What does the Regulomedb Database AI skill do?

Query RegulomeDB v2 GET REST API to score variants for regulatory function and retrieve overlapping evidence (TF binding, histone marks, DNase peaks, footprints, motifs, eQTLs, chromatin state). Scores range 1a (strongest) to 7 (none). Use for GWAS hit prioritization, regulatory variant annotation, cis-regulatory discovery. Use clinvar-database for pathogenicity; gwas-database for trait associations.

Why use Regulomedb Database on TypingMind?

Because you install it once and use it with any model. Regulomedb Database is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Regulomedb Database in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/jaechang-hits/SciAgent-Skills/tree/main/skills/genomics-bioinformatics/databases/regulomedb-database. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Regulomedb Database?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Regulomedb Database?

As many as you like. As long as a model supports skills, you can use Regulomedb Database with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Regulomedb Database AI skill free?

Yes. It is published on GitHub by jaechang-hits under the CC-BY-4.0 license. You only pay your own AI provider for the tokens you use.

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

Regulomedb Database by jaechang-hits — AI Skill for Any LLM

RegulomeDB Database

Overview

RegulomeDB integrates large-scale functional genomics data (ENCODE, Roadmap Epigenomics) to score genetic variants for regulatory potential. Each variant receives a ranking from 1a (highest regulatory confidence: eQTL + TF + DNase + motif + chromatin) to 7 (no known regulatory function). The v2 API is exposed as GET https://regulomedb.org/regulome-search/; the legacy POST /regulome-search/, POST /regulome-summary/, and GET /regulome-datasets/ JSON endpoints are no longer functional (return regulome-notfound stubs or 500). Access is free and requires no authentication.

When to Use

Prioritizing GWAS hits for regulatory follow-up — identify which SNPs land in active regulatory elements
Annotating a VCF or variant list with regulatory scores to filter to functionally relevant variants
Identifying which transcription factors bind near a variant of interest (via the @graph evidence rows)
Checking whether a non-coding variant overlaps a QTL and active chromatin simultaneously (features.QTL)
Retrieving all annotated rsIDs in a genomic region for cis-regulatory analysis (region query with nearby_snps)
Use clinvar-database instead when you need clinical pathogenicity classifications; RegulomeDB scores regulatory function, not germline disease association
Use gwas-database instead when you want published GWAS associations with traits

Prerequisites

Python packages: requests, pandas, matplotlib
Data requirements: rsIDs (e.g., rs4946036), genomic positions (chr1:1000000), or region coordinates (chr1:1000000-2000000)
Genome build: GRCh38 (default) or GRCh37; specify in all requests
Rate limits: No published rate limits; use time.sleep(0.3) between requests in batch workflows

bash
pip install requests pandas matplotlib

Quick Start

python
import requests

BASE = "https://regulomedb.org"

def regulome_score(variant, genome="GRCh38"):
    """Score a single variant (rsID or chr:pos-pos) via the GET /regulome-search/ endpoint."""
    r = requests.get(
        f"{BASE}/regulome-search/",
        params={"regions": variant, "genome": genome, "format": "json"},
        timeout=30,
    )
    r.raise_for_status()
    d = r.json()
    rs = d.get("regulome_score", {})
    vs = d.get("variants", [])
    return {
        "query": variant,
        "ranking": rs.get("ranking"),           # 1a / 1b / ... / 7
        "probability": float(rs.get("probability", 0)),
        "rsids": vs[0].get("rsids") if vs else [],
        "chrom": vs[0].get("chrom") if vs else None,
        "pos": vs[0].get("start") if vs else None,
    }

print(regulome_score("rs4946036"))
# {'query': 'rs4946036', 'ranking': '7', 'probability': 0.18412,
#  'rsids': ['rs4946036'], 'chrom': 'chr6', 'pos': 114819799}

Core API

Query 1: Score a Single Variant (rsID or position)

The GET /regulome-search/ endpoint accepts an rsID or coordinate as regions=. Returns a regulome_score block (probability, ranking, tissue-specific scores) plus features flags and the per-dataset @graph evidence rows.

python
import requests

BASE = "https://regulomedb.org"

def score_variant(variant, genome="GRCh38"):
    """Return the regulome_score block and resolved coordinates."""
    r = requests.get(
        f"{BASE}/regulome-search/",
        params={"regions": variant, "genome": genome, "format": "json"},
        timeout=30,
    )
    r.raise_for_status()
    d = r.json()
    rs = d.get("regulome_score", {})
    vs = d.get("variants", [])
    feats = d.get("features", {})
    print(f"Variant   : {variant}")
    print(f"Resolved  : {vs[0]['chrom']}:{vs[0]['start']} ({', '.join(vs[0].get('rsids', []))})")
    print(f"Ranking   : {rs.get('ranking')}  prob={rs.get('probability')}")
    print(f"Features  : ChIP={feats['ChIP']} Chromatin_accessibility={feats['Chromatin_accessibility']} "
          f"QTL={feats['QTL']} Footprint={feats['Footprint']} PWM_matched={feats['PWM_matched']}")
    return d

# Strong-regulatory locus example
score_variant("chr11:5226739-5226740")
# Ranking: 1a (HBB beta-globin promoter, multi-evidence)

python
# Score by chromosomal position alone
score_variant("chr17:7670000-7670001")  # TP53 region

Query 2: Region Scan — List Annotated Variants in a Window

A range query returns up to limit resolved variants (variants[]) and all @graph evidence rows in the window, plus nearby_snps (rsIDs adjacent to the resolved hits).

python
import requests, pandas as pd

BASE = "https://regulomedb.org"

def scan_region(chrom, start, end, genome="GRCh38", limit=200):
    """List variants in a region with their resolved positions and overlapping rsIDs."""
    r = requests.get(
        f"{BASE}/regulome-search/",
        params={"regions": f"{chrom}:{start}-{end}", "genome": genome,
                "format": "json", "limit": limit},
        timeout=60,
    )
    r.raise_for_status()
    d = r.json()
    variants = d.get("variants", [])
    print(f"Variants in {chrom}:{start}-{end}: {len(variants)} (total indexed = {d.get('total')})")
    rows = [{"rsids": ", ".join(v.get("rsids", [])),
             "chrom": v.get("chrom"),
             "start": v.get("start"),
             "end": v.get("end")} for v in variants]
    return pd.DataFrame(rows)

df = scan_region("chr11", 5226000, 5227000)
print(df.head(10).to_string(index=False))

Query 3: Full Evidence — Parse the `@graph` Rows

Each @graph[i] row is one experimental piece of evidence overlapping the query. Fields: method, target_label, biosample_ontology{term_name, organ_slims, classification}, dataset, file, value, chrom, start, end, strand, ancestry, disease_term_name.

python
import requests, pandas as pd

BASE = "https://regulomedb.org"

def evidence_rows(variant, genome="GRCh38"):
    r = requests.get(
        f"{BASE}/regulome-search/",
        params={"regions": variant, "genome": genome, "format": "json"},
        timeout=60,
    )
    r.raise_for_status()
    g = r.json().get("@graph", [])
    rows = []
    for row in g:
        bs = row.get("biosample_ontology") or {}
        rows.append({
            "method": row.get("method"),
            "target_label": row.get("target_label"),
            "biosample": bs.get("term_name"),
            "organ_slims": ", ".join(bs.get("organ_slims") or []),
            "dataset": row.get("dataset", "").split("/")[-2] if row.get("dataset") else None,
            "value": row.get("value"),
        })
    return pd.DataFrame(rows)

df_evidence = evidence_rows("chr11:5226739-5226740")
# Each method is one of: ChIP-seq, Histone ChIP-seq, ATAC-seq, DNase-seq,
# footprints, PWMs, chromatin state, eQTLs
print(df_evidence["method"].value_counts())

Query 4: TF ChIP-seq Hits — Filter Evidence by Method

To list the transcription factors binding near a variant, filter @graph rows where method == "ChIP-seq" and read target_label + biosample_ontology.term_name.

python
import requests, pandas as pd

BASE = "https://regulomedb.org"

def tf_binding(variant, genome="GRCh38"):
    r = requests.get(f"{BASE}/regulome-search/",
                     params={"regions": variant, "genome": genome, "format": "json"},
                     timeout=60)
    r.raise_for_status()
    rows = []
    for g in r.json().get("@graph", []):
        if g.get("method") != "ChIP-seq":
            continue
        bs = g.get("biosample_ontology") or {}
        rows.append({
            "tf": g.get("target_label"),
            "biosample": bs.get("term_name"),
            "classification": bs.get("classification"),
        })
    return pd.DataFrame(rows)

df_tfs = tf_binding("chr11:5226739-5226740")
print(f"TF ChIP-seq peaks overlapping query: {len(df_tfs)}")
print(df_tfs.groupby("tf").size().sort_values(ascending=False).head(10))

Query 5: Tissue-Specific Regulatory Score

regulome_score.tissue_specific_scores maps ~50 tissues to per-tissue regulatory probabilities (0–1). Rank tissues to identify where the variant has the strongest regulatory signal.

python
import requests, pandas as pd

BASE = "https://regulomedb.org"

def tissue_scores(variant, genome="GRCh38", top_n=10):
    r = requests.get(f"{BASE}/regulome-search/",
                     params={"regions": variant, "genome": genome, "format": "json"},
                     timeout=30)
    r.raise_for_status()
    ts = r.json().get("regulome_score", {}).get("tissue_specific_scores", {})
    s = pd.Series({k: float(v) for k, v in ts.items()})
    return s.sort_values(ascending=False).head(top_n)

print("Top tissues by regulatory probability for chr11:5226739-5226740:")
print(tissue_scores("chr11:5226739-5226740"))

Query 6: Nearby SNPs — List rsIDs Adjacent to a Position

nearby_snps carries dbSNP rsIDs near the resolved coordinates, with reference/alt allele frequencies (when GnomAD-indexed).

python
import requests, pandas as pd

BASE = "https://regulomedb.org"

def nearby(variant, genome="GRCh38"):
    r = requests.get(f"{BASE}/regulome-search/",
                     params={"regions": variant, "genome": genome, "format": "json"},
                     timeout=30)
    r.raise_for_status()
    rows = []
    for s in r.json().get("nearby_snps", []):
        rows.append({
            "rsid": s.get("rsid"),
            "chrom": s.get("chrom"),
            "pos": s.get("coordinates", {}).get("gte"),
            "type": s.get("variation_type"),
            "maf": s.get("maf"),
        })
    return pd.DataFrame(rows)

df_nearby = nearby("rs4946036")
print(f"Nearby SNPs to rs4946036: {len(df_nearby)}")
print(df_nearby.head(10).to_string(index=False))

Key Concepts

RegulomeDB Scoring Schema

RegulomeDB ranks encode the strength of evidence overlapping a variant. The regulome_score.ranking string is one of:

Ranking	Evidence	Confidence
1a	eQTL + TF + DNase + motif + matched footprint	Highest
1b–1f	Multi-evidence (sub-ranks reflect which inputs match)	Very high
2a	TF binding + DNase + motif	High
2b	TF binding + any DNase (no motif required)	High
2c	TF binding + DNase (limited)	Moderate-high
3a	DNase + motif (no TF ChIP-seq)	Moderate
3b	Motif only (no DNase)	Moderate
4	Single TF binding evidence	Low-moderate
5	DNase peak only	Low
6	Other regulatory evidence	Minimal
7	No known regulatory function	None

regulome_score.probability is the numeric model score (0–1) underlying the discrete ranking.

`features` Booleans vs `@graph` Detail

features is a high-level summary — boolean flags indicating presence of ChIP, Chromatin_accessibility, Footprint, PWM, QTL, etc. For per-dataset detail (which exact TF / cell type / experiment), iterate @graph[] and filter by method.

Variant Input Formats

regions= accepts:

python
# rsID — resolved server-side to current-build coordinates
"rs4946036"

# Single-position range
"chr11:5226739-5226740"

# Wider region (returns multiple variants[] entries + larger @graph)
"chr11:5226000-5227000"

Common Workflows

Workflow 1: GWAS Hit Prioritization

Goal: Score a list of GWAS lead SNPs and rank by regulatory confidence.

python
import requests, time, pandas as pd
import matplotlib.pyplot as plt

BASE = "https://regulomedb.org"

gwas_snps = ["rs7903146", "rs10811661", "rs1801282", "rs4946036",
             "rs2268177", "rs10830963", "rs1111875"]

records = []
for snp in gwas_snps:
    r = requests.get(f"{BASE}/regulome-search/",
                     params={"regions": snp, "genome": "GRCh38", "format": "json"},
                     timeout=30)
    r.raise_for_status()
    d = r.json()
    rs = d.get("regulome_score", {})
    feats = d.get("features", {})
    g = d.get("@graph", [])
    tfs = sorted({row["target_label"] for row in g
                  if row.get("method") == "ChIP-seq" and row.get("target_label")})
    records.append({
        "snp": snp,
        "ranking": rs.get("ranking"),
        "probability": float(rs.get("probability", 0)),
        "has_qtl": feats.get("QTL", False),
        "tf_count": len(tfs),
        "num_evidence_rows": len(g),
    })
    time.sleep(0.3)

df = pd.DataFrame(records).sort_values("probability", ascending=False)
print(df.to_string(index=False))
df.to_csv("gwas_regulatory_priority.csv", index=False)

fig, ax = plt.subplots(figsize=(8, 4))
ax.bar(df["snp"], df["probability"], color="steelblue", edgecolor="black")
ax.set_ylabel("Regulatory probability")
ax.set_title("GWAS lead-SNP regulatory probabilities")
plt.xticks(rotation=45, ha="right")
plt.tight_layout()
plt.savefig("gwas_score_distribution.png", dpi=150, bbox_inches="tight")

Workflow 2: Locus Evidence Profile

Goal: Summarize the methods underlying the score at a locus (e.g., HBB promoter).

python
import requests, pandas as pd
import matplotlib.pyplot as plt

BASE = "https://regulomedb.org"

def locus_profile(region, genome="GRCh38"):
    r = requests.get(f"{BASE}/regulome-search/",
                     params={"regions": region, "genome": genome, "format": "json"},
                     timeout=60)
    r.raise_for_status()
    d = r.json()
    rs = d.get("regulome_score", {})
    g = d.get("@graph", [])
    counts = pd.Series([row.get("method") for row in g]).value_counts()
    print(f"\n=== {region} | ranking={rs.get('ranking')} prob={rs.get('probability')} ===")
    print(counts.to_string())
    return counts

counts = locus_profile("chr11:5226739-5226740")  # HBB

fig, ax = plt.subplots(figsize=(8, 4))
counts.plot(kind="barh", color="seagreen", ax=ax)
ax.set_xlabel("Evidence rows in @graph")
ax.set_title("Regulatory evidence by method (HBB promoter)")
plt.tight_layout()
plt.savefig("locus_evidence_profile.png", dpi=150, bbox_inches="tight")

Key Parameters

Parameter	Endpoint	Default	Range / Options	Effect
`regions`	GET /regulome-search/	required	rsID, `chrN:start-end`, or `chrN:pos-pos`	Variant/region to score
`genome`	GET /regulome-search/	`"GRCh38"`	`"GRCh38"`, `"GRCh37"`	Reference genome assembly
`format`	GET /regulome-search/	`"html"`	`"json"`, `"tsv"`, `"html"`	Use `"json"` for programmatic access
`limit`	GET /regulome-search/	`200`	`1`–`1000`	Max resolved variants in `variants[]` for region queries
`from`	GET /regulome-search/	`0`	non-negative int	Offset for paging through large `@graph` lists

Best Practices

Use GET, not POST. The POST /regulome-search/, POST /regulome-summary/, and GET /regulome-datasets/ JSON endpoints return a regulome-notfound stub or HTTP 500. Only GET /regulome-search/?regions=...&genome=...&format=json returns real data.
Read regulome_score.ranking, not regulomedb_score. The field used to be named regulomedb_score in legacy docs; the live API exposes it as regulome_score.ranking (string like "1a", "7").
Add time.sleep(0.3) between calls. RegulomeDB has no published rate limit, but polite spacing prevents intermittent 502s under load.
Score 7 ≠ "no regulation". Score 7 means absence of evidence in RegulomeDB's curated datasets, not biological absence of regulation. Cross-check with ENCODE/Roadmap directly via encode-database for negative results.
For tissue specificity, use tissue_specific_scores. The single ranking is an aggregate; the per-tissue probabilities reveal where the variant has the strongest regulatory signal.
Watch the GRCh37 vs GRCh38 build. rsIDs are auto-resolved to the requested build, but coordinate-based regions=chrN:start-end queries are build-specific — pass the matching genome= value.

Common Recipes

Recipe: Quick Score Lookup

When to use: One-off check before kicking off a larger pipeline.

python
import requests

def quick_score(variant, genome="GRCh38"):
    r = requests.get("https://regulomedb.org/regulome-search/",
                     params={"regions": variant, "genome": genome, "format": "json"},
                     timeout=20)
    r.raise_for_status()
    rs = r.json().get("regulome_score", {})
    print(f"{variant}: ranking={rs.get('ranking')}  prob={rs.get('probability')}")

quick_score("rs4946036")             # ranking=7 prob=0.18412
quick_score("chr11:5226739-5226740") # ranking=1a (HBB promoter)

Recipe: Filter Variants to High-Confidence Regulatory Set

python
import requests, time, pandas as pd

HIGH_CONF = {"1a", "1b", "1c", "1d", "1e", "1f", "2a", "2b"}

def high_conf_only(variants, genome="GRCh38"):
    keep = []
    for v in variants:
        r = requests.get("https://regulomedb.org/regulome-search/",
                         params={"regions": v, "genome": genome, "format": "json"},
                         timeout=30)
        ranking = r.json().get("regulome_score", {}).get("ranking")
        if ranking in HIGH_CONF:
            keep.append({"variant": v, "ranking": ranking})
        time.sleep(0.3)
    return pd.DataFrame(keep)

df = high_conf_only(["rs4946036", "rs7903146", "chr11:5226739-5226740"])
print(df.to_string(index=False))

Recipe: QTL-Linked Variants

When to use: Find variants with features.QTL == True, i.e. those overlapping a curated QTL row in @graph (method "QTLs").

python
import requests, time, pandas as pd

def qtl_overlap(variants, genome="GRCh38"):
    rows = []
    for v in variants:
        r = requests.get("https://regulomedb.org/regulome-search/",
                         params={"regions": v, "genome": genome, "format": "json"},
                         timeout=30)
        d = r.json()
        if not d.get("features", {}).get("QTL"):
            time.sleep(0.3); continue
        for g in d.get("@graph", []):
            if g.get("method") == "QTLs":
                rows.append({
                    "variant": v,
                    "ranking": d.get("regulome_score", {}).get("ranking"),
                    "qtl_target": g.get("target_label"),
                    "value": g.get("value"),
                    "biosample": (g.get("biosample_ontology") or {}).get("term_name"),
                })
        time.sleep(0.3)
    return pd.DataFrame(rows)

print(qtl_overlap(["rs4946036", "chr11:5226739-5226740"]))

Troubleshooting

Problem	Cause	Solution
Response body `{"@id":"/regulome-notfound","@type":["regulome-help"]...}`	Using the legacy POST `/regulome-search/` with a JSON body	Switch to GET with `regions=` query param + `format=json`
HTTP 500	Hitting the deprecated `/regulome-summary/` endpoint	Endpoint is dead; aggregate counts client-side from `@graph[].method`
`KeyError: 'regulomedb_score'`	Field renamed	Use `regulome_score.ranking` (string) and `regulome_score.probability` (float-as-string)
`peaks` / `eqtls` / `assay_type` missing	Old schema	Iterate `@graph[]` and filter by `method` (`ChIP-seq`, `DNase-seq`, `Histone ChIP-seq`, `ATAC-seq`, `footprints`, `PWMs`, `QTLs`, `chromatin state`)
Empty `variants[]` for an rsID	rsID not in RegulomeDB index or build mismatch	Try the chr:pos form; check `genome=` matches the coordinates
Region search returns 0 `@graph` rows	Region size too small or in an uncharacterized chromosome	Widen the window to ≥ 200 bp; avoid alt contigs (`chrUn_`, `_random`)
Region query truncates at 200 results	Default `limit=200`	Pass `limit=1000` or page with `from=0,200,400,...`

Related Skills

gwas-database — NHGRI-EBI GWAS Catalog for published SNP-trait associations; pair with RegulomeDB to prioritize GWAS hits
clinvar-database — Clinical pathogenicity classifications; complements RegulomeDB's functional regulatory evidence
encode-database — Direct ENCODE REST API access for the TF ChIP-seq / ATAC-seq peak sets that underlie RegulomeDB scores
ensembl-database — Variant annotation and gene coordinate lookup; use to map rsIDs to genomic positions before region queries

References

RegulomeDB Official Help — Documentation and scoring schema
RegulomeDB search endpoint — Main REST API entry point (GET only)
Boyle AP et al. "Annotation of functional variation in personal genomes using RegulomeDB." Genome Research 22(9): 1790–1797 (2012). https://doi.org/10.1101/gr.137323.112
Dong S et al. "Annotating genomic variants and their functional effects using RegulomeDB 2.0." Bioinformatics 35(24): 5341–5343 (2019). https://doi.org/10.1093/bioinformatics/btz560

Publisher	jaechang-hits
Repository	`SciAgent-Skills`
Skill name	`regulomedb-database`
Stars	381
Forks	45
Bundled files	Instructions only
License	CC-BY-4.0
Links	Skill source Repository Homepage

Regulomedb Database

Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

RegulomeDB Database

Overview

When to Use

Prerequisites

Quick Start

Core API

Query 1: Score a Single Variant (rsID or position)

Query 2: Region Scan — List Annotated Variants in a Window

Query 3: Full Evidence — Parse the `@graph` Rows

Query 4: TF ChIP-seq Hits — Filter Evidence by Method

Query 5: Tissue-Specific Regulatory Score

Query 6: Nearby SNPs — List rsIDs Adjacent to a Position

Key Concepts

RegulomeDB Scoring Schema

`features` Booleans vs `@graph` Detail

Variant Input Formats

Common Workflows

Workflow 1: GWAS Hit Prioritization

Workflow 2: Locus Evidence Profile

Key Parameters

Best Practices

Common Recipes

Recipe: Quick Score Lookup

Recipe: Filter Variants to High-Confidence Regulatory Set

Recipe: QTL-Linked Variants

Troubleshooting

Related Skills

References

Frequently asked questions

Set up your own AI workspace now

Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

RegulomeDB Database

Overview

When to Use

Prerequisites

Quick Start

Core API

Query 1: Score a Single Variant (rsID or position)

Query 2: Region Scan — List Annotated Variants in a Window

Query 3: Full Evidence — Parse the @graph Rows

Query 4: TF ChIP-seq Hits — Filter Evidence by Method

Query 5: Tissue-Specific Regulatory Score

Query 6: Nearby SNPs — List rsIDs Adjacent to a Position

Key Concepts

RegulomeDB Scoring Schema

features Booleans vs @graph Detail

Variant Input Formats

Common Workflows

Workflow 1: GWAS Hit Prioritization

Workflow 2: Locus Evidence Profile

Key Parameters

Best Practices

Common Recipes

Recipe: Quick Score Lookup

Recipe: Filter Variants to High-Confidence Regulatory Set

Recipe: QTL-Linked Variants

Troubleshooting

Related Skills

References

Frequently asked questions

Related AI Skills

Set up your own AI workspace now

Query 3: Full Evidence — Parse the `@graph` Rows

`features` Booleans vs `@graph` Detail