Agent skill

Copilot History Ingest

by Ar9av in Ar9av/obsidian-wiki

Ingest GitHub Copilot CLI/session history into Obsidian as distilled knowledge.

MITAuto-check: notesKnowledge Management

Install Copilot History Ingest

skills CLI
$ npx skills add Ar9av/obsidian-wiki --skill copilot-history-ingest -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install Ar9av/obsidian-wiki copilot-history-ingest --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/Ar9av/obsidian-wiki.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.skills/copilot-history-ingest .claude/skills/copilot-history-ingest && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
copilot-history-ingest
GitHub stars
3.5k
Token cost
~4.4k tokens
SKILL.md length
1,499 words
Files
2 (incl. references)
Skills in repo
39
Repo updated
First seen
Licence
MIT

At a glance

Ingest GitHub Copilot CLI/session history into Obsidian as distilled knowledge.

  • Works in 6 steps: Survey and Compute Delta → Ingest Checkpoints and Summaries First → Parse Session Turns → …
  • Mining Copilot history
  • SKILL.md covers Before You Start, Ingest Modes, GitHub Copilot Data Layout and Step 1: Survey and Compute Delta, plus 10 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Copilot History Ingest is an agent skill from Ar9av/obsidian-wiki. Ingest GitHub Copilot CLI/session history into Obsidian as distilled knowledge. Use for importing or mining Copilot history; not for general Copilot help, simple session search, or backup.

Its SKILL.md is about 4.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files, including reference files (for example `references/copilot-data-format.md`).

It sits in Knowledge Management. It works with Obsidian and Visual Studio Code. The repository describes itself as: Framework for AI agents to build and maintain a digital brain through Obsidian wiki | Memory System for Agents. The licence is MIT.

When your agent uses it

  • Mining Copilot history
  • Not for general Copilot help
  • Simple session search

Example prompts

  • “/copilot-history-ingest”

Requirements

  • Python 3

Workflow steps

6 steps, taken from the step headings in SKILL.md.

  1. Survey and Compute Delta
  2. Ingest Checkpoints and Summaries First
  3. Parse Session Turns
  4. Cluster by Topic
  5. Distill into Wiki Pages
  6. Update Manifest, Journal, and Special Files

What it can do on your machine

Read from SKILL.md and the folder at commit df54595. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are bash, sql, python, yaml and json).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Copilot History Ingest loads about 4.4k tokens when it runs, and up to ~7.5k if it reads all its reference files. Until then it costs about 53 tokens; SKILL.md has 1,499 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~53
When it runs · the whole SKILL.md, loaded when a task matches
~4.4k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~7.5k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NoteMentions a .env fileSKILL.md:18
    line `@name` override → walk up CWD for `.env` → global config → prompt setup). This gives `OBSIDIAN_VAULT_PATH`, `COPIL
  • NoteMentions a .env fileSKILL.md:75
    is platform-specific and must come from `.env` or user input.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from Ar9av/obsidian-wiki at commit df54595, republished under its MIT licence (© Ar9av). 1,499 words, ~4,430 tokens.

Download SKILL.mdSave it as .claude/skills/copilot-history-ingest/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
copilot-history-ingest
description
Ingest GitHub Copilot CLI/session history into Obsidian as distilled knowledge. Use for importing or mining Copilot history; not for general Copilot help, simple session search, or backup.

Copilot History Ingest — Conversation Mining

You are extracting knowledge from the user's past GitHub Copilot CLI conversations and distilling it into the Obsidian wiki. Conversations are rich but messy — your job is to find the signal and compile it.

This skill can be invoked directly or via the wiki-history-ingest router (/wiki-history-ingest copilot).

Before You Start

Writing profile: Before drafting or rewriting natural-language Markdown, read and apply the Writing Profile Resolution section in llm-wiki/SKILL.md. Framework schema, provenance, safety, and operation-specific requirements take precedence. WRITING.md preferences apply only to newly drafted or rewritten natural-language Markdown; preserve source content and structured records.

  1. Resolve config — follow the Config Resolution Protocol in llm-wiki/SKILL.md (inline @name override → walk up CWD for .env → global config → prompt setup). This gives OBSIDIAN_VAULT_PATH, COPILOT_HISTORY_PATH (defaults to ~/.copilot/session-state), and COPILOT_VSCODE_STORAGE_PATH (VS Code workspaceStorage; platform-specific — ask the user if absent)
  2. Read .manifest.json at the vault root to check what's already been ingested
  3. Read index.md at the vault root to know what the wiki already contains

Ingest Modes

Append Mode (default)

Check .manifest.json for each source file (events JSONL, transcript JSONL, checkpoint, session-store DB). Only process:

  • Sessions not in the manifest (new sessions)
  • Sessions whose updated_at is newer than their ingested_at in the manifest

This is usually what you want — the user ran a few new sessions and wants to capture the delta.

Full Mode

Process everything regardless of manifest. Use after a wiki-rebuild or if the user explicitly asks.

GitHub Copilot Data Layout

Copilot stores data in three locations. Scan all three.

Source 1: ~/.copilot/session-state/ (CLI sessions)
~/.copilot/session-state/
├── <session-uuid>/
│   ├── workspace.yaml           # Session metadata (id, cwd, summary_count, created_at, updated_at)
│   ├── vscode.metadata.json     # VS Code context (workspaceFolder, repositoryProperties, customTitle)
│   ├── events.jsonl             # Full event log — all turns, tool calls, reasoning
│   ├── session.db               # Per-session SQLite (todos/todo_deps only — skip for ingestion)
│   ├── index.md                 # Session summary written at session end
│   ├── checkpoints/             # Checkpoint JSON files (mid-session summaries)
│   │   └── <uuid>.json          # title, overview, history, work_done, technical_details,
│   │                            #   important_files, next_steps
│   ├── files/                   # Artifacts produced during session (plans, diagrams, etc.)
│   └── research/                # Research artifacts
└── ...
Source 2: ~/.copilot/session-store.db (Global SQLite)

The canonical cross-session database. This is the highest-value source: structured, queryable, and pre-summarised.

sessions       — id, cwd, repository, branch, summary, created_at, updated_at, host_type
turns          — session_id, turn_index, user_message, assistant_response, timestamp
checkpoints    — session_id, checkpoint_number, title, overview, history, work_done,
                 technical_details, important_files, next_steps, created_at
session_files  — session_id, file_path, tool_name, turn_index, first_seen_at
session_refs   — session_id, ref_type (commit/pr/issue), ref_value, turn_index, created_at
search_index   — FTS5 virtual table (content, session_id, source_type, source_id)
Source 3: VS Code Workspace Storage (<workspaceStorage>/<hash>/GitHub.copilot-chat/)

VS Code extension data, keyed by workspace hash. The path is platform-specific and must come from .env or user input.

<hash>/GitHub.copilot-chat/
├── transcripts/
│   └── <session-uuid>.jsonl     # Conversation transcripts (same JSONL format as events.jsonl)
├── memory-tool/
│   └── memories/
│       └── <base64-session-id>/ # Per-session saved artifacts (plan.md, etc.)
│           └── plan.md
└── codebase-external.sqlite     # Codebase index (skip — no conversation knowledge)
Key data sources ranked by value:
  1. Checkpoints (session-store.db checkpoints table + per-session checkpoints/*.json) — Pre-distilled summaries with overview, work_done, technical_details, important_files, next_steps. Gold.
  2. Session summaries (session-store.db sessions.summary + index.md) — One-paragraph synopsis per session.
  3. Turns (session-store.db turns table + events.jsonl / transcript JSONL) — Full conversation. Rich but verbose.
  4. Memory artifacts (memory-tool/memories/<id>/plan.md etc.) — Pre-written plans and structured notes the user saved explicitly. Worth importing verbatim (or lightly summarised).
  5. File access patterns (session_files table + tool.execution_* events) — Which files the agent repeatedly touched — reveals high-value project files.
  6. Session refs (session_refs table) — Commits, PRs, and issues linked to sessions.
  7. vscode.metadata.json — Workspace folder path, branch, customTitle (user-set session label). Useful for grouping and naming.

Step 1: Survey and Compute Delta

Scan all three data locations and compare against .manifest.json:

bash
# --- Source 1: per-session directories ---
# Find all session directories (each has workspace.yaml)
ls ~/.copilot/session-state/

# For each session, read workspace.yaml for id/cwd/updated_at
# and vscode.metadata.json for customTitle / repositoryProperties

# --- Source 2: global database ---
# Query session-store.db with sqlite3 (or Python sqlite3)
SELECT s.id, s.cwd, s.repository, s.branch, s.summary, s.updated_at,
       COUNT(DISTINCT t.turn_index) AS turn_count,
       COUNT(DISTINCT c.id)         AS checkpoint_count
FROM sessions s
LEFT JOIN turns t ON t.session_id = s.id
LEFT JOIN checkpoints c ON c.session_id = s.id
GROUP BY s.id
ORDER BY s.updated_at DESC;

# --- Source 3: VS Code workspace storage ---
# For each <hash> directory under workspaceStorage, check for GitHub.copilot-chat/
# Find transcript files
ls <workspaceStorage>/<hash>/GitHub.copilot-chat/transcripts/

Build a unified inventory — one entry per session UUID — and classify:

  • New — not in manifest → needs ingesting
  • Modified — in manifest but updated_at is newer → needs re-ingesting
  • Unchanged — in manifest and not modified → skip in append mode

Report to the user: "Found X sessions in session-state, Y in session-store.db, Z VS Code transcript files. Checkpoints: A. Delta: B new, C modified."

Step 2: Ingest Checkpoints and Summaries First

Checkpoints are already distilled — process them before touching raw turns.

From session-store.db:
sql
SELECT s.id, s.cwd, s.repository, s.branch, s.summary,
       c.checkpoint_number, c.title, c.overview, c.work_done,
       c.technical_details, c.important_files, c.next_steps,
       c.created_at
FROM checkpoints c
JOIN sessions s ON c.session_id = s.id
ORDER BY s.updated_at DESC, c.checkpoint_number ASC;
From per-session checkpoints/*.json:

Each checkpoint file has: title, overview, history, work_done, technical_details, important_files, next_steps.

Read index.md (if present) as a session-level summary — it's typically written at session end and is already concise.

What to extract:
  • overview → high-level description of what the session accomplished
  • work_done → concrete tasks completed (good for skills / project pages)
  • technical_details → implementation specifics (good for concepts pages)
  • important_files → high-value files in the project (good for project pages)
  • next_steps → open threads (good for linking to ongoing project work)

Step 3: Parse Session Turns

Read turns from session-store.db (preferred — already parsed) or from events.jsonl / transcript JSONL.

From session-store.db:
sql
SELECT turn_index, user_message, assistant_response, timestamp
FROM turns
WHERE session_id = '<uuid>'
ORDER BY turn_index ASC;
From events.jsonl / transcript JSONL:

Each file is one session. Each line is a JSON event. See references/copilot-data-format.md for the full schema.

Relevant event types:

typeWhat it isWorth reading?
session.startSession metadata (cwd, branch, version)Yes — establishes project context
user.messageUser turnYes — data.content
assistant.messageAssistant turnYes — data.content (text) + data.toolRequests
tool.execution_startTool callSkim — reveals what files/commands were used
tool.execution_endTool resultNo — usually noise

Extraction strategy for assistant.message:

  • data.content is the assistant's text response — extract this
  • data.reasoningText is internal reasoning — skip (it's the unpacked reasoningOpaque field)
  • data.toolRequests lists tool calls — skim tool names and arguments for file access patterns
  • Skip type: "tool.execution_end" entirely

Step 3b: Process Memory Artifacts

For each session that has a memory-tool/memories/<base64-id>/ directory in VS Code workspace storage, read any markdown files saved there (typically plan.md). These are documents the user explicitly saved — treat them as high-quality, user-authored content.

Decode the base64 directory name to get the session UUID:

python
import base64
session_id = base64.b64decode(dir_name).decode('utf-8')

Memory artifacts map to project skills/ or concepts/ pages, depending on content type.

Step 3c: Extract File and Ref Patterns

From session-store.db:

sql
-- Most-touched files per project
SELECT repository, file_path, COUNT(*) AS touch_count
FROM session_files
GROUP BY repository, file_path
ORDER BY touch_count DESC;

-- Linked commits/PRs/issues per session
SELECT session_id, ref_type, ref_value, turn_index
FROM session_refs
ORDER BY session_id, turn_index;

File access patterns reveal which files are architecturally important — note them on project pages.

Session refs link Copilot sessions to git history — useful for connecting wiki knowledge to concrete code changes.

Step 4: Cluster by Topic

Don't create one wiki page per session. Instead:

  • Group extracted knowledge by topic across sessions
  • A single session about "debugging auth + setting up CI" → two separate topics
  • Three sessions across different days about "React performance" → one merged topic
  • cwd / repository give you a natural first-level grouping; vscode.metadata.json's customTitle gives a human-readable session label

Step 5: Distill into Wiki Pages

Each Copilot project maps to a project directory in the vault. Derive the project name from cwd or repository:

C:\Users\name\git\my-project   → my-project
/Users/name/code/another-app   → another-app

Prefer repository (e.g., owner/repo) from session-store.db over raw cwd when available.

Show full SKILL.md (592 more words)Show less
Project-specific vs. global knowledge
What you foundWhere it goesExample
Project architecture decisionsprojects/<name>/concepts/projects/my-project/concepts/main-architecture.md
Project-specific debugging patternsprojects/<name>/skills/projects/my-project/skills/api-rate-limiting.md
General concept the user learnedconcepts/ (global)concepts/react-server-components.md
Recurring problem across projectsskills/ (global)skills/debugging-hydration-errors.md
A tool/service usedentities/ (global)entities/vercel-functions.md
Patterns across many sessionssynthesis/ (global)synthesis/common-debugging-patterns.md

For each project with content, create or update the project overview page at projects/<name>/<name>.md — named after the project, not _project.md. Obsidian's graph view uses the filename as the node label, so _project.md makes every project show up as _project in the graph. Naming it <name>.md gives each project a distinct, readable node name.

Important: Distill the knowledge, not the conversation. Don't write "In a session on March 15, the user asked about X." Write the knowledge itself, with the session as a source attribution.

Write a summary: frontmatter field on every new/updated page — 1–2 sentences, ≤200 chars, answering "what is this page about?" for a reader who hasn't opened it. wiki-query's cheap retrieval path reads this field to avoid opening page bodies.

Add confidence and lifecycle fields to every new page's frontmatter:

yaml
base_confidence: 0.42
lifecycle: draft
lifecycle_changed: <ISO date today>

Leave lifecycle unchanged on update.

Mark provenance per the convention in llm-wiki (Provenance Markers section):

  • Checkpoints and index.md are pre-distilled by the system — treat checkpoint-derived claims as extracted (the system wrote them from observed actions).
  • Memory artifacts are user-authored — treat as extracted.
  • Conversation turn distillation is mostly inferred. You're synthesizing a coherent claim from many turns. Apply ^[inferred] liberally to synthesized patterns, generalizations across sessions, and "what the user really meant" interpretations.
  • Use ^[ambiguous] when the user changed direction mid-session or when the session ended unresolved.
  • Write a provenance: frontmatter block on every new/updated page summarizing the rough mix.

Step 6: Update Manifest, Journal, and Special Files

Update .manifest.json

For each session processed, add/update its entry with:

  • ingested_at, session_id, updated_at
  • source_type: one of "copilot_session", "copilot_checkpoint", "copilot_transcript", "copilot_memory_artifact"
  • project: the decoded project name
  • pages_created and pages_updated lists

Also update the projects section of the manifest:

json
{
  "project-name": {
    "repository": "owner/repo",
    "cwd": "C:\\Users\\name\\git\\project-name",
    "vault_path": "projects/project-name",
    "last_ingested": "TIMESTAMP",
    "sessions_ingested": 5,
    "sessions_total": 8,
    "checkpoints_ingested": 12,
    "memory_artifacts_ingested": 3
  }
}
Create journal entry + update special files

Update index.md, log.md, and hot.md with one locked call:

bash
obsidian-wiki memory sync COPILOT_HISTORY_INGEST \
  projects=<projects> sessions=<sessions> checkpoints=<checkpoints> \
  pages_updated=<pages_updated> pages_created=<pages_created> \
  mode=<mode> \
  --takeaways "Ingested 5 Copilot sessions across 2 projects; surfaced patterns in API design and testing strategy."

Never hand-edit index.md, log.md, or hot.md — the command takes the lock that keeps a parallel writer from dropping your update. --takeaways is the one-line conceptual summary that used to go in Recent Activity; omit it to leave the previous takeaways untouched.

If an ongoing project is now better understood, record the thread so the next session picks it up: obsidian-wiki memory todo add "<thread>" --origin projects/<name>.md.

See .skills/llm-wiki/references/MEMORY.md for the full procedure.

Privacy

  • Distill and synthesize — don't copy raw conversation text verbatim
  • Skip anything that looks like secrets, API keys, passwords, tokens
  • data.reasoningOpaque / data.reasoningText in assistant events is internal reasoning — skip entirely, never copy to wiki
  • If you encounter personal/sensitive content, ask the user before including it
  • The user's conversations may reference other people — be thoughtful about what goes in the wiki

Reference

See references/copilot-data-format.md for detailed data structure documentation.

QMD Refresh After Vault Writes

QMD is a search index, not the source of truth. If $QMD_WIKI_COLLECTION is empty or unset, skip this step. Run it only after this skill has written or rewritten vault markdown. If QMD refresh fails, do not roll back the vault changes; report the QMD status separately.

Use $QMD_CLI if set; otherwise use qmd.

bash
${QMD_CLI:-qmd} update

If the output says vectors are needed or embeddings may be stale, run:

bash
${QMD_CLI:-qmd} embed

Verify the collection with either:

bash
${QMD_CLI:-qmd} ls "$QMD_WIKI_COLLECTION"

or, when a specific page path is known:

bash
${QMD_CLI:-qmd} get "qmd://$QMD_WIKI_COLLECTION/<page>.md" -l 5

Record one of:

  • QMD refreshed: update + embed + verified
  • QMD refreshed: update only + verified
  • QMD skipped: QMD_WIKI_COLLECTION unset
  • QMD skipped: qmd CLI unavailable
  • QMD failed: <short error summary>

© Ar9av, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file (references) in .skills/copilot-history-ingest of Ar9av/obsidian-wiki.

  • SKILL.md
  • references/copilot-data-format.md

Open the folder on GitHubat commit df54595

Compare with similar skills

Copilot History Ingest next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Copilot History Ingest compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Copilot History Ingest this skillAr9av/obsidian-wiki3.5k—~4.4kAutomated safety check: NotesMIT
Obsidian CLIAtmosphere/atmosphere3.8k13 repos~795Automated safety check: PassApache-2.0
Obsidian Canvas BoardsAgriciDaniel/claude-obsidian15k—~1.4kAutomated safety check: PassMIT
LLM Wikilewislulu/llm-wiki-skill655—~3.7kAutomated safety check: PassNone
Second BrainNicholasSpisak/second-brain737—~1.5kAutomated safety check: NotesNone
Autographsmixs/agent-second-brain393—~3.7kAutomated safety check: PassMIT

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Questions about Copilot History Ingest

What does Copilot History Ingest do?

Ingest GitHub Copilot CLI/session history into Obsidian as distilled knowledge. Copilot History Ingest is an agent skill from Ar9av/obsidian-wiki. Ingest GitHub Copilot CLI/session history into Obsidian as distilled knowledge.

When should I use Copilot History Ingest?

Copilot History Ingest fits situations like: mining Copilot history; not for general Copilot help; simple session search.

How do I install Copilot History Ingest in Claude Code?

Run `npx skills add Ar9av/obsidian-wiki --skill copilot-history-ingest -a claude-code`. Or copy the skill folder (.skills/copilot-history-ingest in Ar9av/obsidian-wiki) into .claude/skills/copilot-history-ingest in your project. Claude Code loads it when a task matches its description.

How do I install Copilot History Ingest in Codex?

Run `npx skills add Ar9av/obsidian-wiki --skill copilot-history-ingest -a codex`. Or copy the skill folder (.skills/copilot-history-ingest in Ar9av/obsidian-wiki) into .agents/skills/copilot-history-ingest in your project. Codex loads it when a task matches its description.

Can I use Copilot History Ingest in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add Ar9av/obsidian-wiki --skill copilot-history-ingest -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/copilot-history-ingest, .gemini/skills/copilot-history-ingest, .github/skills/copilot-history-ingest and .opencode/skills/copilot-history-ingest in your project.

What does Copilot History Ingest need to run?

SKILL.md names no scripts, command-line tools or credentials: Copilot History Ingest is instructions for the agent only. Our summary lists: Python 3.

Does Copilot History Ingest access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Copilot History Ingest safe to install?

Our automated static check of SKILL.md found notes only (mentions a .env file), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.

What licence does Copilot History Ingest use?

Copilot History Ingest is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Copilot History Ingest use?

About 4.4k tokens (SKILL.md is roughly 18k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 3.1k tokens, read only when the agent opens those files.

What are the alternatives to Copilot History Ingest?

Skills that share tags, products or a category with Copilot History Ingest: Obsidian CLI (Atmosphere/atmosphere, 3.8k stars), Obsidian Canvas Boards (AgriciDaniel/claude-obsidian, 15k stars), LLM Wiki (lewislulu/llm-wiki-skill, 655 stars) and Second Brain (NicholasSpisak/second-brain, 737 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Copilot History Ingest?

Ar9av (a GitHub user) maintains it in Ar9av/obsidian-wiki, which has 3,547 GitHub stars. The repository holds 39 skills in this directory. The repository was last updated on October 10, 2026.

Source: Ar9av/obsidian-wiki on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.