Scholar RAG
joshzyj/open-scholar-skill
Build and query a local vector database + GraphRAG over your entire reference library (Zotero or a PDF folder) for literature review.
SOTA semantic search — hybrid (sparse+dense), Graph RAG multi-hop, MMR diversity reranking, recency weighting
Install Memory Search
$ npx skills add ruvnet/ruflo --skill memory-search -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install ruvnet/ruflo memory-search --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/ruvnet/ruflo.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/ruflo-rag-memory/skills/memory-search .claude/skills/memory-search && rm -rf skills-srcUse ~/.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/
Install the "memory-search" agent skill from https://github.com/ruvnet/ruflo/tree/main/plugins/ruflo-rag-memory/skills/memory-search into .claude/skills/memory-search/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "memory-search", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/ruvnet/ruflo/tree/main/plugins/ruflo-rag-memory/skills/memory-searchType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add ruvnet/ruflo --skill memory-search -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install ruvnet/ruflo memory-search --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ruvnet/ruflo.git skills-src && mkdir -p .agents/skills && cp -r skills-src/plugins/ruflo-rag-memory/skills/memory-search .agents/skills/memory-search && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "memory-search" agent skill from https://github.com/ruvnet/ruflo/tree/main/plugins/ruflo-rag-memory/skills/memory-search into .agents/skills/memory-search/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "memory-search", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add ruvnet/ruflo --skill memory-search -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install ruvnet/ruflo memory-search --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ruvnet/ruflo.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/plugins/ruflo-rag-memory/skills/memory-search .cursor/skills/memory-search && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "memory-search" agent skill from https://github.com/ruvnet/ruflo/tree/main/plugins/ruflo-rag-memory/skills/memory-search into .cursor/skills/memory-search/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "memory-search", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/ruvnet/ruflo.git --path plugins/ruflo-rag-memory/skills/memory-search--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add ruvnet/ruflo --skill memory-search -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install ruvnet/ruflo memory-search --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ruvnet/ruflo.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/plugins/ruflo-rag-memory/skills/memory-search .gemini/skills/memory-search && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "memory-search" agent skill from https://github.com/ruvnet/ruflo/tree/main/plugins/ruflo-rag-memory/skills/memory-search into .gemini/skills/memory-search/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "memory-search", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install ruvnet/ruflo memory-searchInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add ruvnet/ruflo --skill memory-search -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/ruvnet/ruflo.git skills-src && mkdir -p .github/skills && cp -r skills-src/plugins/ruflo-rag-memory/skills/memory-search .github/skills/memory-search && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "memory-search" agent skill from https://github.com/ruvnet/ruflo/tree/main/plugins/ruflo-rag-memory/skills/memory-search into .github/skills/memory-search/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "memory-search", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add ruvnet/ruflo --skill memory-search -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install ruvnet/ruflo memory-search --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ruvnet/ruflo.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/plugins/ruflo-rag-memory/skills/memory-search .opencode/skills/memory-search && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "memory-search" agent skill from https://github.com/ruvnet/ruflo/tree/main/plugins/ruflo-rag-memory/skills/memory-search into .opencode/skills/memory-search/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "memory-search", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
memory-search- GitHub stars
- 74k
- Token cost
- ~761 tokens
- SKILL.md length
- 231 words
- Files
- 1
- Skills in repo
- 264
- Repo updated
- First seen
- Licence
- MIT
At a glance
SOTA semantic search — hybrid (sparse+dense), Graph RAG multi-hop, MMR diversity reranking, recency weighting
- Works in 6 steps: Parse query and flags — extract search… → Select retrieval strategy → Apply MMR reranking — for diverse… → …
- Tasks that involve Retrieval-augmented generation
- SKILL.md covers Strategy Selection, Steps and Namespace Guide
- Calls npx
What it does
Memory Search is an agent skill from ruvnet/ruflo. SOTA semantic search — hybrid (sparse+dense), Graph RAG multi-hop, MMR diversity reranking, recency weighting
Its SKILL.md is about 760 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in AI & LLM Engineering, covering Retrieval-augmented generation and Knowledge graphs. It works with Model Context Protocol. The repository describes itself as: 🌊 The original agent harness. Deploy intelligent multi-player swarms, coordinate autonomous workflows, and build conversational AI systems. Features adaptive memory…. The licence is MIT.
When your agent uses it
- Tasks that involve Retrieval-augmented generation
- Tasks that involve Knowledge graphs
Example prompts
- “/memory-search”
Requirements
- Node.js
- Pre-approved tools (allowed-tools): Bash, Read, mcp__plugin_ruflo-core_ruflo__memory_search, mcp__plugin_ruflo-core_ruflo__memory_store, mcp__plugin_ruflo-core_ruflo__memory_list, mcp__plugin_ruflo-core_ruflo__memory_retrieve, mcp__plugin_ruflo-core_ruflo__memory_search_unified, mcp__plugin_ruflo-core_ruflo__agentdb_pattern-search, mcp__plugin_ruflo-core_ruflo__agentdb_context-synthesize
Workflow steps
6 steps, taken from the first numbered list in SKILL.md.
- Parse query and flags — extract search text and strategy flags from arguments
- Select retrieval strategy
- Apply MMR reranking — for diverse results, filter near-duplicates (cosine > 0.92) while maximizing relevance
- Apply recency weighting — boost recent entries with exponential decay (0.95/day)
- Synthesize context (for complex queries)
- Present results — ranked by composite score (relevance * diversity * recency), with source namespace attribution
What it can do on your machine
Read from SKILL.md and the folder at commit 6051f67. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves these tools, so the agent can use them without asking each time:
BashReadmcp__plugin_ruflo-core_ruflo__memory_searchmcp__plugin_ruflo-core_ruflo__memory_storemcp__plugin_ruflo-core_ruflo__memory_listmcp__plugin_ruflo-core_ruflo__memory_retrievemcp__plugin_ruflo-core_ruflo__memory_search_unifiedmcp__plugin_ruflo-core_ruflo__agentdb_pattern-searchmcp__plugin_ruflo-core_ruflo__agentdb_context-synthesize
From allowed-tools in the SKILL.md frontmatter.
Runs code
Shell commands in SKILL.md call:
npx
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md. Its commands use npx, which can reach the network depending on how they are called.
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
Memory Search loads about 761 tokens when it runs. Until then it costs about 31 tokens; SKILL.md has 231 words of instructions outside code blocks.
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.
- NotePre-approves every shell command (allowed-tools: Bash)SKILL.md
allowed-tools: Bash, Read, mcp__plugin_ruflo-core_ruflo__memory_search, mcp__plugin_ruflo-core_ruflo__memory_store,
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 ruvnet/ruflo at commit 6051f67, republished under its MIT licence (© ruvnet). 231 words, ~761 tokens.
Download SKILL.mdSave it as
.claude/skills/memory-search/SKILL.md (or your agent's skills folder).- name
- memory-search
- description
- SOTA semantic search — hybrid (sparse+dense), Graph RAG multi-hop, MMR diversity reranking, recency weighting
- allowed-tools
- Bash, Read, mcp__plugin_ruflo-core_ruflo__memory_search, mcp__plugin_ruflo-core_ruflo__memory_store, mcp__plugin_ruflo-core_ruflo__memory_list, mcp__plugin_ruflo-core_ruflo__memory_retrieve, mcp__plugin_ruflo-core_ruflo__memory_search_unified, mcp__plugin_ruflo-core_ruflo__agentdb_pattern-search, mcp__plugin_ruflo-core_ruflo__agentdb_context-synthesize
- argument-hint
- <query> [--hybrid] [--graph-rag] [--namespace NAME]
Memory Search (SOTA)
State-of-the-art semantic search across Ruflo memory with multiple retrieval strategies.
Strategy Selection
Choose based on query type:
- Default (dense): fast single-hop semantic match
- --hybrid: sparse + dense with RRF fusion (20-49% better for keyword+semantic queries)
- --graph-rag: multi-hop knowledge retrieval (30-60% better for reasoning queries)
Steps
Parse query and flags — extract search text and strategy flags from arguments
Select retrieval strategy:
Dense search (default):
bash npx @claude-flow/cli@latest memory search --query "QUERY" --namespace NAMESPACE --limit 10Or via MCP:
mcp__plugin_ruflo-core_ruflo__memory_search({ query: "QUERY", namespace: "NAMESPACE", limit: 10 })Hybrid search (when --hybrid or query has specific keywords):
bash npx ruvector search "QUERY" --hybrid --limit 10Graph RAG (when --graph-rag or multi-hop reasoning needed):
bash npx ruvector search "QUERY" --graph-rag --limit 10Smart retrieval (when --smart or complex recall needed):
bash npx @claude-flow/cli@latest memory search --query "QUERY" --smart --limit 10Or via MCP:
mcp__plugin_ruflo-core_ruflo__memory_search({ query: "QUERY", smart: true, limit: 10 })Applies 5-phase pipeline: query expansion, RRF fusion, recency boost, MMR diversity, session round-robin. Best for: multi-session recall, temporal queries, diverse result sets.
Unified cross-namespace:
mcp__plugin_ruflo-core_ruflo__memory_search_unified({ query: "QUERY", limit: 10 })Apply MMR reranking — for diverse results, filter near-duplicates (cosine > 0.92) while maximizing relevance
Apply recency weighting — boost recent entries with exponential decay (0.95/day)
Synthesize context (for complex queries):
mcp__plugin_ruflo-core_ruflo__agentdb_context-synthesize({ query: "QUERY", sources: ["patterns", "tasks", "solutions"] })Present results — ranked by composite score (relevance * diversity * recency), with source namespace attribution
Namespace Guide
| Namespace | Best For |
|---|---|
patterns | "How did we handle X?" |
tasks | "What was the context for Y?" |
solutions | "How did we fix Z?" |
feedback | "What did the user prefer?" |
security | "Known vulnerabilities in..." |
| (omit) | Search all namespaces |
© ruvnet, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in plugins/ruflo-rag-memory/skills/memory-search of ruvnet/ruflo.
Open the folder on GitHubat commit 6051f67
Compare with similar skills
Memory Search 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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Memory Search this skillruvnet/ruflo | 74k | — | ~761 | Automated safety check: Notes | MIT | |
| Scholar RAGjoshzyj/open-scholar-skill | 168 | — | ~7.4k | Automated safety check: Notes | Custom licence | |
| Docsmint Document ManagerHiAi-gg/docsmint | 118 | — | ~584 | Automated safety check: Pass | Apache-2.0 | |
| Datamind ContextOpenDCAI/DataMind | 423 | — | ~528 | Automated safety check: Pass | Apache-2.0 | |
| MCP Local RAGshinpr/mcp-local-rag | 407 | — | ~4.4k | Automated safety check: Pass | MIT | |
| Local RAG Searchnkapila6/mcp-local-rag | 134 | 1 repos | ~1.6k | Automated safety check: Pass | MIT |
Similar skills
Docsmint Document Manager
HiAi-gg/docsmint
Manage and research DocsMint documents through its scoped MCP tools, including categories, folders, hybrid search, GraphRAG, rerank, and index refresh.
Datamind Context
OpenDCAI/DataMind
Use DataMind from Codex to ingest local files, query RAG or graph knowledge, store facts, and inspect profiles.
MCP Local RAG
shinpr/mcp-local-rag
Searches, saves, and maintains a local document index through a local RAG MCP server.
Local RAG Search
nkapila6/mcp-local-rag
Efficiently perform web searches using the mcp-local-rag server with semantic similarity ranking.
Pgvector Semantic Search
timescale/pg-aiguide
A skill your agent uses for setting up vector similarity search with pgvector for AI/ML embeddings, RAG applications, or semantic search.
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SPARC Development Methodology
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Applies the SPARC method (specification, pseudocode, architecture, refinement, completion) with 17 specialized modes and multi-agent orchestration, from research to deployment.
Swarm Orchestration
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Coordinates a hierarchical swarm of specialized agents through the claude-flow CLI for work that spans several files or modules at once.
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Sets up and drives Ruflo, an npm-installed orchestration layer for multi-agent swarms, persistent memory, routing, hooks and its MCP tool catalog.
Agent Coordination
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Related
Works with
Categories
Questions about Memory Search
What does Memory Search do?
SOTA semantic search — hybrid (sparse+dense), Graph RAG multi-hop, MMR diversity reranking, recency weighting. Memory Search is an agent skill from ruvnet/ruflo.
When should I use Memory Search?
Memory Search fits situations like: tasks that involve Retrieval-augmented generation; tasks that involve Knowledge graphs.
How do I install Memory Search in Claude Code?
Run `npx skills add ruvnet/ruflo --skill memory-search -a claude-code`. Or copy the skill folder (plugins/ruflo-rag-memory/skills/memory-search in ruvnet/ruflo) into .claude/skills/memory-search in your project. Claude Code loads it when a task matches its description.
How do I install Memory Search in Codex?
Run `npx skills add ruvnet/ruflo --skill memory-search -a codex`. Or copy the skill folder (plugins/ruflo-rag-memory/skills/memory-search in ruvnet/ruflo) into .agents/skills/memory-search in your project. Codex loads it when a task matches its description.
Can I use Memory Search 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 ruvnet/ruflo --skill memory-search -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/memory-search, .gemini/skills/memory-search, .github/skills/memory-search and .opencode/skills/memory-search in your project.
What does Memory Search need to run?
Going by SKILL.md and its folder, Memory Search needs the command-line tools its instructions call (npx). Our summary lists: Node.js. Its frontmatter pre-approves these tools: Bash, Read, mcp__plugin_ruflo-core_ruflo__memory_search, mcp__plugin_ruflo-core_ruflo__memory_store, mcp__plugin_ruflo-core_ruflo__memory_list, mcp__plugin_ruflo-core_ruflo__memory_retrieve, mcp__plugin_ruflo-core_ruflo__memory_search_unified, mcp__plugin_ruflo-core_ruflo__agentdb_pattern-search, mcp__plugin_ruflo-core_ruflo__agentdb_context-synthesize.
Does Memory Search access the network?
SKILL.md contains no URLs. Its commands use npx, which can reach the network depending on how they are called. This is read from the text; nothing was executed.
Is Memory Search safe to install?
Our automated static check of SKILL.md found notes only (pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.
What licence does Memory Search use?
Memory Search 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 Memory Search use?
About 761 tokens (SKILL.md is roughly 3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Memory Search?
Skills that share tags, products or a category with Memory Search: Scholar RAG (joshzyj/open-scholar-skill, 168 stars), Docsmint Document Manager (HiAi-gg/docsmint, 118 stars), Datamind Context (OpenDCAI/DataMind, 423 stars) and MCP Local RAG (shinpr/mcp-local-rag, 407 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Memory Search?
ruvnet (a GitHub user) maintains it in ruvnet/ruflo, which has 74,089 GitHub stars. The repository holds 264 skills in this directory. The repository was last updated on October 8, 2026.
Source: ruvnet/ruflo on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.