Paper To Skill

Paper-to-Skill Extractor

An interactive skill for extracting research paradigms and methodological techniques from cognitive science and neuroscience papers. The output is a well-structured skill conforming to this project's SKILL.md format.

Focus: Strict extraction of reproducible methods — experimental designs, data acquisition parameters, processing pipelines, analysis procedures, and stimulus specifications. This is NOT about summarizing a paper's novelty or theoretical contributions.

Trigger Conditions

Activate this skill when the user:

• Provides a paper (PDF path, file, or pasted text) and asks to extract research skills/methods
• Uses phrases like "extract skills from this paper", "turn this paper into a skill", "what methods can I reuse from this paper"

Research Planning Protocol

Before extracting skills from a paper, you MUST:

1. Clarify the extraction goal — What type of methodological knowledge is the user looking for?
2. Justify the source — Is this paper a suitable source (empirical, methods, review)? What type-specific extraction strategy applies?
3. Declare expected outputs — What kind of skill(s) do you expect to generate (paradigm design, analysis pipeline, modeling)?
4. Note limitations — Are there missing parameters, ambiguous descriptions, or domain gaps in this paper?
5. Present the extraction 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.

Interactive Workflow

Phase 1: Paper Ingestion

1. Read the paper provided by the user (PDF path, file content, or pasted sections).

PDF Reading Guidance — Claude Code's Read tool natively supports PDF files. Use the following strategy:

• Short PDFs (up to ~10 pages): Read the entire file in a single call with no pages parameter.
• Long PDFs (more than 10 pages): Read in chunks using the pages parameter (maximum 20 pages per request). Example sequence: pages: "1-10", then pages: "11-20", and so on.
• Recommended reading order:

1. Read pages 1-2 first (abstract + introduction) to identify the paper type and decide whether full extraction is warranted.

2. Then read the Methods section in detail (locate the relevant page range from the table of contents or section headers).

3. Read Results and Discussion selectively for reported parameter values not stated in Methods.

4. Identify the paper type — this determines the extraction strategy:

• Experimental paper — contains original experiments with participants
• Methods paper — introduces or validates an analysis technique/pipeline
• Computational modeling paper — builds or tests formal models of cognition
• Review/theoretical paper — synthesizes literature or proposes theoretical frameworks

3. Confirm the paper type with the user before proceeding.

See references/extraction-guide.md for detailed extraction strategies per paper type.

Phase 2: Content Scanning and Candidate Identification

Scan the paper and identify all extractable methodological content organized into these categories:

Present candidates to the user in the following format:

I identified the following extractable methods from this paper:

## Experimental Design
- [1] Paradigm: <name> — <brief description>
- [2] Trial structure: <summary of trial flow and timing>

## Data Acquisition
- [3] <Modality> recording setup: <key parameters>

## Data Processing
- [4] Preprocessing pipeline: <step summary>
- [5] Artifact rejection: <method and criteria>

## Analysis Methods
- [6] <Analysis name>: <brief description>
- [7] <Analysis name>: <brief description>

## Stimulus Materials
- [8] <Material type>: <construction approach>

Which items would you like me to extract into skills?
(Enter numbers, ranges like 1-4, or "all")

Phase 2.5: Suitability Gate

Before presenting candidates, apply this strict suitability filter to each one:

SUITABLE — include if the candidate:

NOT SUITABLE — filter out if the candidate:

Decision rule: "Does this candidate contain enough specific, actionable detail that a researcher could REPRODUCE a method, pipeline, or paradigm from it?" If YES → [SUITABLE]. If NO or UNCERTAIN → [FILTERED — reason].

Mark each candidate when presenting to the user. Filtered candidates are shown but de-prioritized — the user can override any filter decision.

Phase 3: User Selection and Confirmation

1. Receive the user's selection of which items to extract.
2. For each selected item, perform deep extraction (see extraction depth requirements below).
3. Present the extracted detail for user review before generating the final skill file.

Phase 4: Skill Generation

1. Generate the skill file(s) using the standard template (see references/skill-template.md).
2. Each generated skill must:

• Have valid YAML frontmatter with name, description, and papers fields
• Include all numerical parameters with their citations from the source paper
• Stay under 500 lines; use references/ subdirectory for overflow
• Pass the domain-knowledge litmus test: "Would a competent programmer who has never taken a cognitive science course get this wrong?"

3. Present the generated skill to the user and ask for confirmation before saving.

Phase 5: Self-Verification (Hallucination Check)

After generating the skill but before saving, perform a systematic verification of every numerical parameter and specific factual claim against the source paper.

Verification procedure — for each numerical value or specific claim in the generated skill:

1. Locate in source — Find the corresponding statement in the original paper. Use the source location recorded during extraction.
2. Verify value — Confirm exact numerical match, correct units, and complete context (e.g., "0.1-30 Hz bandpass" must not be truncated to "0.1 Hz").
3. Classify any issues found:

Reporting — Present the verification results to the user:

Self-Verification Results:
- Claims checked: N
- Verified: M
- Issues found: K
 - [HIGH] <claim> — <issue type>: <details>
 - [LOW] <claim> — <issue type>: <details>

Rules:

• High-severity issues (not_found, value_mismatch, unit_error) must be corrected before saving.
• Medium/low-severity issues are flagged but the skill can be saved with them annotated.
• Do NOT flag reasonable paraphrasing, organizational differences, or standard terminology substitutions.

Extraction Depth Requirements

For every extracted item, the following cross-cutting rules apply to ALL categories:

Cross-Cutting Extraction Rules

1. Preserve exact numbers — Never round. If the paper says "513 ms", write "513 ms", not "~500 ms".
2. Track source location — For every extracted numerical value, record where it appears in the paper: "Section X.Y, paragraph N", "Table N", "Figure N caption", or "Supplementary Materials, page N". This enables downstream verification.
3. Flag missing information — If a standard parameter for this method type is not reported in the paper, explicitly note its absence (e.g., "Filter order: not reported").
4. Capture rationale — When the authors explain WHY they chose a parameter value, include that justification alongside the value.
5. Note deviations from convention — When authors explicitly deviate from field conventions, capture both what they did and their stated reason.

These rules apply to every category below. The parameter tables in generated skills must include a Source Location column (see references/skill-template.md).

Experimental Design Parameters

• Paradigm name and classification (e.g., "oddball paradigm", "visual world paradigm")
• Number of conditions and their operational definitions
• Trial sequence: fixation → stimulus → ISI → response window (with exact ms values)
• Number of trials per condition and total
• Block structure and rest intervals
• Counterbalancing method (Latin square, full counterbalancing, pseudo-randomization constraints)
• Practice trial specifications
• Participant exclusion criteria applied at the design level

Data Acquisition Parameters

• EEG: Sampling rate (Hz), electrode count and montage system, reference electrode, ground electrode, impedance threshold, amplifier model, filter settings during recording
• fMRI: TR, TE, voxel size, slice count, slice order, field strength, coil type, number of volumes, dummy scans discarded
• Eye-tracking: Sampling rate, calibration procedure (5-point, 9-point), fixation definition criteria, saccade velocity threshold
• Behavioral: Response device, response mapping, timeout duration, feedback presence/absence
• MEG: Sampling rate, sensor type and count, head position indicator settings, noise reduction method

Data Processing Pipeline

• Software used (with version numbers)
• Step-by-step sequence with order preserved
• Filter parameters: type (FIR/IIR), cutoff frequencies, order/transition bandwidth, causal vs. zero-phase
• Re-referencing scheme
• Epoching: time window relative to event, baseline correction window
• Artifact rejection: method (threshold, ICA, regression), specific thresholds and criteria
• Trial/participant exclusion rates reported
• Interpolation method for bad channels

Analysis Method Details

• Statistical test name and implementation
• Model specification (for regression/mixed models: fixed effects, random effects, link function)
• Multiple comparison correction: method, parameters (e.g., cluster-forming threshold, number of permutations)
• Region of interest definitions (coordinates, anatomical labels, time windows)
• Effect size measure used and interpretation benchmarks
• Visualization methods with axis specifications

Stimulus Material Specifications

• Material type (words, images, sounds, videos)
• Total number of stimuli and per-condition counts
• Controlled variables and matching criteria (frequency, length, luminance, valence)
• Norming source and database (e.g., "SUBTLEX-US word frequency", "IAPS valence ratings")
• Presentation parameters: duration, size/visual angle, position, contrast
• Randomization constraints applied to stimulus ordering