Demand-driven knowledge base. Auto-enriches answers when gaps detected. Drives Obsidian + Dataview for live KB views.
Condense KB - absorb redundant sources into a mature conclusion. Source files move to vicky/sources/.absorbed/ (hidden dotfolder, excluded from queries/dashboard/graph), conclusion records them under derived_from: frontmatter. Crystallization keeps the KB small while preserving provenance. Use /vicky:crystalize when one conclusion is the canonical takeaway and underlying sources no longer need to surface. Always dry-run first.
Autonomous code-optimization loop. Reads experiment.md task queue, estimates time per task, executes in git worktrees (build + test + perf benchmark), measures actual time, learns estimates, merges improvements. Capacity-queue scheduling fills 30-min cycles and repeats. Use /vicky:experiment to start the loop. Estimates self-correct over time. Works for rendering, shader, physics, LOD optimization.
Docs → KB sync. Walks the repository for markdown / text documentation outside `vicky/`, finds files that changed since the last index pass, and mirrors them into `vicky/sources/`. Skips code, binaries, generated artefacts, and the vault itself. Use /vicky:index after editing READMEs, ADRs, or any project docs so Vicky stays current.
Walk the KB and connect what is already there. Drains the pending queue into sources and rebuilds the graph. No external fetches, no stub conclusions — conclusions are only born from real synthesis via `conclude` / `complete-research`. For new external data use /vicky:research, which calls this skill at the end of its own pipeline. Use /vicky:learn on its own when pending notes arrived from elsewhere (git pull, sibling agents, cron) and just need absorbing.
Get new data into the KB. Web-searches the topic, attaches findings to a pending stub, then auto-calls the `learn` MCP tool so the new material is promoted to a source and relinked. Conclusions are written separately via `conclude` once you have a real takeaway. Use /vicky:research "<topic>" when the KB has a gap on a specific subject.
Admin access level
Server config contains admin-level keywords
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Demand-driven knowledge base MCP server. Auto-enriches answers when gaps detected. Drives Obsidian + Dataview for live KB views.
dashboard / dql tools)
graphifyships as a bundled dependency (graphifyy on npm).npm installpulls it in — no external install needed. Vicky resolves the local binary at runtime.
/plugin marketplace add yesitsfebreeze/vicky
/plugin install vicky@vicky
Registers the MCP server, hooks init() to SessionStart, and exposes the vicky skill. First session scaffolds the Obsidian preset (.obsidian/ with Dataview + graph) at the project root and vicky/{sources,conclusions,pending,graphs,Dashboard.md,WORKFLOW.md} beside it. The project root is the Obsidian vault.
git clone https://github.com/yesitsfebreeze/vicky ~/vicky
cd ~/vicky/src && npm install && npm run build
Register the MCP server pointing at ~/vicky/dist/index.js. Example .mcp.json:
{
"mcpServers": {
"vicky": {
"command": "node",
"args": ["~/vicky/dist/index.js"]
}
}
}
Restart the agent. First call triggers init(). Open the project root in Obsidian — the .obsidian/ preset enables Dataview + graph settings. (Plugins must be installed via the Community Plugins UI on first run; the preset only declares which are enabled.)
cd ~/vicky && git pull && cd src && npm install && npm run build
Restart the agent so the MCP server reloads. vicky/ is yours — init() never overwrites existing files.
| Tool | Purpose |
|---|---|
research-gap "q" | Query KB; auto-enqueue if gap |
research | Drain pending queue → conclusion stubs |
query "q" | Direct KB lookup (focus-biased) |
remember "title" | Save findings to vault |
enqueue "q" | Manual queue add |
dashboard | KB report via Obsidian + Dataview |
dql "<query>" | Run arbitrary DQL. query="help" for syntax |
relink | Rebuild link graph |
web-search | Web research helper |
~/vicky/ # plugin root (clone target)
├── .claude-plugin/ # plugin manifest
├── hooks/ # SessionStart init hook
├── dist/ # bundled MCP server (CI-built, committed)
├── scaffold/ # template tree mirroring project root (copied to . on init)
├── skills/ # Claude Code skills
├── src/ # source + build (node_modules lives here)
│ ├── package.json
│ ├── build.js
│ ├── index.js, init.js, dashboard.js, ...
│ └── tools/
└── README.md # this file
<your project>/ # ← Obsidian vault (open this in Obsidian)
├── .obsidian/ # Obsidian preset (Dataview + graph + plugin list)
└── vicky/ # created by init() in each project
├── sources/ # external research, papers
├── conclusions/ # synthesized knowledge
├── pending/ # queued research questions
├── .graphify/ # graphify state — graph.json (semantic, BFS target)
├── graphs/ # Dataview-queryable wiki: vicky.md + cluster pages
├── .graphifyignore # extract scope (excludes pending/, graphs/, etc.)
├── WORKFLOW.md # focus, rules, routing (edit to steer Vicky)
└── Dashboard.md # live Dataview views
Single source of truth for runtime behavior. Edit frontmatter:
active_focus: [perf, nanite] # bias query results + Dashboard
priority_tags: [blocker]
auto_enqueue: true # false = gaps don't auto-queue
default_workflow: default # default | deep-dive | triage
Sections: Focus, Active Rules, Workflows, Routing (regex → workflow). Re-read on every tool call.
Vicky drives obsidian.exe eval to run real DQL inside Obsidian's Dataview plugin. Same engine as the human-facing Dashboard.md, no separate renderer.
Full DQL reference: https://blacksmithgu.github.io/obsidian-dataview/queries/structure/
Top queries the agent uses:
# Hubs (most-referenced)
TABLE WITHOUT ID file.link AS Node, length(file.inlinks) AS Inlinks
FROM "conclusions" OR "sources"
WHERE length(file.inlinks) > 0
SORT length(file.inlinks) DESC LIMIT 20
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Stack entry point. Routing decision table + setup checklist + maintenance audit as auto-loaded skills. Ships SessionStart + PreToolUse routing hooks, /stack:doctor, /stack:bootstrap, /stack:update.
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Persistent Obsidian-vault knowledge base — ingest, search, and link knowledge with OpenAI embeddings and Tantivy full-text search.
The machine — a portable Claude Code payload: eager-generalist default agent, specialist sub-agents, on-demand skills, lifecycle hooks, output styles, and the hub singleton daemon (coordination, board, mine catalog). Pairs with the kern memory plugin, installed alongside. Specialized per-repo by /oil into /.machine.
MCP server that saves 98% of your context window with session continuity. Sandboxed code execution in 11 languages, FTS5 knowledge base with BM25 ranking, and automatic state restore across compactions.
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