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.
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.
Fully automatic KB building. Install vicky, run `/vicky:learn`, watch your knowledge grow. Tier-progressive drain (high-importance files first), auto-advancing between tiers. File monitors detect changes and auto-react (relink, relearn) — zero manual intervention. No external fetches, no stub conclusions — synthesis only via `conclude`. /vicky:research auto-feeds learn. Monitors also auto-trigger on pending growth or graph changes.
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.
Vicky knowledge-base context primer. Loaded automatically on every session start. Tells Claude which MCP tools Vicky exposes, how WORKFLOW.md steers behavior, when to call dashboard/dql, and the active vault layout. Invoke manually with /vicky:setup if context has been compacted away.
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.
/vicky:learn, watch your KB grow automatically. Tier-progressive, fully hands-off.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 | Fetch sources, call learn (tier-progressive, sets up monitors) |
learn [tier] [count] | Tier-progressive: drain pending by importance, relink, create file monitors |
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 |
When you run /vicky:learn, it instructs Claude Code to create monitors on dependent files:
.graphify/graph.json changes → auto-run /vicky:relink (refresh links).graphify/.graphify_ast.json changes → auto-run /vicky:learn (re-analyze importance)pending/ grows → auto-run /vicky:learn (drain queue)sources/ changes → auto-run /vicky:relink (update graph)No periodic polling needed — reactions trigger instantly when files change. Manage monitors with /monitor list, /monitor pause vicky/, /monitor delete vicky/.... See MONITORS.md for details.
~/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
npx claudepluginhub yesitsfebreeze/vicky --plugin vickyFn-level code index MCP server. Splits source files into per-function body files for token-efficient navigation. Bidirectional watcher syncs edits.
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.
Self-organizing knowledge graph as agent memory. Auto-captures each turn, recalls relevant thoughts on demand per prompt, and exposes the kern MCP tools. Requires the `kern` CLI on PATH.
Copy-on-write overlay filesystem over the working directory, backed by a dedicated bare git store. Each session edits on its own branch; only touched files become deltas; Stop materializes them to disk.
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.
Open-source, local-first Claude Code plugin for token reduction, context compression, and cost optimization using hybrid RAG retrieval (BM25 + vector search), reranking, AST-aware chunking, and compact context packets.
Complete AI coding workflow system. Self-correcting memory + persistent FTS5-indexed research wikis + auto-research loop + multi-LLM council on a single SQLite store. 33 skills, 8 agents, 22 commands, 37 hook scripts across 24 events. Cross-agent via SkillKit.
Write SQL, explore datasets, and generate insights faster. Build visualizations and dashboards, and turn raw data into clear stories for stakeholders.
Give your AI a memory — mine projects and conversations into a searchable palace. 35 MCP tools, auto-save hooks, and guided setup.
Agent Skills for AI/ML tasks including dataset creation, model training, evaluation, and research paper publishing on Hugging Face Hub