).
Create synthesis notes in analysis/:
write_note(
title="Narrative Structure",
directory="analysis",
note_type="note",
tags=["analysis", "structure"],
content="""# Narrative Structure
Analysis of the work's narrative architecture.
## Observations
- [structure] <Overall arc description>
- [technique] <Key narrative strategies>
...
## Relations
- analyzes [[<Protagonist>]]
- analyzes [[<Key Character>]]
- explores [[<Central Theme>]]
..."""
)
Recommended analysis notes:
During chapter processing, new minor characters, locations, and symbols will emerge. Create notes for any that appear in 3+ chapters or carry thematic weight.
# Validate each entity type
schema_validate(noteType="Character")
schema_validate(noteType="Theme")
schema_validate(noteType="Chapter")
schema_validate(noteType="Location")
schema_validate(noteType="Symbol")
schema_validate(noteType="LiteraryDevice")
schema_diff(noteType="Character")
# ... for each type
Fix issues found — common fixes:
edit_noteSpot-check bidirectional relations: if Chapter X features [[Character]], does Character have observations referencing Chapter X? Fix gaps.
Generate canvas files for visual exploration:
# Character relationship web
canvas(query="type:Character AND role:protagonist OR role:antagonist OR role:supporting")
# Theme connections
canvas(query="type:Theme")
# Chapter timeline with key events
canvas(query="type:Chapter", layout="timeline")
This pipeline works for any literary text. Adjust schemas for genre:
| Genre | Schema Adjustments |
|---|---|
| Novel | Base schemas work as-is; add genre-specific Character fields as needed |
| Play | Add Act and Scene schemas; Character gets speaking_lines field |
| Poetry collection | Replace Chapter with Poem; add form, meter, rhyme_scheme fields |
| Non-fiction | Replace Chapter with Section; add Argument, Evidence schemas |
| Short story collection | Add Story schema with narrator, setting, word_count |
| Epic/myth | Add Deity, Prophecy schemas; Location gets mythological_significance |
| Memoir | Character schema gets relationship_to_narrator; add Memory schema |
| Work Length | Batch Size | Estimated Notes |
|---|---|---|
| Novella (~40K words) | 5-10 chapters | ~50-80 |
| Novel (~80K words) | 8-12 chapters | ~100-150 |
| Long novel (~200K+ words) | 10-15 chapters | ~200-300 |
| Series (multiple volumes) | 1 volume at a time | ~200+ per volume |
schema_validate after each batch, not just at the end. Catch drift early.[quote] observations with chapter attribution.analysis/ should be written after all chapters are processed, when you have the full picture.