Agent skill

Gnomad Database

by LeonChaoX in LeonChaoX/qinyan-academic-skills

Query gnomAD (Genome Aggregation Database) for population allele frequencies, variant constraint scores (pLI, LOEUF), and loss-of-function intolerance.

CC0-1.0Auto-check passedResearch & Science

Install Gnomad Database

skills CLI
$ npx skills add LeonChaoX/qinyan-academic-skills --skill gnomad-database -a claude-code

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

GitHub CLI
$ gh skill install LeonChaoX/qinyan-academic-skills gnomad-database --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/LeonChaoX/qinyan-academic-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/'skills/12-科学数据库/gnomad-database' .claude/skills/gnomad-database && 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
gnomad-database
GitHub stars
943
Used in
1 other repo
Token cost
~3.1k tokens
SKILL.md length
639 words
Files
3 (incl. references)
Skills in repo
31
Repo updated
First seen
Licence
CC0-1.0

At a glance

Query gnomAD (Genome Aggregation Database) for population allele frequencies, variant constraint scores (pLI, LOEUF), and loss-of-function intolerance.

  • Works in 6 steps: gnomAD GraphQL API → Querying Variants by Gene → Querying a Specific Variant → …
  • Tasks that involve Bioinformatics
  • SKILL.md covers Overview, When to Use This Skill, Core Capabilities and Query Workflows, plus 3 more sections
  • Reaches gnomad.broadinstitute.org

What it does

Gnomad Database is an agent skill from LeonChaoX/qinyan-academic-skills. Query gnomAD (Genome Aggregation Database) for population allele frequencies, variant constraint scores (pLI, LOEUF), and loss-of-function intolerance. Essential for variant pathogenicity interpretation, rare disease genetics, and identifying loss-of-function intolerant genes.

Its SKILL.md is about 3.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files, including reference files (for example `references/graphql_queries.md` and `references/variant_interpretation.md`).

It sits in Research & Science, covering Bioinformatics. It works with GraphQL. The repository describes itself as: A curated, multilingual library of 182 installable AI agent skills for end-to-end academic research—spanning literature discovery, scientific writing, grant development… The licence is CC0-1.0.

When your agent uses it

  • Tasks that involve Bioinformatics

Example prompts

  • “/gnomad-database”

Requirements

  • Python 3

Workflow steps

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

  1. gnomAD GraphQL API
  2. Querying Variants by Gene
  3. Querying a Specific Variant
  4. Gene Constraint Scores
  5. Population Frequency Analysis
  6. Structural Variants (gnomAD-SV)

What it can do on your machine

Read from SKILL.md and the folder at commit df5a498. 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 (its code samples are python).

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

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • gnomad.broadinstitute.org

    Also links to:

    • 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

Gnomad Database loads about 3.1k tokens when it runs, and up to ~5.3k if it reads all its reference files. Until then it costs about 73 tokens; SKILL.md has 639 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~73
When it runs · the whole SKILL.md, loaded when a task matches
~3.1k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~5.3k

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 LeonChaoX/qinyan-academic-skills at commit df5a498, republished under its CC0-1.0 licence (© LeonChaoX). 639 words, ~3,114 tokens.

Download SKILL.mdSave it as .claude/skills/gnomad-database/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
gnomad-database
description
Query gnomAD (Genome Aggregation Database) for population allele frequencies, variant constraint scores (pLI, LOEUF), and loss-of-function intolerance. Essential for variant pathogenicity interpretation, rare disease genetics, and identifying loss-of-function intolerant genes.
license
CC0-1.0
metadata.skill-author
Kuan-lin Huang

gnomAD Database

Overview

The Genome Aggregation Database (gnomAD) is the largest publicly available collection of human genetic variation, aggregated from large-scale sequencing projects. gnomAD v4 contains exome sequences from 730,947 individuals and genome sequences from 76,215 individuals across diverse ancestries. It provides population allele frequencies, variant consequence annotations, and gene-level constraint metrics that are essential for interpreting the clinical significance of genetic variants.

Key resources:

When to Use This Skill

Use gnomAD when:

  • Variant frequency lookup: Checking if a variant is rare, common, or absent in the general population
  • Pathogenicity assessment: Rare variants (MAF < 1%) are candidates for disease causation; gnomAD helps filter benign common variants
  • Loss-of-function intolerance: Using pLI and LOEUF scores to assess whether a gene tolerates protein-truncating variants
  • Population-stratified frequencies: Comparing allele frequencies across ancestries (African/African American, Admixed American, Ashkenazi Jewish, East Asian, Finnish, Middle Eastern, Non-Finnish European, South Asian)
  • ClinVar/ACMG variant classification: gnomAD frequency data feeds into BA1/BS1 evidence codes for variant classification
  • Constraint analysis: Identifying genes depleted of missense or loss-of-function variation (z-scores, pLI, LOEUF)

Core Capabilities

1. gnomAD GraphQL API

gnomAD uses a GraphQL API accessible at https://gnomad.broadinstitute.org/api. Most queries fetch variants by gene or specific genomic position.

Datasets available:

  • gnomad_r4 — gnomAD v4 exomes (recommended default, GRCh38)
  • gnomad_r4_genomes — gnomAD v4 genomes (GRCh38)
  • gnomad_r3 — gnomAD v3 genomes (GRCh38)
  • gnomad_r2_1 — gnomAD v2 exomes (GRCh37)

Reference genomes:

  • GRCh38 — default for v3/v4
  • GRCh37 — for v2
2. Querying Variants by Gene
python
import requests

def query_gnomad_gene(gene_symbol, dataset="gnomad_r4", reference_genome="GRCh38"):
    """Fetch variants in a gene from gnomAD."""
    url = "https://gnomad.broadinstitute.org/api"

    query = """
    query GeneVariants($gene_symbol: String!, $dataset: DatasetId!, $reference_genome: ReferenceGenomeId!) {
      gene(gene_symbol: $gene_symbol, reference_genome: $reference_genome) {
        gene_id
        gene_symbol
        variants(dataset: $dataset) {
          variant_id
          pos
          ref
          alt
          consequence
          genome {
            af
            ac
            an
            ac_hom
            populations {
              id
              ac
              an
              af
            }
          }
          exome {
            af
            ac
            an
            ac_hom
          }
          lof
          lof_flags
          lof_filter
        }
      }
    }
    """

    variables = {
        "gene_symbol": gene_symbol,
        "dataset": dataset,
        "reference_genome": reference_genome
    }

    response = requests.post(url, json={"query": query, "variables": variables})
    return response.json()

# Example
result = query_gnomad_gene("BRCA1")
gene_data = result["data"]["gene"]
variants = gene_data["variants"]

# Filter to rare PTVs
rare_ptvs = [
    v for v in variants
    if v.get("lof") == "LC" or v.get("consequence") in ["stop_gained", "frameshift_variant"]
    and v.get("genome", {}).get("af", 1) < 0.001
]
print(f"Found {len(rare_ptvs)} rare PTVs in {gene_data['gene_symbol']}")
3. Querying a Specific Variant
python
import requests

def query_gnomad_variant(variant_id, dataset="gnomad_r4"):
    """Fetch details for a specific variant (e.g., '1-55516888-G-GA')."""
    url = "https://gnomad.broadinstitute.org/api"

    query = """
    query VariantDetails($variantId: String!, $dataset: DatasetId!) {
      variant(variantId: $variantId, dataset: $dataset) {
        variant_id
        chrom
        pos
        ref
        alt
        genome {
          af
          ac
          an
          ac_hom
          populations {
            id
            ac
            an
            af
          }
        }
        exome {
          af
          ac
          an
          ac_hom
          populations {
            id
            ac
            an
            af
          }
        }
        consequence
        lof
        rsids
        in_silico_predictors {
          id
          value
          flags
        }
        clinvar_variation_id
      }
    }
    """

    response = requests.post(
        url,
        json={"query": query, "variables": {"variantId": variant_id, "dataset": dataset}}
    )
    return response.json()

# Example: query a specific variant
result = query_gnomad_variant("17-43094692-G-A")  # BRCA1 missense
variant = result["data"]["variant"]

if variant:
    genome_af = variant.get("genome", {}).get("af", "N/A")
    exome_af = variant.get("exome", {}).get("af", "N/A")
    print(f"Variant: {variant['variant_id']}")
    print(f"  Consequence: {variant['consequence']}")
    print(f"  Genome AF: {genome_af}")
    print(f"  Exome AF: {exome_af}")
    print(f"  LoF: {variant.get('lof')}")
4. Gene Constraint Scores

gnomAD constraint scores assess how tolerant a gene is to variation relative to expectation:

python
import requests

def query_gnomad_constraint(gene_symbol, reference_genome="GRCh38"):
    """Fetch constraint scores for a gene."""
    url = "https://gnomad.broadinstitute.org/api"

    query = """
    query GeneConstraint($gene_symbol: String!, $reference_genome: ReferenceGenomeId!) {
      gene(gene_symbol: $gene_symbol, reference_genome: $reference_genome) {
        gene_id
        gene_symbol
        gnomad_constraint {
          exp_lof
          exp_mis
          exp_syn
          obs_lof
          obs_mis
          obs_syn
          oe_lof
          oe_mis
          oe_syn
          oe_lof_lower
          oe_lof_upper
          lof_z
          mis_z
          syn_z
          pLI
        }
      }
    }
    """

    response = requests.post(
        url,
        json={"query": query, "variables": {"gene_symbol": gene_symbol, "reference_genome": reference_genome}}
    )
    return response.json()

# Example
result = query_gnomad_constraint("KCNQ2")
gene = result["data"]["gene"]
constraint = gene["gnomad_constraint"]

print(f"Gene: {gene['gene_symbol']}")
print(f"  pLI:   {constraint['pLI']:.3f}  (>0.9 = LoF intolerant)")
print(f"  LOEUF: {constraint['oe_lof_upper']:.3f}  (<0.35 = highly constrained)")
print(f"  Obs/Exp LoF: {constraint['oe_lof']:.3f}")
print(f"  Missense Z:  {constraint['mis_z']:.3f}")

Constraint score interpretation:

ScoreRangeMeaning
pLI0–1Probability of LoF intolerance; >0.9 = highly intolerant
LOEUF0–∞LoF observed/expected upper bound; <0.35 = constrained
oe_lof0–∞Observed/expected ratio for LoF variants
mis_z−∞ to ∞Missense constraint z-score; >3.09 = constrained
syn_z−∞ to ∞Synonymous z-score (control; should be near 0)
5. Population Frequency Analysis
python
import requests
import pandas as pd

def get_population_frequencies(variant_id, dataset="gnomad_r4"):
    """Extract per-population allele frequencies for a variant."""
    url = "https://gnomad.broadinstitute.org/api"

    query = """
    query PopFreqs($variantId: String!, $dataset: DatasetId!) {
      variant(variantId: $variantId, dataset: $dataset) {
        variant_id
        genome {
          populations {
            id
            ac
            an
            af
            ac_hom
          }
        }
      }
    }
    """

    response = requests.post(
        url,
        json={"query": query, "variables": {"variantId": variant_id, "dataset": dataset}}
    )
    data = response.json()
    populations = data["data"]["variant"]["genome"]["populations"]

    df = pd.DataFrame(populations)
    df = df[df["an"] > 0].copy()
    df["af"] = df["ac"] / df["an"]
    df = df.sort_values("af", ascending=False)
    return df

# Population IDs in gnomAD v4:
# afr = African/African American
# ami = Amish
# amr = Admixed American
# asj = Ashkenazi Jewish
# eas = East Asian
# fin = Finnish
# mid = Middle Eastern
# nfe = Non-Finnish European
# sas = South Asian
# remaining = Other
6. Structural Variants (gnomAD-SV)

gnomAD also contains a structural variant dataset:

python
import requests

def query_gnomad_sv(gene_symbol):
    """Query structural variants overlapping a gene."""
    url = "https://gnomad.broadinstitute.org/api"

    query = """
    query SVsByGene($gene_symbol: String!) {
      gene(gene_symbol: $gene_symbol, reference_genome: GRCh38) {
        structural_variants {
          variant_id
          type
          chrom
          pos
          end
          af
          ac
          an
        }
      }
    }
    """

    response = requests.post(url, json={"query": query, "variables": {"gene_symbol": gene_symbol}})
    return response.json()

Query Workflows

Workflow 1: Variant Pathogenicity Assessment
  1. Check population frequency — Is the variant rare enough to be pathogenic?

    • Use gnomAD AF < 1% for recessive, < 0.1% for dominant conditions
    • Check ancestry-specific frequencies (a variant rare overall may be common in one population)
  2. Assess functional impact — LoF variants have highest prior probability

    • Check lof field: HC = high-confidence LoF, LC = low-confidence
    • Check lof_flags for issues like "NAGNAG_SITE", "PHYLOCSF_WEAK"
  3. Apply ACMG criteria:

    • BA1: AF > 5% → Benign Stand-Alone
    • BS1: AF > disease prevalence threshold → Benign Supporting
    • PM2: Absent or very rare in gnomAD → Pathogenic Moderate
Show full SKILL.md (220 more words)Show less
Workflow 2: Gene Prioritization in Rare Disease
  1. Query constraint scores for candidate genes
  2. Filter for pLI > 0.9 (haploinsufficient) or LOEUF < 0.35
  3. Cross-reference with observed LoF variants in the gene
  4. Integrate with ClinVar and disease databases
Workflow 3: Population Genetics Research
  1. Identify variant of interest from GWAS or clinical data
  2. Query per-population frequencies
  3. Compare frequency differences across ancestries
  4. Test for enrichment in specific founder populations

Best Practices

  • Use gnomAD v4 (gnomad_r4) for the most current data; use v2 (gnomad_r2_1) only for GRCh37 compatibility
  • Handle null responses: Variants not observed in gnomAD are not necessarily pathogenic — absence is informative
  • Distinguish exome vs. genome data: Genome data has more uniform coverage; exome data is larger but may have coverage gaps
  • Rate limit GraphQL queries: Add delays between requests; batch queries when possible
  • Homozygous counts (ac_hom) are relevant for recessive disease analysis
  • LOEUF is preferred over pLI for gene constraint (less sensitive to sample size)

Data Access

Additional Resources

© LeonChaoX, CC0-1.0. 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 skills/12-科学数据库/gnomad-database of LeonChaoX/qinyan-academic-skills.

  • SKILL.md
  • references/graphql_queries.md
  • references/variant_interpretation.md

Open the folder on GitHubat commit df5a498

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in LeonChaoX/qinyan-academic-skills, which our catalogue first saw on October 9, 2026.

Compare with similar skills

Gnomad Database 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.

Gnomad Database compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Gnomad Database this skillLeonChaoX/qinyan-academic-skills9431 repos~3.1kAutomated safety check: PassCC0-1.0
Gnomad Databasejaechang-hits/SciAgent-Skills3711 repos~7.2kAutomated safety check: PassCustom licence
Alphagenome Single Variant Analysisgoogle-deepmind/science-skills3.2k2 repos~3kAutomated safety check: NotesApache-2.0
13C Metabolic Flux AnalysisK-Dense-AI/scientific-agent-skills48k1 repos~3.2kAutomated safety check: PassMIT
Clinvar Databasegoogle-deepmind/science-skills3.2k2 repos~3.9kAutomated safety check: NotesApache-2.0
Metabolic Study Planneraiming-lab/AutoResearchClaw15k—~1.9kAutomated safety check: PassMIT

Similar skills

  • Gnomad Database

    jaechang-hits/SciAgent-Skills

    gnomAD v4 population variant frequencies via GraphQL API. An agent skill from jaechang-hits/SciAgent-Skills.

    371 GitHub starsUsed in 1 repo~7.2k tokens
    Research & ScienceAuto-check passed
  • Alphagenome Single Variant Analysis

    google-deepmind/science-skills

    Analyzes genetic variant effects on gene expression (RNA-seq), chromatin accessibility (DNASE), histone marks (ChIP), and transcription factors using the AlphaGenome API.

    3.2k GitHub starsUsed in 2 repos~3k tokens
    Research & ScienceAuto-check: notes
  • 13C Metabolic Flux Analysis

    K-Dense-AI/scientific-agent-skills

    Estimates reaction fluxes inside cells from steady-state carbon-13 labeling data with a bundled mfapy-based solver, and reports which fluxes the data pin down.

    48k GitHub starsUsed in 1 repo~3.2k tokens
    Research & ScienceAuto-check passed
  • Clinvar Database

    google-deepmind/science-skills

    A skill your agent uses when needing clinical significance, pathogenicity classifications (e.g., Pathogenic, Benign, VUS), clinical evidence rationales, or finding "hard positive" benchmark controls…

    3.2k GitHub starsUsed in 2 repos~3.9k tokens
    Research & ScienceAuto-check: notes
  • Metabolic Study Planner

    aiming-lab/AutoResearchClaw

    Turns a broad metabolic modelling topic into a concrete, paper-shaped plan with organism, model, perturbations, metrics and figures before any FBA code is written.

    15k GitHub stars~1.9k tokensUpdated 1 mo ago
    Research & ScienceAuto-check passed
  • Dbsnp Database

    google-deepmind/science-skills

    A skill your agent uses when you want to look up, map, and search for short genetic variants (SNPs, indels) in NCBI's dbSNP database.

    3.2k GitHub starsUsed in 2 repos~3.4k tokens
    Research & ScienceAuto-check: notes

More from LeonChaoX/qinyan-academic-skills

All 31 skills in this repo
  • Paper Slide Deck

    LeonChaoX/qinyan-academic-skills

    Generate professional slide deck images from academic papers and content.

    943 GitHub starsUsed in 2 repos~4.6k tokens
    Auto-check passed
  • Parallel Web

    LeonChaoX/qinyan-academic-skills

    Search the web, extract URL content, and run deep research using the Parallel Chat API and Extract API.

    943 GitHub starsUsed in 1 repo~2.9k tokens
    Auto-check: notes
  • Research Proposal

    LeonChaoX/qinyan-academic-skills

    Generate academic research proposals for PhD applications. An agent skill from LeonChaoX/qinyan-academic-skills.

    943 GitHub starsUsed in 2 repos~4.8k tokens
    Auto-check passed
  • Medical Imaging Review

    LeonChaoX/qinyan-academic-skills

    Write comprehensive literature reviews for medical imaging AI research.

    943 GitHub starsUsed in 3 repos~1.1k tokens
    Auto-check: notes
  • Depmap

    LeonChaoX/qinyan-academic-skills

    Query the Cancer Dependency Map (DepMap) for cancer cell line gene dependency scores (CRISPR Chronos), drug sensitivity data, and gene effect profiles.

    943 GitHub starsUsed in 2 repos~2.8k tokens
    Auto-check passed
  • Phylogenetics

    LeonChaoX/qinyan-academic-skills

    Build and analyze phylogenetic trees using MAFFT (multiple alignment), IQ-TREE 2 (maximum likelihood), and FastTree (fast NJ/ML).

    943 GitHub starsUsed in 2 repos~3.5k tokens
    Auto-check passed

Works with

Questions about Gnomad Database

What does Gnomad Database do?

Query gnomAD (Genome Aggregation Database) for population allele frequencies, variant constraint scores (pLI, LOEUF), and loss-of-function intolerance. Gnomad Database is an agent skill from LeonChaoX/qinyan-academic-skills. Query gnomAD (Genome Aggregation Database) for population allele frequencies, variant constraint scores (pLI, LOEUF), and loss-of-function intolerance.

When should I use Gnomad Database?

Gnomad Database fits situations like: tasks that involve Bioinformatics.

How do I install Gnomad Database in Claude Code?

Run `npx skills add LeonChaoX/qinyan-academic-skills --skill gnomad-database -a claude-code`. Or copy the skill folder (skills/12-科学数据库/gnomad-database in LeonChaoX/qinyan-academic-skills) into .claude/skills/gnomad-database in your project. Claude Code loads it when a task matches its description.

How do I install Gnomad Database in Codex?

Run `npx skills add LeonChaoX/qinyan-academic-skills --skill gnomad-database -a codex`. Or copy the skill folder (skills/12-科学数据库/gnomad-database in LeonChaoX/qinyan-academic-skills) into .agents/skills/gnomad-database in your project. Codex loads it when a task matches its description.

Can I use Gnomad Database 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 LeonChaoX/qinyan-academic-skills --skill gnomad-database -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/gnomad-database, .gemini/skills/gnomad-database, .github/skills/gnomad-database and .opencode/skills/gnomad-database in your project.

What does Gnomad Database need to run?

SKILL.md names no scripts, command-line tools or credentials: Gnomad Database is instructions for the agent only. Our summary lists: Python 3.

Does Gnomad Database access the network?

SKILL.md names 2 domains. In commands or code: gnomad.broadinstitute.org; the agent is likely to contact it when it follows the instructions. As links in the text: github.com. This is read from the text; nothing was executed.

Is Gnomad Database 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 Gnomad Database use?

Gnomad Database is published under the CC0-1.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Gnomad Database use?

About 3.1k tokens (SKILL.md is roughly 12k 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 2.2k tokens, read only when the agent opens those files.

What are the alternatives to Gnomad Database?

Skills that share tags, products or a category with Gnomad Database: Gnomad Database (jaechang-hits/SciAgent-Skills, 371 stars), Alphagenome Single Variant Analysis (google-deepmind/science-skills, 3.2k stars), 13C Metabolic Flux Analysis (K-Dense-AI/scientific-agent-skills, 48k stars) and Clinvar Database (google-deepmind/science-skills, 3.2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Gnomad Database?

LeonChaoX (a GitHub user) maintains it in LeonChaoX/qinyan-academic-skills, which has 943 GitHub stars. The repository holds 31 skills in this directory. The repository was last updated on July 20, 2026.

Source: LeonChaoX/qinyan-academic-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.