A structured and highly scalable Map-Reduce workflow designed to analyze, summarize, and extract deep insights from massive documents that exceed standard LLM context windows.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install map-reduce-llm
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install map-reduce-llm using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is LLM Map-Reduce for Large Document Analysis?
The map-reduce-llm skill provides an engineered blueprint for running structured Map-Reduce workflows on large datasets and extensive textual content. When long documents exceed an LLM's context window, simple querying fails, and pure RAG (Retrieval-Augmented Generation) missing out on holistic, global understanding. This framework bridges that gap by offering a programmatic, auditable approach to document synthesis using Openclaw Skills.
By leveraging intelligent chunking, independent mapping, and iterative reduction, this skill ensures that large-scale textual analysis retains high context fidelity without suffering from information loss or hallucinations. It is completely model-agnostic, supporting sequential, parallel, hierarchical, or agentic architectures to handle arbitrary text lengths seamlessly.
LLM Map-Reduce for Large Document Analysis Use Cases
- Long Transcript Summarization: Processing massive meeting transcripts, earnings calls, or interviews to harvest key decisions, timelines, and action items.
- Technical Report & Research Synthesis: Evaluating dense multi-page academic papers, regulatory documents, or compliance sheets to extract trends and cross-references.
- Codebase Architecture Reviews: Inspecting whole modules or codebase files to detect design patterns, technical debt, and high-level architectural constraints.
- Multi-Document Comparative Analysis: Aggregating insights across a cluster of related files or documentation hubs to form unified, comprehensive reports.
How LLM Map-Reduce for Large Document Analysis Works
- Reconnaissance: The system runs initial low-level checks on the raw file to discover its encoding, structure, line count, and byte properties.
- Intelligent Chunking: The large file is cleanly divided into manageable pieces using structure-aware or recursive character methods, preserving an overlap buffer to prevent context fracturing.
- Map Phase: Each chunk is passed to an LLM independently alongside a highly constrained prompt. The model processes the segment in isolation, converting raw text into strict, self-contained structured data.
- Collapse (Optional): Adjacent or related Map outputs are merged sequentially to compress information volume before entering final synthesis.
- Reduce Phase: The summarized Map data is combined into a singular prompt where a premier model reads the distilled observations to generate global themes, find anomalies, and build the final report.
- Quality Control & Traceability: Every generated claim is automatically mapped back to its originating chunk ID for strict validation and debugging.
LLM Map-Reduce for Large Document Analysis Setup
System Pre-requisites & Inspection
First, analyze your target file's properties to select your chunking layout.
On Linux:
# Inspect basic file properties and encoding
file -i big_document.txt
# Quick chunking by line count (1000 lines per file)
split -l 1000 -d --additional-suffix=.txt big_document.txt chunk_
On Windows PowerShell:
# Efficient streaming split via StreamReader to save memory
$inputFile = "big_document.txt"
$linesPerChunk = 1000
$reader = [System.IO.StreamReader]::new($inputFile)
$chunkIndex = 0
$lineCount = 0
$writer = $null
try {
while (-not $reader.EndOfStream) {
if ($lineCount % $linesPerChunk -eq 0) {
if ($writer) { $writer.Dispose() }
$writer = [System.IO.StreamWriter]::new("chunk_{0:D3}.txt" -f $chunkIndex)
$chunkIndex++
}
$writer.WriteLine($reader.ReadLine())
$lineCount++
}
} finally {
if ($writer) { $writer.Dispose() }
$reader.Dispose()
}
LLM Map-Reduce for Large Document Analysis Data Schema & Taxonomy
The map-reduce-llm skill establishes a strict folder anatomy to maintain auditability. All generated outputs utilize explicit tracking frameworks for complete diagnostic traceability.
Run Folder Layout
| Artifact | Type | Role |
|---|---|---|
chunk_*.txt |
Source File Parts | Semantically isolated text blocks extracted from the primary document. |
map_output_*.json |
Structured Mid-layer | Extracted bullet-points, metrics, or key-value entities per chunk. |
manifest.json |
Meta Registry | An index file matching every source chunk ID to its respective Map summary. |
final_report.md |
Final Output | The synthesized executive overview featuring direct chunk citations. |
Traceability Matrix
Every claim generated during the reduction phase follows an explicit lineage pattern:
[Claim/Insight] -> Cites -> [Chunk ID] -> Verifiable against -> [Source Text Segment]
LLM Map-Reduce for Large Document Analysis Advanced Features
- Agentic Sub-Agent Delegation: Launches isolated sub-agents with clean, independent context windows to handle individual mapping tasks concurrently, granting the orchestrator virtually limitless total processing scope.
- Hierarchical Reduction Architecture: Supports multi-tiered reduction loops, processing intermediary Map outputs into group-level summaries before passing them to the final global reducer—critical for multi-gigabyte text corpuses.
- Vector RAG Fusion Filtering: Combines vector database retrieval with the pipeline to conditionally Map only high-relevance chunks instead of scanning the full file.
- Tool-Enriched Mapping: Equips processing agents with embedded tools like code interpreters, mathematical evaluators, or web search queries to enrich chunk details mid-flight.
SKILL.md
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