Atlas - Persistent Semantic Memory

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Atlas ingests project documentation and code into persistent semantic memory using Qdrant and Voyage embeddings. It enables cross-session context retrieval via semantic search, file/directory ingestion, and temporal filtering. Useful when project context exceeds a single session or you need to recall past decisions and patterns.

Sby Skills Guide Bot
ProductivityIntermediate
6/2/2026
Claude Code
#semantic-memory#vector-database#context-ingestion#persistent-search#qdrant

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Automated summary

Atlas ingests project documentation and code into persistent semantic memory using Qdrant and Voyage embeddings for cross-session context retrieval.

Strengths

  • Fast, accurate semantic search across large file collections
  • Dual temporal and semantic indexing for flexible retrieval
  • Native integration with Claude Code via MCP
  • Recursive directory ingestion with smart filtering

Limitations

  • Requires a local Qdrant instance and a Voyage API key
  • Non-trivial memory and disk usage with many vectors
  • Does not support images or binary files
When to use it

Use Atlas when you need to retain and query project context across sessions without losing information.

When not to use it

Avoid Atlas for one-off tasks with minimal context or if you cannot maintain the required infrastructure.

Security analysis

Safe
Quality score93/100

The skill uses specific bun commands for document ingestion and semantic search via an external embedding API without destructive or exfiltrating patterns. It does not contain malicious payloads, obfuscated code, or instructions to disable safety.

No concerns found

How this analysis is made

Examples

Remember API architecture
Remember the API architecture we discussed last week.
Find authentication patterns
Find all mentions of authentication patterns in our codebase.
Ingest research files
Ingest all the .atlas research files from the research directory.

name: atlas description: Ingest project documentation and code into persistent semantic memory (Qdrant + Voyage embeddings). Use when user wants to remember context across sessions, ingest docs, or search previous work. Requires Qdrant running locally and VOYAGE_API_KEY set. allowed-tools: Bash(bun:*)

Atlas - Persistent Semantic Memory

Atlas provides automatic context ingestion and retrieval using Voyage embeddings + Qdrant vector database. Solves the context overflow problem by storing knowledge persistently across sessions.

Quick Start

Prerequisites

  1. Qdrant running locally:
docker run -d -p 6333:6333 qdrant/qdrant
  1. VOYAGE_API_KEY set (get from https://voyageai.com):
export VOYAGE_API_KEY="your-key-here"
  1. Verify setup:
curl http://localhost:6333/health

Ingesting Context

Store files in Atlas memory for persistent retrieval:

Ingest Single File

cd ~/production/atlas
bun run --filter @inherent.design/atlas atlas ingest /path/to/file.md

Ingest Directory (Recursive)

cd ~/production/atlas
bun run --filter @inherent.design/atlas atlas ingest /path/to/docs/ --recursive

Ingest Multiple Paths

cd ~/production/atlas
bun run --filter @inherent.design/atlas atlas ingest README.md src/index.ts docs/ -r

What gets ingested:

  • Supported: .md, .ts, .tsx, .js, .jsx, .json, .yaml, .qntm, .rs, .go, .py, .sh, .css, .html
  • Ignored: node_modules, .git, dist, build, coverage, .atlas

Processing:

  • Chunks text (768 tokens, 13% overlap) for semantic coherence
  • Embeds with Voyage-3-large (1024-dim)
  • Stores in Qdrant with dual-indexing (semantic QNTM keys + temporal timestamps)
  • Preserves original text for future consolidation

Searching Context

Retrieve relevant context semantically:

Basic Search

cd ~/production/atlas
bun run --filter @inherent.design/atlas atlas search "typescript patterns"

Limited Results

cd ~/production/atlas
bun run --filter @inherent.design/atlas atlas search "memory consolidation" --limit 10

Temporal Filtering (Since Date)

cd ~/production/atlas
bun run --filter @inherent.design/atlas atlas search "sleep patterns" --since "2025-12-25"

Chronological Timeline

cd ~/production/atlas
bun run --filter @inherent.design/atlas atlas timeline --since "2025-12-01"

When to Use This Skill

Use Atlas when:

  • User asks to "remember this across sessions"
  • Project context is too large for single session
  • User wants to search previous work/decisions
  • Documentation needs to be queryable
  • Building on previous research or code

Examples:

  • "Remember the API architecture we discussed"
  • "What did we decide about the database schema?"
  • "Find all mentions of authentication patterns"
  • "Ingest all the .atlas research files"

Architecture

Built on .atlas research (Steps 1-4 + Sleep Patterns):

Stack:

  • Voyage-3-large embeddings (1024-dim, 9.74% better than OpenAI)
  • Qdrant HNSW index (M=16, int8 quantization, 4x compression)
  • RecursiveCharacterTextSplitter (semantic boundaries)
  • Dual-indexing (QNTM semantic keys + RFC 3339 timestamps)

Production Config (from Step 3 research):

  • Recall@10: >0.98
  • Latency: 10-50ms (p95)
  • Memory: 1.4GB RAM + 5GB disk per 1M vectors

Technical Details

For implementation details, see:

Packages:

  • @inherent.design/atlas-core - Core library (embeddings, storage, search)
  • @inherent.design/atlas - Command-line interface
  • @inherent.design/atlas-mcp - MCP server for Claude Code integration

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1. Download the file

Download SKILL.md

From the root of a test project, save the file as .skill-downloads/atlas-persistent-semantic-memory-mm5t8apq/SKILL.md, or use the command below. This folder is for inspecting the skill before installation.

Command for macOS/Linux or WSL: requires a POSIX shell and curl. On Windows without WSL, use the download button and create the folder manually.

mkdir -p '.skill-downloads/atlas-persistent-semantic-memory-mm5t8apq' && curl --fail --show-error --location 'https://skills-guides.com/api/skills/atlas-persistent-semantic-memory-mm5t8apq/download' --output '.skill-downloads/atlas-persistent-semantic-memory-mm5t8apq/SKILL.md.part' && mv '.skill-downloads/atlas-persistent-semantic-memory-mm5t8apq/SKILL.md.part' '.skill-downloads/atlas-persistent-semantic-memory-mm5t8apq/SKILL.md'

2. Check prerequisites

Read SKILL.md before using it. If it references scripts or external services, inspect them and prepare the required access in your test environment.

Tools declared by the author (check availability in your agent): Bash(bun:*)

Original excerpt: Prerequisites
  1. Qdrant running locally:
docker run -d -p 6333:6333 qdrant/qdrant
  1. VOYAGE_API_KEY set (get from https://voyageai.com):
export VOYAGE_API_KEY="your-key-here"
  1. Verify setup:
curl http://localhost:6333/health

The platforms on this page are catalog indications. The paths below follow tool documentation; they do not prove this skill has been run in those tools. No minimum tool version is confirmed for this listing.

3. Load the skill

After inspection, copy the complete folder into your project using one of the commands below, then open a new tool session. The command stops if a folder with the same name already exists: compare the files before replacing it.

Claude Code
test -f '.skill-downloads/atlas-persistent-semantic-memory-mm5t8apq/SKILL.md' && test -s '.skill-downloads/atlas-persistent-semantic-memory-mm5t8apq/SKILL.md' && mkdir -p '.claude/skills' && test ! -e '.claude/skills/atlas' && cp -R '.skill-downloads/atlas-persistent-semantic-memory-mm5t8apq' '.claude/skills/atlas'

OpenSkills

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Official OpenSkills installation
openskills install './.skill-downloads/atlas-persistent-semantic-memory-mm5t8apq'

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Read .skill-downloads/atlas-persistent-semantic-memory-mm5t8apq/SKILL.md and apply its instructions to this example: [describe a test input]. Identify any missing files, dependencies or access. Produce the result requested by the skill without inventing data or running unreviewed scripts.

Expected result: output matching the skill instructions, or a precise list of missing prerequisites. A read confirmation alone does not validate a first use. If an external resource is unavailable, that path remains untested.

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