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name: longread description: > Use this skill when an agent (main agent or subagent) encounters a file too large to read in a single pass — e.g. cat, read_file, or Read tool hits size limits or truncates output. First assess whether the file is suitable for chunk-based parallel reading, then proceed accordingly. Supports PDF, DOCX, TXT, MD, PPTX files. NOT for structured data (CSV, DTA, XLSX, etc.). user-invocable: true allowed-tools: Bash, Read, Agent, Glob argument-hint: <file_path>

Longread Skill

Use this skill when a file is too large to read in a single pass (e.g. cat, read_file, or Read tool hits size limits or truncates output).

Step 0: Assess Suitability (REQUIRED)

Before splitting, determine whether the file is actually suitable for the chunk-and-summarize pattern. Not all large files benefit from this approach.

Files SUITABLE for this skill (non-structured, prose-like content):

• PDF documents (reports, papers, books, manuals)
• DOCX documents (articles, contracts, essays)
• TXT / MD files (long-form text, documentation)
• PPTX files (slide decks with text content)

Files NOT suitable — use code instead:

• CSV, TSV, DTA, XLS/XLSX — structured/tabular data. Use pandas, Stata, or other data tools to query, filter, aggregate. Splitting rows across chunks destroys data integrity.
• JSON, JSONL — structured data. Use jq or Python to parse and extract.
• Log files — typically need grep/awk/filtering, not summarization.
• Source code files — use grep, AST tools, or targeted reads with offset/limit.

Also consider whether the task itself fits the pattern:

• Suitable tasks: summarization, information extraction, question answering over prose, finding specific sections in a long document.
• Unsuitable tasks: statistical analysis, counting, aggregation, joins, sorting, exact search — these need code, not parallel reading.

If the file or task is unsuitable, do NOT proceed with this skill. Instead, use the appropriate tool (Python/pandas for data, grep for logs, targeted Read with offset for code, etc.) and tell the user why you chose that approach.

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Workflow (only after confirming suitability)

Step 1: Split the Document

python /app/.agents/skills/longread/scripts/split_doc.py <file_path>

The script will output JSON with chunk file paths:

{
  "status": "success",
  "chunk_files": ["/mnt/agents/chunks/doc_part_1.txt", ...],
  "num_chunks": 5
}

Step 2: Create a Reader Subagent

create_subagent(
  name="chunk_reader",
  system_prompt="You are a document reader. Read the assigned chunk carefully and extract key information. Summarize the main points concisely."
)

Step 3: Launch Parallel Tasks

For each chunk file, launch a subagent in parallel (single message, multiple tool calls):

task(agent="chunk_reader", prompt="Read /mnt/agents/chunks/doc_part_1.txt and summarize the key points.")
task(agent="chunk_reader", prompt="Read /mnt/agents/chunks/doc_part_2.txt and summarize the key points.")
...

Step 4: Aggregate Results

After all subagents complete, combine their summaries to answer the user's original question.

Script Options

The split script supports:

• PDF, DOCX, TXT, MD, PPTX files
• Default chunk size: 32k tokens with 10% overlap
• Output directory: /mnt/agents/chunks/