Converts an input document (.txt, .md, .pdf, .docx) into a structured RSVP token stream with ORP alignment and configurable WPM. Use when a user wants to speed-read a document, prepare a reading session, or generate a token stream for a speed-reading UI.
From rsvp-speed-readernpx claudepluginhub richfrem/agent-plugins-skills --plugin rsvp-speed-readerThis skill is limited to using the following tools:
acceptance-criteria.mdevals/evals.jsonevals/results.tsvfallback-tree.mdreferences/acceptance-criteria.mdreferences/architecture.mdreferences/diagrams/rsvp-reading-flow.mmdreferences/fallback-tree.mdreferences/token-stream-schema.mdrequirements.txtscripts/execute.pyscripts/orp_engine.pyscripts/parse_document.pyGuides Next.js Cache Components and Partial Prerendering (PPR) with cacheComponents enabled. Implements 'use cache', cacheLife(), cacheTag(), revalidateTag(), static/dynamic optimization, and cache debugging.
Migrates code, prompts, and API calls from Claude Sonnet 4.0/4.5 or Opus 4.1 to Opus 4.5, updating model strings on Anthropic, AWS, GCP, Azure platforms.
Configures VPN and dedicated connections like Direct Connect, ExpressRoute, Interconnect for secure on-premises to AWS, Azure, GCP, OCI hybrid networking.
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).
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.mdAcceptance criteria:references/acceptance-criteria.mdFallback tree:references/fallback-tree.mdToken stream schema:references/token-stream-schema.md
Invoke this skill when the user:
Before executing, collect:
.txt, .md, .pdf, .docx)300. Range: 100-1000../rsvp_output.jsonIf any are missing, ask for them before proceeding.
python3 ./scripts/parse_document.py \
--input <file_path> \
--output /tmp/rsvp_words.json
python3 ./scripts/orp_engine.py \
--input /tmp/rsvp_words.json \
--wpm <wpm> \
--output <output_path>
Report to the user:
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}
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]
After successful generation, offer:
rsvp-comprehension-agent