agents-team-book-metadata

Extract and validate chapter-level novel metadata with a dual-agent workflow.

523|125|Updated Nov 11, 2025

Search Tags:#memory #metadata #parser #novel #chapter #checker #dual-agent

Ready to run

npx skills add https://github.com/anymouschina/TapCanvas --skill agents-team-book-metadata

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: agents-team-book-metadata
Source: https://github.com/anymouschina/TapCanvas/tree/main/apps/agents-cli/skills/agents-team-book-metadata
Command: npx skills add https://github.com/anymouschina/TapCanvas --skill agents-team-book-metadata

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill enables a coordinated dual-agent workflow to extract complete per-chapter metadata for novels and persist it in a searchable memory index, ensuring traceability and resumable runs.

Core Features & Use Cases

Collaborative parsing and validation: a parser builds chapter-level metadata, a checker validates completeness and consistency, and both outputs are written to a structured memory layout.
Memory-backed continuation: results are stored in .agents/memory/books/<BookSlug>/metadata for easy retrieval and resumption.
End-to-end story workflows: suitable for novel chapter metadata extraction, character/plot graph creation, and integration into downstream storyboard and generation tasks.

Quick Start

Spawn the agents-team-book-metadata pipeline to process a novel's chapters and store the metadata in the memory index.

Frequently Asked Questions about agents-team-book-metadata

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I extract per-chapter metadata for a novel automatically?▼

You extract per-chapter metadata for a novel by running a dual-agent workflow where a parser generates the metadata and a checker validates completeness, assembling the final JSON output. This automated process ensures traceability and structured memory indexing for long-form storytelling projects.

What is a dual-agent workflow for chapter metadata validation?▼

A dual-agent workflow for chapter metadata validation uses one agent to parse and build chapter-level metadata, while a second agent checks the output for completeness and consistency. Both outputs are then written to a structured memory layout to ensure data quality and resumable execution.

How do I index novel chapters into a searchable memory for resumable runs?▼

To index novel chapters into a searchable memory, the workflow persists extracted metadata and structured progress files into the .agents/memory path. This creates a memory-backed continuation layout that enables easy retrieval and resumption of chapter processing tasks.

Can I generate character and plot metadata for long-form storytelling projects?▼

Yes, you can generate character and plot metadata for long-form storytelling projects using this pipeline. The dual-agent extraction process creates per-chapter metadata suitable for character and plot graph creation, which can integrate directly into downstream storyboard and generation tasks.

Do I need a specific environment setup to run a metadata parser and checker pipeline?▼

Running a metadata parser and checker pipeline requires loading the agents-team and cognitive-memory modules to execute the defined sequence. The workflow enforces this specific load order to assemble the final JSON and persist results correctly to the structured memory index.

Why does a chapter metadata extraction workflow need a separate checker agent?▼

A chapter metadata extraction workflow needs a separate checker agent to validate the parser's output for completeness and consistency. This dual-agent validation prevents malformed metadata from entering the memory index, ensuring that downstream storyboard and generation tasks receive standardized, reliable JSON data.