parsidion

name

parsidion

description

Use first when a request references past knowledge or future memory: "have we seen this before", "what do we know", "check notes", "prior art", "save to the vault", "remember this", "don't forget", "capture this", "the vault", "ParsidionVault", or "ClaudeVault". Parsidion owns the persistent markdown vault (default ~/ParsidionVault, legacy ~/ClaudeVault fallback) for debugging fixes, reusable patterns, research, and cross-project context. Use it to search existing knowledge, save new discoveries, rebuild the index, run the session summarizer, or configure vault hooks. Do not invoke for pure coding/debugging with no memory, notes, or persistence angle.

Parsidion vault - Knowledge Management System

A richly organized Obsidian vault (default ~/ParsidionVault/, with legacy ~/ClaudeVault/ fallback) that replaces built-in auto memory with structured, searchable, cross-linked knowledge.

Philosophy

• Search before create. Always search the vault before creating a new note. Duplicate knowledge fragments rot.
• Atomic notes. One concept per note. If a note covers two distinct ideas, split it.
• Frontmatter is mandatory. Every note must have a valid YAML frontmatter block. No exceptions.
• Knowledge compounds. Individual notes gain value through links. An unlinked note is a dead note.
• Confidence matters. Tag what you know vs. what you suspect. Update confidence as understanding deepens.

Vault-First Consultation

The vault is your first stop — not your last resort. Before web search, before documentation, before experimentation, check whether the vault already holds a solution.

Debugging: Search Before You Diagnose

When you encounter an error, exception, or unexpected behavior:

1. Extract the key signal: the exception type, package name, or the most distinctive phrase from the error message
2. Dispatch the vault-explorer agent with the signal as the query.
3. Answer returned? Apply the documented solution; update the note if you learn something new about the problem. Read specific files from the Sources section only if you need more depth.
4. "No relevant vault notes found"? Diagnose, solve, then save the solution so future sessions benefit.

Implementation: Check for Prior Art

When implementing any feature, pattern, or integration:

1. Dispatch the vault-explorer agent with the feature keyword as the query.
2. Answer returned? Reuse and adapt — a proven implementation beats a fresh one every time. Read specific files from the Sources section for details; check the sources frontmatter field for code references.
3. "No relevant vault notes found"? Implement it, then save the pattern so future sessions benefit.

Efficient Vault Search

Dispatch the vault-explorer agent with a natural language query. The agent searches all relevant vault folders using the appropriate Grep strategy, ranks matches, reads the top files, and returns:

• ## Answer — synthesized answer ready to use
• ## Sources — absolute paths for targeted Read calls if deeper context is needed

No results? Dispatch the research-agent to research externally and save findings to the vault.

The Vault-First Loop

Error or implementation question
  → Dispatch vault-explorer agent
    → Found? Apply / adapt solution → Update note with new learnings
    → Not found? Solve it → Dispatch research-agent to save

Saving after a successful solve is as important as searching before. Every unsaved solution is a missed opportunity for every future session.

---

Vault Structure

~/ParsidionVault/        # Or legacy ~/ClaudeVault/ when upgrading
├── CLAUDE.md            # Auto-generated index (rebuilt by update_index.py)
├── config.yaml          # Optional — hook/summarizer settings (see Configuration)
├── Daily/               # Session summaries (Daily/YYYY-MM/DD.md)
├── Projects/            # Per-project context and decisions
├── Languages/           # Python, Rust, TypeScript, Swift, etc.
├── Frameworks/          # Next.js, FastAPI, Textual, Rich, etc.
├── Patterns/            # Design patterns, architectural solutions
├── Debugging/           # Error patterns, diagnostic steps, fixes
├── Tools/               # CLI tools, libraries, packages
├── Research/            # Deep-dive research documents
├── Knowledge/           # General knowledge, concepts, reference material
├── History/             # Historical notes
└── Templates/           # Symlinked to skill templates (read-only)

Conventions

Filenames

• Kebab-case only: python-async-patterns.md, nextjs-app-router-caching.md
• Descriptive but concise -- aim for 3-5 words

Subfolders for Related Note Clusters

When 3 or more notes share a common subject, group them into a subfolder named after that subject:

# Before (scattered)
Research/
  fastapi-middleware-basics.md
  fastapi-middleware-auth.md
  fastapi-middleware-cors.md

# After (grouped)
Research/
  fastapi-middleware/
    basics.md
    auth.md
    cors.md

Rules:

• The subfolder name is the shared subject prefix in kebab-case (e.g., fastapi-middleware/)
• Note filenames inside the subfolder drop the redundant prefix (e.g., auth.md not fastapi-middleware-auth.md)
• This applies both proactively (when creating a 3rd note on a subject) and retroactively (reorganize existing notes when a 3rd sibling appears)
• Update all [[wikilinks]] in related fields after moving notes
• Run update_index.py after any reorganization

Cross-References

• Use wikilinks for all internal references: [[python-async-patterns]]
• Place wikilinks in the related frontmatter field AND inline in the note body where contextually relevant

Folder Placement

Content Type	Folder
Language-specific knowledge (syntax, idioms, stdlib)	Languages/
Framework-specific knowledge (config, APIs, gotchas)	Frameworks/
Reusable design patterns and architectural solutions	Patterns/
Error patterns, diagnostic steps, bug fixes	Debugging/
CLI tools, libraries, package notes	Tools/
Long-form research and analysis	Research/
General knowledge, concepts, reference material	Knowledge/
Per-project decisions, architecture, key paths	Projects/
Daily session summaries	Daily/YYYY-MM/DD.md (e.g. Daily/2026-03/13.md)

Frontmatter Standard

Every note must begin with this YAML block:

---
date: YYYY-MM-DD
type: pattern|debugging|research|project|daily|tool|language|framework|knowledge
tags: [tag1, tag2]
project: project-name        # optional - omit if not project-specific
confidence: high|medium|low
sources: []                  # URLs, file paths, or references
related: []                  # [[wikilinks]] to other vault notes
---

Field rules:

• date: The date the note was created or last substantially updated.
• type: Must be one of the enumerated values. Drives folder placement.
• tags: Freeform but prefer existing tags. Check the vault index first. NEVER use underscores — always kebab-case (hyphens). Convert repo names like par_ai_core to par-ai-core. Prefer short singular tags — e.g. voxel not voxel-engine, hook not hooks, fractal not fractals. Longer compound tags are acceptable only when the shorter form would be ambiguous.
• project: Optional. Must also be kebab-case (no underscores). Convert repo names: par_ai_core → par-ai-core.
• confidence: high = verified across multiple interactions or sources. medium = likely correct, single source. low = hypothesis or unverified.
• related: Must contain at least one wikilink in inline quoted array format: ["[[note-one]]", "[[note-two]]"]. No orphan notes.

When to Save Knowledge

Save a note when you encounter:

• Stable patterns confirmed across multiple interactions or projects
• Key architectural decisions and the reasoning behind them
• Important file paths and project structure insights
• Solutions to recurring problems -- especially if the fix was non-obvious
• Debugging insights -- error messages mapped to root causes and fixes
• User preferences for workflow, tools, coding style, communication
• Explicit requests -- when the user says "remember this" or equivalent

When NOT to Save

Do not create notes for:

• Session-specific context -- current task details, temporary variables, in-progress work
• Incomplete or unverified information -- wait until confidence is at least medium
• Anything already in CLAUDE.md -- the vault supplements project instructions, it does not duplicate them
• Raw code dumps -- code without explanation, context, or the "why" has no vault value
• Transient state -- things that will be irrelevant by next session

Anti-Patterns

Anti-Pattern	Correct Approach
Creating a note without frontmatter	Always include the full frontmatter block
Creating a duplicate note	Search the vault first; update the existing note instead
Dumping code without context	Explain what the code does, why it matters, and when to use it
Orphan notes (no links)	Every note must link to at least one other note via related
Monolithic notes (multiple concepts)	Split into atomic notes, one concept each, linked together
Modifying files in Templates/	Templates are managed by the skill -- never edit directly
3+ notes on the same subject left flat	Move them into a named subfolder; update all wikilinks

Workflow Integration

Hooks

All hooks read <resolved vault>/config.yaml for settings. CLI args override config values.

Claude Code installs the full hook set below. Codex runtime hooks are session lifecycle only: native Codex SessionStart and Stop hooks registered in ~/.codex/hooks.json when codex_hooks = true is enabled in ~/.codex/config.toml. Gemini runtime hooks are also lifecycle only: SessionStart and SessionEnd commands registered in ~/.gemini/settings.json via --runtime gemini or --runtime all. Gemini runtime hooks are separate from prompt AI backend selection and do not add a Gemini prompt backend; Gemini has no native subagent lifecycle capture in this first pass.

Hook	Behavior	Config section
SessionStart	Loads relevant vault notes as a compact one-line-per-note index (title + tags) by default — minimal token usage. --verbose flag or verbose_mode: true config switches to full note summaries. Optional AI selection via --ai [MODEL] or session_start_hook.ai_model uses the configured prompt AI backend.	session_start_hook
SessionEnd	Captures learnings from the session transcript (fires once at session end); auto-launches the summarizer when pending entries exist	session_stop_hook
PreCompact	Snapshots current working state so context survives compaction	pre_compact_hook

Rebuilding the Index

The CLAUDE.md at the vault root is auto-generated. Rebuild it when:

• The user requests it ("rebuild vault index", "update vault index", "refresh vault index", "refresh the vault", "sync the vault")
• After creating, renaming, moving, or deleting notes
• The index timestamp is older than 24 hours

uv run ~/.claude/skills/parsidion/scripts/update_index.py

This scans all vault folders, reads frontmatter, and produces:

• A structured root CLAUDE.md index with tag cloud, recent activity, and per-folder listings
• Per-folder MANIFEST.md files — table-format indexes (Note | Tags | Summary) written inside each subfolder for quick orientation without loading the full root index
• Staleness markers — notes with zero incoming wikilinks AND older than 30 days are flagged [STALE?] in the index (surfaced for review, never auto-deleted)

Confirm to the user when the rebuild is complete.

Summarizing Pending Sessions

The stop hook queues session transcript paths when it detects learnable content. Run the summarizer on demand to generate structured vault notes.

Supported transcript locations:

• Claude Code: ~/.claude/projects/**/*.jsonl
• Codex CLI: ~/.codex/sessions/**/*.jsonl
• Gemini CLI: ~/.gemini/**/*.jsonl and <project>/.gemini/**/*.jsonl
• pi (global): ~/.pi/agent/sessions/**/*.jsonl
• pi (project-local): <project>/.pi/agent-sessions/**/*.jsonl

Running the Summarizer

From a terminal outside Claude Code (normal usage):

uv run ~/.claude/skills/parsidion/scripts/summarize_sessions.py

From inside a Claude Code session (testing/debugging only): Claude Code sets the CLAUDECODE env var which blocks nested Claude sessions. Unset it for the subprocess:

env -u CLAUDECODE uv run ~/.claude/skills/parsidion/scripts/summarize_sessions.py

Process a single explicit file (useful for testing one entry):

env -u CLAUDECODE uv run ~/.claude/skills/parsidion/scripts/summarize_sessions.py \
  --sessions /path/to/file.jsonl

Options

Flag	Description	Config key
--sessions FILE	Process an explicit JSONL file instead of the default pending queue	—
--dry-run, -n	Preview what would be written without creating notes	—
--model MODEL	Explicit large-model override (default: backend large model)	summarizer.model
--persist	Accepted for backwards compatibility; currently unused	summarizer.persist

Uses the configured prompt AI backend: Claude runs through claude -p, Codex runs through codex exec, and no Claude Agent SDK or Codex SDK is required for this summarizer path. Processes up to 5 sessions in parallel with anyio (configurable via summarizer.max_parallel). Rebuilds the vault index automatically when done. Sessions from today whose generated note type is daily are skipped (today's daily note is still being built by the stop hook).

Write-Gate Filter

Before generating a note, the summarizer asks Claude to evaluate whether the session contains reusable insight. Transient sessions (failed experiments, routine builds, dead-ends with no generalizable lesson) are skipped rather than saved. Skipped sessions are reported separately from failures in the output summary.

Hierarchical Summarization

For sessions whose cleaned transcript exceeds max_cleaned_chars (default 12,000), the summarizer splits the transcript into chunks and uses the backend small model by default (configurable via summarizer.cluster_model) to summarize each chunk first. The chunk summaries are then fed to the main backend large-model note generator. This preserves context from very long sessions that would otherwise be truncated.

Automated Backlinks

After writing a new note, the summarizer scans existing vault notes for tag overlap and injects bidirectional [[wikilinks]] — updating both the new note's related field and adding back-references in matching existing notes. This builds the link graph automatically rather than requiring manual maintenance.

Vault Merge

vault-merge is a global CLI command for backend-aware AI-assisted note merging. It detects near-duplicate notes, merges their content via the configured prompt AI backend, and updates all bidirectional backlinks.

Usage

# Find near-duplicate pairs using embedding similarity (scan only, no merge)
vault-merge --scan

# Merge two specific notes (NOTE_A survives, NOTE_B is trashed)
vault-merge NOTE_A NOTE_B --execute

# Merge without rebuilding the index (for batch merges — rebuild once at the end)
vault-merge NOTE_A NOTE_B --no-index --execute

Key Flags

Flag	Description
--scan	List near-duplicate pairs sorted by cosine similarity; no merge performed
--execute	Actually perform the merge (default is dry-run preview)
--no-index	Skip rebuilding the vault index after the merge; use during batch operations and run update_index.py once when all merges are done

Use --no-index on every individual merge during a batch deduplication run; run update_index.py once at the end. The vault-deduplicator agent handles this automatically.

Vault Doctor

vault_doctor.py scans all vault notes for structural issues and repairs repairable issues via the configured prompt AI backend.

Issues detected

Code	Severity	Description
MISSING_FRONTMATTER	error	No YAML frontmatter block
MISSING_FIELD	error	date/type missing (all notes); confidence/related missing (non-daily)
INVALID_TYPE	error	type not in allowed set
INVALID_DATE	warning	date not in YYYY-MM-DD format
ORPHAN_NOTE	warning	No [[wikilinks]] in related field
BROKEN_WIKILINK	warning	Link target not found in vault
HEADING_MISMATCH	warning	No # heading found; first ## heading should be promoted to # (skips daily notes)
FLAT_DAILY	warning	Daily/YYYY-MM-DD.md instead of Daily/YYYY-MM/DD.md
PREFIX_CLUSTER	warning	3+ flat notes share a kebab prefix — should be moved into a subfolder

Daily notes (type: daily or path under Daily/) are exempt from confidence, related, and orphan checks.

Running the doctor

# Scan and report only (no writes)
uv run --no-project ~/.claude/skills/parsidion/scripts/vault_doctor.py --dry-run

# Fix everything in one pass (frontmatter + tags + subfolders) — used by nightly cron
env -u CLAUDECODE uv run --no-project ~/.claude/skills/parsidion/scripts/vault_doctor.py --fix-all

# Individual fix modes:
# Repair frontmatter issues via the configured prompt AI backend (3 parallel workers by default)
env -u CLAUDECODE uv run --no-project ~/.claude/skills/parsidion/scripts/vault_doctor.py --fix-frontmatter
env -u CLAUDECODE uv run --no-project ~/.claude/skills/parsidion/scripts/vault_doctor.py --fix-frontmatter --jobs 5 --timeout 180

# Detect and merge duplicate tags (plural/singular, hyphen/underscore, collapsed)
uv run --no-project ~/.claude/skills/parsidion/scripts/vault_doctor.py --fix-tags           # dry-run
uv run --no-project ~/.claude/skills/parsidion/scripts/vault_doctor.py --fix-tags --execute # apply

# Detect and migrate prefix clusters into subfolders
uv run --no-project ~/.claude/skills/parsidion/scripts/vault_doctor.py --migrate-subfolders           # dry-run
uv run --no-project ~/.claude/skills/parsidion/scripts/vault_doctor.py --migrate-subfolders --execute # apply

# Repair up to 20 notes at a time
env -u CLAUDECODE uv run --no-project ~/.claude/skills/parsidion/scripts/vault_doctor.py --fix-frontmatter --limit 20

# Errors only (skip warnings)
uv run --no-project ~/.claude/skills/parsidion/scripts/vault_doctor.py --errors-only --dry-run

For Claude CLI backend calls launched from inside Claude Code, unset CLAUDECODE as shown above. Codex backend calls use codex exec with an internal recursion guard.

--fix-all is equivalent to --fix-frontmatter --fix-tags --migrate-subfolders --execute. The nightly cron via summarize_sessions.py --run-doctor uses --fix-all.

Repairs run in parallel (--jobs N, default 3). Each prompt AI subprocess (claude -p or codex exec) is independent so parallelism is safe; state updates and console output are guarded by a lock so lines are never interleaved. The per-call timeout (--timeout SECS) defaults to 120s — increase it when running many parallel workers to avoid spurious timeouts.

Repairable codes (prompt AI backend can fix): MISSING_FRONTMATTER, MISSING_FIELD, INVALID_TYPE, INVALID_DATE, ORPHAN_NOTE. Auto-repairable without prompt AI (Python-only): BROKEN_WIKILINK (exact/semantic match), DUPLICATE_TAG (merge via --fix-tags), HEADING_MISMATCH (promotes first ## to #; enabled by default, disable with --no-fix-headings). Auto-repairable via Python + prompt AI filter: PREFIX_CLUSTER — candidates are detected by Python, then the configured small model filters out generic-word false positives (e.g. 'fixing', 'missing'), keeping only specific subject names (project, library, OS, tool). Files are then moved and wikilinks patched by Python. Not auto-repairable (require manual fix): FLAT_DAILY.

Singleton guard

Only one doctor may run at a time. On startup the doctor checks doctor_state.json for a pid field. If that process is still alive it prints an error and exits. Otherwise it writes its own PID immediately to claim the lock and clears it via atexit when it exits (including on SIGTERM; a SIGKILL'd process is detected as stale on the next run).

Auto-commit stale files

Before scanning, the doctor runs git status --porcelain on the vault and commits any tracked or untracked files whose mtime is ≥ 15 minutes old. Deletions are skipped (no mtime to check). The commit message is chore(vault): auto-commit N stale file(s) via vault_doctor. This is a no-op when the vault has no .git directory or git.auto_commit is false in config. With --dry-run the files are listed but not committed.

State tracking

The doctor writes <resolved vault>/doctor_state.json to avoid reprocessing notes unnecessarily:

Status	Meaning	Next run behaviour
ok	No issues found	Skipped for 7 days
fixed	Prompt AI backend repaired it	Re-checked to confirm
failed	Prompt AI backend returned no output	Retried
timeout	Prompt AI backend timed out once	Retried once more
needs_review	Timed out on retry	Skipped — user must fix manually
skipped	Only non-repairable issues	Skipped indefinitely

Use --no-state to ignore the state file and rescan all notes.

Run update_index.py after repairs — it reads doctor_state.json and adds a vault health line to CLAUDE.md's Quick Stats section.

Configuration

All hooks and the summarizer read <resolved vault>/config.yaml for settings. Precedence: hardcoded defaults → config.yaml → CLI args (last one wins).

A template with all options lives at ~/.claude/skills/parsidion/templates/config.yaml. Copy it to the vault root to get started:

cp ~/.claude/skills/parsidion/templates/config.yaml ~/ParsidionVault/config.yaml

Config Sections

📝 Note: Model IDs shown in the config block below (e.g. claude-sonnet-4-6, claude-haiku-4-5-20251001, gpt-5.5, BAAI/bge-small-en-v1.5) are the hardcoded defaults. They can be changed via the corresponding keys in <resolved vault>/config.yaml without modifying any scripts. See the template at ~/.claude/skills/parsidion/templates/config.yaml for all available keys.

session_start_hook:
  ai_model: null           # Model for AI note selection (null = disabled)
  max_chars: 4000          # Max context injection characters
  ai_timeout: 25           # AI call timeout in seconds
  recent_days: 3           # Days to look back for recent notes
  debug: false             # Append injected context to debug log in $TMPDIR
  verbose_mode: false      # If true, inject full note summaries instead of compact one-line index
  use_embeddings: true     # Blend semantic matches into context; graceful fallback if db absent

session_stop_hook:
  ai_model: null           # Model for AI classification (null = disabled)
  ai_timeout: 25           # AI call timeout in seconds
  auto_summarize: true     # Auto-launch summarizer when pending entries exist
  transcript_tail_lines: 200      # Lines to inspect by default
  pi_transcript_tail_lines: 1000  # Deeper fallback tail for pi transcripts when needed

subagent_stop_hook:
  enabled: true            # Set false to disable subagent transcript capture entirely
  min_messages: 3          # Minimum assistant message count (pi transcripts default to 1 when unset)
  excluded_agents: "vault-explorer,research-agent"  # Never capture these

pre_compact_hook:
  lines: 200               # Transcript lines to analyse

summarizer:
  model: null          # null = ai_models.<backend>.large
  max_parallel: 5          # Concurrent summarization tasks
  transcript_tail_lines: 400
  max_cleaned_chars: 12000
  persist: false           # Legacy no-op; retained for CLI compatibility
  cluster_model: null  # null = ai_models.<backend>.small

ai:
  backend: auto            # auto | claude-cli | codex-cli | none

ai_models:
  claude:
    small: claude-haiku-4-5-20251001
    large: claude-sonnet-4-6
  codex:
    small: gpt-5.5
    large: gpt-5.5

codex_cli:
  command: codex
  timeout: 60
  sandbox: read-only
  ephemeral: true
  skip_git_repo_check: true
  suppress_notify: true

embeddings:
  model: BAAI/bge-small-en-v1.5  # fastembed model ID; ~67 MB ONNX model, cached after first run
  min_score: 0.45                 # Minimum cosine similarity for search results
  top_k: 10                       # Default result count

git:
  auto_commit: true        # Auto-commit vault changes after writes

• auto prefers the active runtime when detectable and falls back to Claude CLI when ambiguous.
• Codex backend uses codex exec with default ephemeral/read-only/skip-git-repo-check/output-last-message prompt calls and the normal Codex CLI auth path; Parsidion does not manage Codex auth files such as ~/.codex/auth.json. Internal calls also pass --config notify=[] by default so user-configured Codex turn-complete notifications do not fire for vault summarization. This is not OpenAI API-key provider support. Gemini runtime hooks do not imply ai.backend: gemini; no Gemini prompt backend is provided yet.
• summarize_sessions.py also uses the configured prompt AI backend. Claude uses claude -p, Codex uses codex exec, and no Claude Agent SDK or Codex SDK is required for this path.

Programmatic Access

import vault_common

# Load all config (cached per process)
config = vault_common.load_config()

# Get a single value with fallback
max_chars = vault_common.get_config("session_start_hook", "max_chars", 4000)

If config.yaml is missing or unreadable, all get_config() calls return the default.

Manual Usage

Creating a Note

1. Search the vault to confirm the note does not already exist.
2. Create the file in the appropriate folder with kebab-case naming.
3. Include the full frontmatter block.
4. Write the note body with inline wikilinks to related notes.
5. Ensure the related frontmatter field is populated.
6. Run update_index.py to rebuild the index.

Searching the Vault

• Dispatch the vault-explorer agent for natural language queries — it searches priority folders, ranks matches, and returns a synthesized answer with source paths.
• Use vault_common.py functions for programmatic search from scripts.
• Use Obsidian's built-in search for interactive visual exploration.
• Use Grep or Glob tools only for targeted investigation when you know the specific file or exact pattern you need.

Agents

Agent	Trigger	Description
vault-explorer	"what do we know about X", "check our notes", "prior art"	Semantic vault search — returns a synthesized answer with source paths
research-agent	"research X and save to vault", no vault results found	External research via Brave Search + Web Fetch; saves findings to vault
vault-deduplicator	"deduplicate the vault", "find duplicate notes", "merge duplicate vault notes"	Scans for near-duplicate note pairs by embedding similarity, evaluates each pair, merges confirmed duplicates, and rebuilds the index

Linking Notes

• Use [[wikilinks]] in both the related frontmatter field and the note body.
• When a new note references an existing note, consider updating the existing note's related field to create a bidirectional link.

Updating Existing Notes

• Prefer updating an existing note over creating a new one on the same topic.
• Update the date field when making substantial changes.
• Adjust confidence as understanding evolves.

Complementary Tools

NotebookLM

Vault notes capture research as structured text. The notebooklm skill transforms the same source material into audio overviews, podcasts, quizzes, flashcards, mind maps, slide decks, and reports — formats the vault cannot produce.

Common patterns after vault research:

• Audio consumption: Generate a podcast from the URLs in a vault note's sources field.
• Study materials: Create quizzes or flashcards from a Research/ note.
• Briefing documents: Generate a briefing-doc from a cluster of related notes.
• Mind maps: Visual overview of a notebook containing the same sources as a vault cluster.

Workflow: Add the sources URLs from your vault note(s) as NotebookLM sources, then use notebooklm generate for the desired artifact. The research-agent does this automatically when NotebookLM is available.

Requirement: pip install notebooklm-py then notebooklm login. Run notebooklm status to check. If unavailable, the vault workflow is completely unaffected.

Graph Color Groups

The Obsidian graph at <resolved vault>/.obsidian/graph.json uses color groups to visually categorize nodes by tag.

Current Color Groups

Group	Tags	RGB (decimal)
Projects	#synknot, #parvitar, #parsistant, #termflix, #parvault, #parsidion, #pkm, #vault, #par-cc-bot, #parllama, #par-dc-bot, #par-particle-life, #par-shape-2d, #par_qr_3d, #par_scrape, #par-gpt, #par-ocr, #par_infini_sweeper, #parsplat, #pim-leaderboard, #par-ai-core, #cctmux	48340
Debugging	#debugging	16733986
Patterns	#memory, #migration, #sync, #architecture, #pattern, #automation, #config, #event-driven, #pipeline, #subprocess, #crdt, #layered-config, #rate-limiting, #semantic-search, #vector-search, #codesign, #state-machine, #concurrency	5025616
Research	#research, #qdrant, #embeddings, #vector-database, #ocr	10233776
Tools & SDKs	#claude-code, #claude-sdk, #claude, #rich, #mcp, #ollama, #maturin, #redis, #websockets, #sentry, #mermaid, #mermaid-cli, #custom-tools, #acp-protocol, #tool, #api, #encryption, #discord, #security, #git, #performance, #plugin, #cli, #llm, #hooks, #sqlite, #agent, #pydantic, #langchain, #multi-provider, #docker, #xdg, #image, #textual, #typer, #fastapi, #websocket, #image-processing, #json, #yaml, #ai	2201331
Languages	#rust, #python, #swift, #swiftui, #typescript, #nextjs, #react, #macos, #macos-26, #rust-packages, #ui, #ux, #documentation, #sql, #ios, #python-bindings	16761095
Terminal	#terminal, #tmux, #par-term, #par-term-emu-core-rust, #tui, #vt100, #vte, #pty, #ansi, #crossterm, #svt	38536
Graphics / 3D	#wgpu, #sdf, #sdf-terrain, #voxel, #fractals, #mandel, #vrm, #avatar, #face-tracking, #arkit, #graphics, #fractal, #gpu-rendering, #animation, #gaussian-splatting, #physics, #rendering, #simulation, #glsl, #shader, #shaders, #3d-printing, #par-fractal, #voxel-world, #sparse-voxel-tree, #sub-voxel, #wgsl, #vulkan, #game	15277667
Daily	#daily	16744448

When to Update

Update graph.json when:

• A new project tag is introduced (add to the Projects group)
• A new language or framework tag appears frequently (add to Languages or Tools/AI)
• A new topic cluster emerges that warrants its own color group
• An existing tag is renamed or removed

How to Update

1. Read <resolved vault>/.obsidian/graph.json
2. Locate the matching colorGroups entry by its query pattern
3. To add a tag to an existing group: append OR tag:#newtag to the query string
4. To create a new group: append a new object to the colorGroups array:

   {
     "query": "tag:#newtag",
     "color": { "a": 1, "rgb": <decimal_rgb> }
   }
   

5. Write the updated file — Obsidian picks up changes automatically

RGB Format

Colors are stored as a single decimal integer (e.g., 16733986 = #FF2DA2 in hex). To convert:

• Hex to decimal: int("FF2DA2", 16)
• Choose colors that are visually distinct from existing groups

Color Group Priority

Groups are evaluated in order — the first matching group wins. Place more specific queries (e.g., a single project tag) before broad ones (e.g., #research).

Auditing Coverage

Use check_graph_coverage.py to find vault tags that are not covered by any color group and to spot stale group entries (tags in graph.json that no longer exist in the vault):

python ~/.claude/skills/parsidion/scripts/check_graph_coverage.py

# Only report uncovered tags used 2+ times
python ~/.claude/skills/parsidion/scripts/check_graph_coverage.py --threshold 2

# Machine-readable JSON output
python ~/.claude/skills/parsidion/scripts/check_graph_coverage.py --json

Full Graph Colorizer Workflow

Run this workflow after batch summarizations, vault doctor runs, or whenever the tag taxonomy changes significantly:

1. Merge duplicate tags first — run vault_doctor.py --fix-tags --execute to merge plural/singular, hyphen/underscore, and collapsed duplicates. This reduces tag sprawl before colorizing.
2. Rebuild the index — run update_index.py so the tag cloud reflects the current state.
3. Audit coverage — run check_graph_coverage.py --threshold 2 to see which tags are uncovered and what groups they should belong to.
4. Update graph.json — read <resolved vault>/.obsidian/graph.json, add uncovered tags to the appropriate colorGroups entry by appending OR tag:#newtag to the query string.
5. Verify — re-run the audit to confirm coverage is ≥ 90%. Obsidian picks up graph.json changes automatically.

The --json output from check_graph_coverage.py includes a stats object with total_vault_tags, covered, uncovered, and stale counts for scripting.