rsvp-reading

Dependencies

This skill requires Python 3.8+ and standard library only. No external packages needed.

To install this skill's dependencies:

pip-compile ./requirements.in
pip install -r ./requirements.txt

See ./requirements.txt for the dependency lockfile (currently empty — standard library only).

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RSVP Reading Skill

Rapid Serial Visual Presentation (RSVP) is a speed reading method popularized by tools like Spritz. Words are flashed one at a time in a fixed position, while one letter per word is highlighted (typically in red) as an eye anchor — the Optimal Recognition Point (ORP). This eliminates horizontal eye movement, the primary bottleneck of traditional reading, enabling speeds of 200-600+ WPM with solid comprehension.

This skill converts any document into an RSVP token stream: each word paired with its ORP index and a calibrated display delay based on your target WPM.

Full architecture: references/architecture/architecture.md Acceptance criteria: references/acceptance-criteria.md Fallback tree: references/fallback-tree.md Token stream schema: references/token-stream-schema.md

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Trigger Conditions

Invoke this skill when the user:

• Says "speed read [file]", "RSVP [file]", or "read [file] at [N] WPM"
• Uploads or references a document and asks to "read it fast"
• Requests a token stream or reading session from a document
• Mentions "Rapid Serial Visual Presentation", "Spritz", or "ORP alignment"
• Wants to "flash-read" content at a target WPM
• Asks to convert a document into an RSVP-compatible stream for a reading UI

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Discovery Phase

Before executing, collect:

1. Input file path - What file should be parsed? (.txt, .md, .pdf, .docx)
2. WPM - Reading speed in words-per-minute. Default: 300. Range: 100-1000.
3. Output format - Where to save the token stream JSON? Default: ./rsvp_output.json
4. Pause at sentence ends - Insert extra delay at sentence boundaries? Default: false.
5. ORP alignment - Use Spritz ORP highlighting formula? Default: true.

If any required fields (file path, WPM) are missing, ask for them before proceeding. Reasonable defaults apply for optional fields.

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Execution

Step 1: Parse the Document

python ./scripts/parse_document.py \
  --input <file_path> \
  --output /tmp/rsvp_words.json

Step 2: Generate Token Stream

python ./scripts/orp_engine.py \
  --input /tmp/rsvp_words.json \
  --wpm <wpm> \
  --output <output_path>

ORP accuracy: The ORP index follows the Spritz formula. Verified by tests/test_orp_engine.py.

Step 3: Confirm Output

Report to the user:

• Total word count
• Estimated reading time at the chosen WPM
• Output file path
• Preview of first 5 tokens

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Output Format

Each token in the stream follows the schema in references/token-stream-schema.md:

{"w": "Hello", "orp": 1, "delay_ms": 200, "is_sentence_end": false, "is_para_end": false}

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Confirmation Gate

Before generating for files > 50,000 words, display:

This document contains ~{word_count} words.
At {wpm} WPM this will take ~{minutes} minutes to read.
Generating token stream (~{token_count} tokens) to {output_path}.
Proceed? [yes/no]

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Next Actions

After successful generation, offer:

1. Open the reading session with the rsvp-comprehension-agent
2. Adjust WPM and regenerate
3. Parse a different document

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Limitations

• PDF fidelity: Scanned PDFs or image-heavy PDFs may produce degraded word extraction; recommend .txt or .md inputs when possible.
• WPM range: Very high WPM (>800) may reduce comprehension; the skill will warn but not block.
• Large documents: Files >50,000 words trigger a confirmation gate before processing.
• Language: ORP formula is optimized for English; other languages may have suboptimal ORP placement.