synthesis

Tapestry Synthesis

Analyze a stored Tapestry artifact: $ARGUMENTS

When to use this skill

Use this skill when:

• A user wants interpretation or analysis of already-ingested content
• You need to consolidate multiple sources into research notes
• The user asks for synthesis, insights, or higher-level understanding
• You need to organize ingested content into the knowledge base hierarchy
• The deterministic ingest is complete and model-based reasoning is needed

Overview

This skill is the workflow layer for model-eligible reasoning.

Use it after deterministic ingest has already produced:

1. a capture JSON file
2. a normalized feed JSON file
3. a Markdown knowledge-base note
4. a structured handoff payload describing the recommended analysis

The package runtime does not call any model APIs directly. This skill owns the interpretation step.

This skill is also responsible for maintaining the book-like knowledge base under knowledge-base/.

Important: The synthesis mode configuration controls when this skill should be invoked:

• auto: Agent evaluates note accumulation and decides whether to merge (intelligent, load-based)
• manual: Only runs when explicitly requested by user
• batch: Runs after multiple ingests to process all at once
• deterministic: Runs automatically after every single ingest (high overhead)

See config/tapestry.config.json for current mode setting.

Workflow

1. Identify the target:
   • if the user gave a stored note path, use it directly
   • if the user gave a URL and it has not been ingested yet, run $tapestry-ingest first
2. IMPORTANT: Change to the project root directory before running the script:

cd /path/to/your/tapestry/project
python synthesis/_scripts/load_context.py \
  "$ARGUMENTS"

Or use the --project-root flag:

python synthesis/_scripts/load_context.py \
  --project-root /path/to/your/tapestry/project \
  "$ARGUMENTS"

1. Read the returned handoff payload carefully:
   • digest is the deterministic baseline
   • analysis.skill, analysis.instructions, and analysis.deliverable describe the intended workflow
   • note_text, feed_payload, and capture_payload are the factual source materials
   • Identify the language by reading the content naturally (title, body, comments)
2. Ensure the book hierarchy exists: