RLM
Implements the Recursive Language Model (RLM) pattern for handling massive contexts that exceed token limits by treating content as external variables rather than loading directly into prompts. Supports multiple LLM providers (Claude SDK, Ollama), automatic content type detection with optimized chunking strategies, parallel batch processing with concurrency controls, and context persistence to disk. Designed for analyzing large codebases, documents, or datasets by decomposing them into manageable pieces while maintaining hierarchical processing capabilities through controlled recursion depth.
Composite of vulnerability cleanliness, spec conformance, provenance, stability, and usage signals — scanned and weighted by Cognium. Human and agent signals are tracked separately. Last scanned 2026-09-19.
Scan details: Circle-IR · 2026-09-19 · Appeal
View full trust & usage report →Metadata
- Version
- 1.0.0
- Skill type
- atomic
- Execution layer
- mcp-remote
- Category
- ai-ml
- Source
- PulseMCP
- Repository
- github.com/richardwhiteii/rlm
- Author type
- human
- Last scanned
- 2026-09-19
- Updated
- 2026-09-19
Use via MCP
Resolve RLM from your agent
Streamable HTTP transport at https://api.skillsregistry.net/mcp. No auth for read tools. Discovery: .well-known/mcp.json.
One command in your shell — Claude Code wires it up and verifies the connection. Run /mcp in any session to confirm.
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp--scope user for --scope project to commit it to .mcp.json.