Agent skill

Self Paced Reading Designer

by NeuroAIHub in NeuroAIHub/BrainPilot

Expert guidance for designing self-paced reading experiments: region segmentation, timing parameters, comprehension probes, and spillover analysis

AGPL-3.0Auto-check passed

Install Self Paced Reading Designer

skills CLI
$ npx skills add NeuroAIHub/BrainPilot --skill self-paced-reading-designer -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install NeuroAIHub/BrainPilot self-paced-reading-designer --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/NeuroAIHub/BrainPilot.git skills-src && mkdir -p .claude/skills && cp -r skills-src/packages/skills/skills/04_Psycholinguistics/self-paced-reading-designer .claude/skills/self-paced-reading-designer && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
self-paced-reading-designer
GitHub stars
1.1k
Token cost
~5.2k tokens
SKILL.md length
2,598 words
Files
3 (incl. references)
Skills in repo
59
Repo updated
First seen
Licence
AGPL-3.0

At a glance

Expert guidance for designing self-paced reading experiments: region segmentation, timing parameters, comprehension probes, and spillover analysis

  • Works in 6 steps: Select a Presentation Method → Configure Timing Parameters → Design Critical Regions → …
  • SKILL.md covers Why SPR Design Requires Domain…, Research Planning Protocol, ⚠️ Verification Notice and Core Workflow, plus 3 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Self Paced Reading Designer is an agent skill from NeuroAIHub/BrainPilot. Expert guidance for designing self-paced reading experiments: region segmentation, timing parameters, comprehension probes, and spillover analysis

Its SKILL.md is about 5.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files, including reference files (for example `references/analysis-guide.md` and `references/region-segmentation.md`).

The repository describes itself as: BrainPilot: Automating Brain Discovery with Agentic Research. The licence is AGPL-3.0.

Example prompts

  • “/self-paced-reading-designer”

Workflow steps

6 steps, taken from the step headings in SKILL.md.

  1. Select a Presentation Method
  2. Configure Timing Parameters
  3. Design Critical Regions
  4. Design Comprehension Questions
  5. Design Item and Condition Structure
  6. Decide Between SPR and Eye-Tracking

What it can do on your machine

Read from SKILL.md and the folder at commit 93f6855. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • github.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Self Paced Reading Designer loads about 5.2k tokens when it runs, and up to ~13k if it reads all its reference files. Until then it costs about 44 tokens; SKILL.md has 2,598 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~44
When it runs · the whole SKILL.md, loaded when a task matches
~5.2k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~13k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from NeuroAIHub/BrainPilot at commit 93f6855, republished under its AGPL-3.0 licence (© NeuroAIHub). 2,598 words, ~5,168 tokens.

Download SKILL.mdSave it as .claude/skills/self-paced-reading-designer/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
self-paced-reading-designer
description
Expert guidance for designing self-paced reading experiments: region segmentation, timing parameters, comprehension probes, and spillover analysis
domain
psycholinguistics
version
1.0.0
authors
AI-generated with domain expert review
papers
Just, Carpenter, & Woolley, 1982, Jegerski, 2014, Keating & Jegerski, 2015, Mitchell, 2004, Boyce, Futrell, & Levy, 2020
dependencies.required
research-literacy
review_status
ai-generated

Self-Paced Reading Designer

This skill encodes expert knowledge for designing self-paced reading (SPR) experiments in psycholinguistics. SPR is the most widely used behavioral method for studying real-time sentence comprehension during reading (Jegerski, 2014). A competent programmer without psycholinguistics training will reliably make errors in region segmentation, spillover design, and comprehension question construction -- all of which invalidate the resulting data.

For detailed region segmentation strategies, see references/region-segmentation.md. For statistical analysis guidance, see references/analysis-guide.md.


Why SPR Design Requires Domain Expertise

Self-paced reading appears deceptively simple: participants press a button to reveal successive words. But the scientific value of an SPR experiment depends entirely on decisions that require psycholinguistic training:

  1. Region boundaries determine what you can measure. A critical region that spans a clause boundary conflates syntactic processing with wrap-up effects (Just & Carpenter, 1980). A non-specialist would not know this.
  2. Spillover is not a bug -- it is the primary data pattern. In SPR, processing difficulty at word N often appears in reading times at words N+1 and N+2, not at word N itself (Mitchell, 2004; Rayner, 1998). Failing to include and analyze spillover regions means missing the effect entirely.
  3. Comprehension questions that target the critical manipulation create demand characteristics. Participants learn to attend strategically to the manipulation, distorting natural reading patterns (Jegerski, 2014).
  4. Word length and frequency confounds are invisible to non-specialists. If the critical word in condition A is longer or less frequent than in condition B, reading time differences reflect lexical properties, not the intended manipulation (Keating & Jegerski, 2015).

Research Planning Protocol

Before executing the domain-specific steps below, you MUST:

  1. State the research question — What specific sentence processing question is this SPR study addressing?
  2. Justify the method choice — Why SPR (not eye-tracking, ERP, acceptability judgment)? What alternatives were considered?
  3. Declare expected outcomes — What reading time pattern (at which region) would support vs. refute the hypothesis?
  4. Note assumptions and limitations — What does SPR assume? Where could it mislead (e.g., lack of regressive eye movements)?
  5. Present the plan to the user and WAIT for confirmation before proceeding.

For detailed methodology guidance, see the research-literacy skill.

⚠️ Verification Notice

This skill was generated by AI from academic literature. All parameters, thresholds, and citations require independent verification before use in research. If you find errors, please open an issue.

Core Workflow

Step 1: Select a Presentation Method

Choose based on your research question, population, and resources:

1A. Non-Cumulative Moving Window (Standard)
  • The sentence is displayed as dashes; each button press reveals the next word and re-masks the previous one (Just, Carpenter, & Woolley, 1982)
  • Advantages: Most widely used, large existing literature for comparison, preserves spatial layout information
  • Disadvantages: Prevents regressions (unlike natural reading), produces spillover effects that spread over 2-4 words (Mitchell, 2004)
  • Use when: You need comparability with the existing SPR literature; you are studying incremental sentence processing
1B. Cumulative Moving Window
  • Each button press reveals the next word, but previously revealed words remain visible
  • Advantages: More similar to natural reading (partial text context remains)
  • Disadvantages: Rarely used; harder to compare with the dominant non-cumulative literature; participants may re-read prior context, introducing noise (Jegerski, 2014)
  • Use when: Naturalness of reading is more important than comparability with prior work
1C. Phrase-by-Phrase Presentation
  • Sentences are segmented into multi-word regions; each button press reveals a phrase
  • Advantages: Faster for participants; appropriate when word-level resolution is not needed
  • Disadvantages: Region boundaries must be linguistically principled (see references/region-segmentation.md); reduces temporal resolution; risks confounding region length with reading time
  • Use when: Your manipulation spans a multi-word constituent and word-by-word resolution is unnecessary
1D. Centered (RSVP-style) Presentation
  • Words appear one at a time at a fixed screen position (typically center)
  • Advantages: Eliminates eye movement confounds; simpler programming
  • Disadvantages: Destroys spatial layout; removes positional information that readers normally use; rarely used in modern SPR (Jegerski, 2014)
  • Avoid unless: You have a specific theoretical reason to eliminate spatial layout
1E. Maze Task (Modern Alternative)
  • Two words appear simultaneously; the participant selects the word that continues the sentence (Forster, Guerrera, & Elliot, 2009; Witzel, Witzel, & Forster, 2012)
  • L-maze (Lexicality maze): Distractor is a pronounceable nonword
  • G-maze (Grammaticality maze): Distractor is a real word that is ungrammatical in context
  • A-maze (Auto-maze): Distractors generated automatically via NLP (Boyce, Futrell, & Levy, 2020)
  • Advantages: Dramatically reduced spillover compared to SPR; forced incremental processing; works well for web-based data collection (Boyce et al., 2020); better statistical power per item than SPR for syntactic effects (Witzel et al., 2012)
  • Disadvantages: Slower overall pace; dual-task demand (comprehension + selection); less natural than button-press SPR; requires distractor generation
  • Use when: You need precise localization of effects, want to reduce spillover, or plan web-based data collection
Step 2: Configure Timing Parameters
ParameterRecommended ValueRationale
Response timeoutNone (self-paced) or 3000-5000 ms per regionNo timeout is standard for in-lab SPR; timeout prevents excessively slow responses in web-based studies (Boyce et al., 2020)
Inter-stimulus interval (ISI)0 ms for non-cumulative moving windowStandard practice; the next word appears immediately when the previous is masked (Just et al., 1982)
ISI for phrase-by-phrase0 ms (typical)Any nonzero ISI introduces a blank that disrupts reading and may introduce strategic pausing
Pre-sentence fixation+ or * for 500-1000 msOrients attention to display location; standard in SPR (Jegerski, 2014)
Post-sentence delay0-500 ms before comprehension questionBrief delay prevents motor interference between last word button-press and question response
Practice trials6-10 items minimumFamiliarizes participants with button-press rhythm and comprehension questions; use different sentences than experimental items (Jegerski, 2014; Keating & Jegerski, 2015)
Step 3: Design Critical Regions

This is the most consequential design decision in an SPR experiment. See references/region-segmentation.md for full guidelines.

Core Principles
  1. Match critical regions across conditions for word length (in characters) and lexical frequency. If your manipulation requires different words, match them on length (+/- 1 character) and log frequency (use SUBTLEX-US; Brysbaert & New, 2009). Unmatched items introduce confounds that mimic or mask experimental effects.

  2. Include at least 2-3 spillover words after the critical region. Processing difficulty at the critical region reliably spills over to subsequent words in SPR (Just et al., 1982; Mitchell, 2004; Rayner, 1998). Without spillover regions, you will miss your effect. These spillover words must be identical across conditions.

  3. Avoid placing critical regions at clause or sentence boundaries. Reading times at clause-final and sentence-final positions are inflated by wrap-up processes -- integration of clause-level meaning, discourse updating, and possibly implicit prosodic boundary effects (Just & Carpenter, 1980; Warren, White, & Reichle, 2009). This inflation is independent of your manipulation and adds noise.

  4. Keep critical regions short (ideally a single word). Multi-word critical regions reduce temporal resolution and introduce length confounds. If you must use a multi-word region, it must have the same number of words and matched total character length across conditions.

  5. Ensure the pre-critical region is identical across conditions. Any difference before the critical word can create baseline differences in reading time that propagate into the critical region via spillover.

Step 4: Design Comprehension Questions

Comprehension questions serve two purposes: ensuring participants read for meaning, and providing an exclusion criterion for inattentive participants.

Guidelines
ParameterRecommendationRationale
Proportion of trials with questions1/3 to 1/2 of all trials (experimental + filler)Fewer than 1/3: participants may stop reading carefully; more than 1/2: task becomes tedious, and participants may shift to a question-anticipation strategy (Just et al., 1982; Jegerski, 2014)
Answer balance50% yes / 50% no for yes/no questionsPrevents response bias toward one answer
Question contentTarget semantic content of the sentence, NOT the critical manipulationQuestions about the manipulation teach participants what you are studying, inducing strategic reading (Jegerski, 2014)
Accuracy exclusion threshold>80% correct to retain participantStandard criterion; lower accuracy suggests the participant was not reading for comprehension (Jegerski, 2014; common practice across SPR studies)
Question timingImmediately after the sentence (or after the final button press)Delayed questions test memory, not comprehension
Example of Good vs. Bad Comprehension Questions

Suppose the experimental sentence manipulates relative clause attachment:

The maid of the actress who was on the balcony shouted to the crowd.

  • Good question: "Did someone shout to the crowd?" (targets overall meaning, not the critical attachment)
  • Bad question: "Who was on the balcony?" (directly probes the ambiguity under investigation, alerting participants to the manipulation)
Step 5: Design Item and Condition Structure
Latin Square Design

For within-subjects manipulations, use a Latin square design so that each participant sees each item in exactly one condition, and each condition is seen equally often across participants (Keating & Jegerski, 2015).

  • For a 2-condition design: 2 lists; each item appears in condition A for half the participants, condition B for the other half
  • For a 2x2 design: 4 lists (one per condition combination)
  • Assign participants to lists in rotation
Items Per Condition
PopulationMinimum Items per ConditionRationale
L1 speakers, robust effect (e.g., garden-path)24 items per conditionSufficient for medium-to-large effects in mixed models (Keating & Jegerski, 2015)
L1 speakers, subtle effect (e.g., pragmatic inference)32-40 items per conditionSmaller effects require more items for adequate power (Keating & Jegerski, 2015; Brysbaert & Stevens, 2018)
L2 speakers32-40 items per conditionHigher variability in L2 populations requires more observations (Marsden, Thompson, & Plonsky, 2018)
Show full SKILL.md (1,083 more words)Show less
Filler Items
ParameterRecommendationRationale
Filler-to-experimental ratio2:1 or 3:1 (fillers : experimental items)Prevents participants from identifying the experimental pattern; higher ratios reduce strategic processing (Keating & Jegerski, 2015)
Filler varietyInclude multiple sentence types, lengths, and structuresMonotonous fillers fail to mask the experimental manipulation
Filler complexityInclude some fillers of similar complexity to experimental itemsIf only experimental items are complex, participants learn to attend differently to them
Comprehension questions on fillersYes -- at least the same rate as on experimental itemsIf questions only follow experimental items, participants learn that complex sentences predict questions
Step 6: Decide Between SPR and Eye-Tracking

This is a design-level decision that should be made before programming the experiment.

CriterionSPREye-Tracking
Equipment costLow (any computer)High (dedicated eye-tracker, ~$20,000-$50,000)
Online data collectionYes (web-based SPR and Maze work well)No (requires in-lab calibration)
Temporal resolutionWord-by-word, with substantial spilloverMultiple fixation measures (first fixation, gaze duration, go-past, total time, regressions)
RegressionsNot measurable (non-cumulative display prevents rereading)Yes -- regressions are a primary measure of reanalysis
Ecological validityModerate (button-press is unnatural, but spatial layout preserved)Higher (closer to natural reading)
Sensitivity to early/late processing stagesLow (only a single RT per region, which blends all processing stages)High (first-pass vs. second-pass measures separate early from late processing; Rayner, 1998)
Best forRobust syntactic/semantic effects, web-based or underfunded studies, L2 populations without lab accessNuanced temporal dynamics, distinguishing processing stages, studying regressions, garden-path recovery

Rule of thumb: If you only need to know whether a manipulation affects reading time, SPR is sufficient. If you need to know when during processing the effect occurs (early lexical access vs. late reanalysis), use eye-tracking.


Common Pitfalls

These are errors that non-specialists routinely make:

  1. No spillover region. The most common fatal flaw. If the sentence ends at or immediately after the critical word, spillover effects have nowhere to appear, and the effect is lost. Always include 2-3 words of identical post-critical material across conditions.

  2. Critical region at a clause boundary. Wrap-up effects at clause-final positions (Just & Carpenter, 1980) inflate reading times by 50-100+ ms regardless of condition, swamping the experimental effect or producing spurious interactions.

  3. Length/frequency mismatch. Longer words take approximately 30-40 ms per additional character in SPR (Ferreira & Clifton, 1986). A 2-character difference between conditions creates a ~60-80 ms confound, which can easily exceed the size of most psycholinguistic effects.

  4. Comprehension questions targeting the manipulation. This transforms the experiment from measuring natural reading into measuring strategic disambiguation. Participants adapt within 10-15 trials (Jegerski, 2014).

  5. Too few items per condition. With fewer than 24 items per condition, even large effects (d = 0.8) may not reach significance in mixed-effects models, particularly with by-item random slopes (Brysbaert & Stevens, 2018).

  6. No fillers or insufficient fillers. Without a 2:1 filler-to-item ratio, participants identify the experimental manipulation and shift to strategic reading (Keating & Jegerski, 2015).

  7. Analyzing only the critical region. Even when an effect appears on the critical word, it typically continues into the spillover region. Analyzing only one region provides an incomplete picture and may miss effects that appear exclusively in spillover.

  8. Using raw reading times without controlling for word length. Raw RTs conflate lexical processing speed with the experimental manipulation. Either match word length precisely or use residual RTs / include word length as a covariate in the statistical model (Ferreira & Clifton, 1986).

  9. Ignoring trial position effects. Reading speed increases across the experiment as participants become practiced. Include trial order as a covariate or present items in a randomized order (Jegerski, 2014).

  10. Not checking comprehension accuracy before analyzing RTs. Participants with low accuracy (<80%) may not be reading for comprehension. Their RT data are uninterpretable and should be excluded (Jegerski, 2014).


Quick Reference: SPR Design Checklist

Before running your experiment, verify:

  • Critical regions are matched for word length and frequency across conditions
  • At least 2-3 identical spillover words follow the critical region in all conditions
  • Critical region is not at a clause or sentence boundary
  • Pre-critical region is identical across conditions
  • At least 24 items per condition (32+ for subtle effects or L2 populations)
  • Filler-to-experimental ratio is at least 2:1
  • Comprehension questions on 1/3 to 1/2 of trials, balanced yes/no
  • No comprehension question directly targets the experimental manipulation
  • Latin square counterbalancing across lists
  • 6-10 practice trials with different sentences than experimental items
  • Analysis plan includes spillover regions (at least critical +1, +2)
  • RT trimming criteria defined a priori (see references/analysis-guide.md)

References

  • Baayen, R. H., Davidson, D. J., & Bates, D. M. (2008). Mixed-effects modeling with crossed random effects for subjects and items. Journal of Memory and Language, 59, 390-412.
  • Boyce, V., Futrell, R., & Levy, R. P. (2020). Maze Made Easy: Better and easier measurement of incremental processing difficulty. Journal of Memory and Language, 111, 104082.
  • Brysbaert, M., & New, B. (2009). Moving beyond Kucera and Francis: A critical evaluation of current word frequency norms and the introduction of a new and improved word frequency measure for American English. Behavior Research Methods, 41, 977-990.
  • Brysbaert, M., & Stevens, M. (2018). Power analysis and effect size in mixed effects models. Journal of Cognition, 1(1), 9.
  • Ferreira, F., & Clifton, C. (1986). The independence of syntactic processing. Journal of Memory and Language, 25, 348-368.
  • Forster, K. I., Guerrera, C., & Elliot, L. (2009). The maze task: Measuring forced incremental sentence processing time. Behavior Research Methods, 41, 163-171.
  • Jegerski, J. (2014). Self-paced reading. In J. Jegerski & B. VanPatten (Eds.), Research methods in second language psycholinguistics. Routledge.
  • Just, M. A., & Carpenter, P. A. (1980). A theory of reading: From eye fixations to comprehension. Psychological Review, 87, 329-354.
  • Just, M. A., Carpenter, P. A., & Woolley, J. D. (1982). Paradigms and processes in reading comprehension. Journal of Experimental Psychology: General, 111, 228-238.
  • Keating, G. D., & Jegerski, J. (2015). Experimental designs in sentence processing research. Studies in Second Language Acquisition, 37, 1-32.
  • Marsden, E., Thompson, S., & Plonsky, L. (2018). A methodological synthesis of self-paced reading in second language research. Applied Psycholinguistics, 39, 861-904.
  • Mitchell, D. C. (2004). On-line methods in language processing: Introduction and historical review. In M. Carreiras & C. Clifton (Eds.), The on-line study of sentence comprehension. Psychology Press.
  • Rayner, K. (1998). Eye movements in reading and information processing: 20 years of research. Psychological Bulletin, 124, 372-422.
  • Warren, T., White, S. J., & Reichle, E. D. (2009). Investigating the causes of wrap-up effects: Evidence from eye movements and E-Z Reader. Cognition, 111, 132-137.
  • Witzel, N., Witzel, J., & Forster, K. (2012). Comparisons of online reading paradigms: Eye tracking, moving-window, and maze. Journal of Psycholinguistic Research, 41, 105-128.

© NeuroAIHub, AGPL-3.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 2 other files (references) in packages/skills/skills/04_Psycholinguistics/self-paced-reading-designer of NeuroAIHub/BrainPilot.

  • SKILL.md
  • references/analysis-guide.md
  • references/region-segmentation.md

Open the folder on GitHubat commit 93f6855

Compare with similar skills

Self Paced Reading Designer next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Self Paced Reading Designer compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Self Paced Reading Designer this skillNeuroAIHub/BrainPilot1.1k—~5.2kAutomated safety check: PassAGPL-3.0
Frontend Slideszarazhangrui/frontend-slides30k16 repos~7kAutomated safety check: PassMIT
Algorithmic Art with p5.jsanthropics/skills180k38 repos~4.9kAutomated safety check: PassApache-2.0
Canvas Designanthropics/skills180k52 repos~3kAutomated safety check: PassApache-2.0
Impeccablebestofjs/bestofjs3.1k26 repos~2.6kAutomated safety check: PassMIT
Brand and Design Toolkitnextlevelbuilder/ui-ux-pro-max-skill135k1 repos~3.5kAutomated safety check: PassMIT

Similar skills

  • Frontend Slides

    zarazhangrui/frontend-slides

    Builds animated HTML slide decks that run in the browser with no dependencies, or converts PowerPoint files to the web, starting from visual style previews.

    30k GitHub starsUsed in 16 repos~7k tokens
    Documents & OfficeAuto-check passed
  • Official

    Creates original generative art in two steps: a written algorithmic philosophy, then a p5.js sketch with seeded randomness and an interactive viewer for exploring parameters.

    180k GitHub starsUsed in 38 repos~4.9k tokens
    Media & CreativeAuto-check passed
  • Canvas Design

    anthropics/skills

    Official

    Creates original posters and static art as PNG or PDF by first writing a short design philosophy, then expressing it visually on a canvas.

    180k GitHub starsUsed in 52 repos~3k tokens
    Media & CreativeAuto-check passed
  • Impeccable

    bestofjs/bestofjs

    A skill your agent uses when the user wants to design, redesign, shape, critique, audit, polish, clarify, distill, harden, optimize, adapt, animate, colorize, extract, or otherwise improve a…

    3.1k GitHub starsUsed in 26 repos~2.6k tokens
    Frontend & DesignAuto-check passed
  • Brand and Design Toolkit

    nextlevelbuilder/ui-ux-pro-max-skill

    Bundles design tasks behind one skill: brand identity, tokens, UI styling, logos, corporate identity mockups, slides, banners, icons and social images.

    135k GitHub starsUsed in 1 repo~3.5k tokens
    Media & CreativeAuto-check passed
  • HyperFrames Animation

    heygen-com/hyperframes

    Collects motion rules, scene blueprints, transitions and runtime adapters for HyperFrames video compositions, with GSAP as the default animation runtime.

    60k GitHub starsUsed in 3 repos~2.1k tokens
    Media & CreativeAuto-check passed

More from NeuroAIHub/BrainPilot

All 59 skills in this repo
  • Deeplabcut

    NeuroAIHub/BrainPilot

    Toolbox for markerless animal pose estimation with DeepLabCut.

    1.1k GitHub stars~1.7k tokensUpdated 9 days ago
    Auto-check passed
  • Fmriprep

    NeuroAIHub/BrainPilot

    Preprocess task-based or resting-state fMRI data with fMRIPrep — a robust, BIDS-App preprocessing pipeline built on FSL, ANTs, FreeSurfer, AFNI, and Nilearn.

    1.1k GitHub stars~4.1k tokensUpdated 9 days ago
    Auto-check passed
  • Mne Python Guide

    NeuroAIHub/BrainPilot

    Domain-validated pipeline guidance for EEG/MEG data analysis using MNE-Python: data loading, preprocessing (filtering, ICA, re-referencing), epoching, ERP/ERF computation, time-frequency…

    1.1k GitHub stars~2.3k tokensUpdated 9 days ago
    Auto-check passed
  • Netneurotools Guide

    NeuroAIHub/BrainPilot

    Domain-validated guidance for network neuroscience analysis using netneurotools: datasets, brain network metrics, connectivity consensus, modularity, spatial statistics, null models, and cortical…

    1.1k GitHub stars~2.6k tokensUpdated 9 days ago
    Auto-check passed
  • Nature Figure

    NeuroAIHub/BrainPilot

    Submission-grade Nature/high-impact journal figure workflow for Python or R.

    1.1k GitHub starsUsed in 1 repo~1.3k tokens
    Auto-check passed
  • Pycortex Guide

    NeuroAIHub/BrainPilot

    Domain-validated guidance for cortical surface visualization and brain surface rendering of fMRI data using pycortex: data types (Volume, Vertex, Dataset), 2D cortical flatmaps, 3D WebGL brain…

    1.1k GitHub stars~1.6k tokensUpdated 9 days ago
    Auto-check passed

Questions about Self Paced Reading Designer

What does Self Paced Reading Designer do?

Expert guidance for designing self-paced reading experiments: region segmentation, timing parameters, comprehension probes, and spillover analysis. Self Paced Reading Designer is an agent skill from NeuroAIHub/BrainPilot.

How do I install Self Paced Reading Designer in Claude Code?

Run `npx skills add NeuroAIHub/BrainPilot --skill self-paced-reading-designer -a claude-code`. Or copy the skill folder (packages/skills/skills/04_Psycholinguistics/self-paced-reading-designer in NeuroAIHub/BrainPilot) into .claude/skills/self-paced-reading-designer in your project. Claude Code loads it when a task matches its description.

How do I install Self Paced Reading Designer in Codex?

Run `npx skills add NeuroAIHub/BrainPilot --skill self-paced-reading-designer -a codex`. Or copy the skill folder (packages/skills/skills/04_Psycholinguistics/self-paced-reading-designer in NeuroAIHub/BrainPilot) into .agents/skills/self-paced-reading-designer in your project. Codex loads it when a task matches its description.

Can I use Self Paced Reading Designer in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add NeuroAIHub/BrainPilot --skill self-paced-reading-designer -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/self-paced-reading-designer, .gemini/skills/self-paced-reading-designer, .github/skills/self-paced-reading-designer and .opencode/skills/self-paced-reading-designer in your project.

What does Self Paced Reading Designer need to run?

SKILL.md names no scripts, command-line tools or credentials: Self Paced Reading Designer is instructions for the agent only.

Does Self Paced Reading Designer access the network?

SKILL.md names 1 domain. As links in the text: github.com. This is read from the text; nothing was executed.

Is Self Paced Reading Designer safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Self Paced Reading Designer use?

Self Paced Reading Designer is published under the AGPL-3.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Self Paced Reading Designer use?

About 5.2k tokens (SKILL.md is roughly 21k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 8.2k tokens, read only when the agent opens those files.

What are the alternatives to Self Paced Reading Designer?

Skills that share tags, products or a category with Self Paced Reading Designer: Frontend Slides (zarazhangrui/frontend-slides, 30k stars), Algorithmic Art with p5.js (anthropics/skills, 180k stars), Canvas Design (anthropics/skills, 180k stars) and Impeccable (bestofjs/bestofjs, 3.1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Self Paced Reading Designer?

NeuroAIHub (a GitHub organization) maintains it in NeuroAIHub/BrainPilot, which has 1,062 GitHub stars. The repository holds 59 skills in this directory. The repository was last updated on October 2, 2026.

Source: NeuroAIHub/BrainPilot on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.