Agent-Skills.md

Agent Skills: CBDB Local (SQLite)

Query the China Biographical Database (CBDB) locally via SQLite for biographical data on 656K+ historical Chinese figures from the 7th century BCE through the 19th century CE. Use when searching for Chinese historical figures, scholars, officials, or literary figures — their biographical details, family/kinship networks, official postings, social associations, examination records, or addresses. Runs entirely locally after initial database download (~556 MB). Faster and more flexible than the API version.

UncategorizedID: kltng/humanities-skills/cbdb-local

Install this agent skill to your local

pnpm dlx add-skill https://github.com/kltng/humanities-skills/tree/HEAD/cbdb-local

Skill Files

Browse the full folder contents for cbdb-local.

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cbdb-local/SKILL.md

Skill Metadata

Name
cbdb-local
Description
Query the China Biographical Database (CBDB) locally via SQLite for biographical data on 656K+ historical Chinese figures from the 7th century BCE through the 19th century CE. Use when searching for Chinese historical figures, scholars, officials, or literary figures — their biographical details, family/kinship networks, official postings, social associations, examination records, or addresses. Runs entirely locally after initial database download (~556 MB). Faster and more flexible than the API version.

CBDB Local (SQLite)

Query the China Biographical Database locally — 656K+ historical Chinese figures, 7th c. BCE to 19th c. CE. No network calls after setup.

When to Use

  • Use cbdb-local (this skill) for: offline access, complex queries, batch analysis, full kinship/social network traversal, raw SQL queries
  • Use cbdb-api for: quick lookups when database isn't downloaded, or when disk space is limited

Setup

Download and extract the SQLite database (~69 MB compressed, ~556 MB uncompressed):

python3 scripts/cbdb_query.py setup

Requires 7z for extraction:

  • macOS: brew install 7zip
  • Windows: winget install 7zip.7zip or download from https://www.7-zip.org/
  • Linux: sudo apt install p7zip-full

Setup is one-time. The script downloads from GitHub, extracts, creates search indexes, and cleans up the archive.

Quick Start

Search by Name

# Chinese name (exact match, then alt names, then fuzzy)
python3 scripts/cbdb_query.py person "蘇軾"

# Pinyin name
python3 scripts/cbdb_query.py person "Su Shi"

# By person ID (returns full profile)
python3 scripts/cbdb_query.py person --id 3767

Single match returns a full profile (bio + alt names + kinship + offices + associations + addresses + entries + status + texts + institutions + posting addresses). Multiple matches return a list of candidates.

Specific Data

python3 scripts/cbdb_query.py kinship 3767       # Family tree
python3 scripts/cbdb_query.py offices 3767        # Official postings
python3 scripts/cbdb_query.py associations 3767   # Social network
python3 scripts/cbdb_query.py addresses 3767      # Places/addresses
python3 scripts/cbdb_query.py entries 3767        # Exam/entry records
python3 scripts/cbdb_query.py altnames 3767       # Alternative names
python3 scripts/cbdb_query.py status 3767         # Social status
python3 scripts/cbdb_query.py texts 3767          # Writings/texts
python3 scripts/cbdb_query.py institutions 3767   # Academies, temples, etc.
python3 scripts/cbdb_query.py postaddr 3767       # Places of official service

Raw SQL

python3 scripts/cbdb_query.py sql "SELECT c_personid, c_name_chn, c_birthyear FROM BIOG_MAIN WHERE c_dy = 15 AND c_birthyear > 1000 LIMIT 10"

Only SELECT queries are allowed. For complex queries, use ZZZ_ denormalized tables (e.g., ZZZ_ENTRY_DATA, ZZZ_KIN_BIOG_ADDR) which pre-join names and descriptions — much simpler than multi-table joins. See references/database_schema.md for the full list.

Output Format

All commands output JSON. Example for person "蘇軾":

{
  "basic_info": {
    "person_id": 3767,
    "name_chn": "蘇軾",
    "name_pinyin": "Su Shi",
    "dynasty_chn": "宋",
    "birth_year": 1036,
    "death_year": 1101
  },
  "alt_names": [
    { "name_chn": "子瞻", "type_chn": "字", "type": "Courtesy name" },
    { "name_chn": "東坡居士", "type_chn": "室名、別號", "type": "Studio name, Style name" }
  ],
  "kinship": [
    { "name_chn": "蘇洵", "relation_chn": "父", "relation": "F" },
    { "name_chn": "蘇轍", "relation_chn": "弟", "relation": "B-" }
  ],
  "offices": [...],
  "associations": [...],
  "addresses": [...],
  "entries": [...],
  "status": [...],
  "texts": [
    { "title_chn": "東坡集", "role_chn": "撰著者", "role": "Author" }
  ],
  "institutions": [...],
  "posted_addresses": [...]
}

Kinship Tree Visualization

Generate family tree diagrams from CBDB kinship data.

Tree Commands

# Graphviz DOT format (recommended for quality)
python3 scripts/cbdb_query.py tree 3767 -d 2 -f dot -o su_shi.dot

# Render DOT to image (requires: brew install graphviz)
dot -Tsvg su_shi.dot -o su_shi.svg
dot -Tpng su_shi.dot -o su_shi.png

# Mermaid format (paste into GitHub, Obsidian, etc.)
python3 scripts/cbdb_query.py tree 3767 -d 2 -f mermaid -o su_shi.md

# Direct SVG (requires graphviz, falls back to DOT if missing)
python3 scripts/cbdb_query.py tree 3767 -d 2 -f svg -o su_shi.svg

Visual Conventions

  • Gold node = ego (the queried person)
  • Blue nodes = male, Pink nodes = female
  • Solid arrows = parent → child
  • Dashed lines = marriage / affinal relations
  • Dotted lines = siblings
  • Nodes show name and birth–death years when known
  • Generations are laid out top-to-bottom (ancestors above, descendants below)

Options

| Flag | Description | Default | |---|---|---| | --depth / -d | BFS expansion depth from ego | 2 | | --format / -f | dot, mermaid, or svg | dot | | --output / -o | Output file path | Auto-named |

Depth 1 = ego's direct kin only. Depth 2 = kin of kin (recommended). Depth 3+ can produce very large trees.

Network Export (for Gephi / Graph Analysis)

Export CBDB relationships as network graphs in GEXF (Gephi native), CSV (nodes + edges), or JSON.

Export Commands

# Kinship network from a person, expanding outward
python3 scripts/cbdb_query.py export-kinship 3767 -d 2 -o su_shi_kin.gexf

# Social association network
python3 scripts/cbdb_query.py export-associations 3767 -d 1 -o su_shi_assoc.gexf

# Combined kinship + associations
python3 scripts/cbdb_query.py export-network 3767 -d 1 -o su_shi_full.gexf

# Co-office network: people who held the same office
python3 scripts/cbdb_query.py export-office "翰林學士" --dynasty 15 -o hanlin.gexf

# Person-place bipartite network
python3 scripts/cbdb_query.py export-place "眉山" -o meishan.gexf

Options

| Flag | Description | Default | |---|---|---| | --format / -f | gexf, csv, or json | gexf | | --depth / -d | Expansion depth (1 = direct, 2 = friends-of-friends) | 1 | | --output / -o | Output file path | Auto-named | | --dynasty | Filter by dynasty code (e.g., 15 = Song) | All | | --year-from | Filter postings from this year | None | | --year-to | Filter postings up to this year | None | | --addr-type | Filter place network by address type (1 = 籍貫) | All |

Network Types

| Command | Nodes | Edges | Use Case | |---|---|---|---| | export-kinship | Persons | Kin relations (labeled F, M, S, B, W, etc.) | Family tree / clan analysis | | export-associations | Persons | Social ties (friend, teacher, political ally) | Social network analysis | | export-network | Persons | Both kin + social | Combined prosopography | | export-office | Persons | Co-officeholding | Political elite networks | | export-place | Persons + Places | Affiliations | Spatial analysis, migration |

Multiple Seeds

All person-based exports accept multiple person IDs:

python3 scripts/cbdb_query.py export-kinship 3767 1762 -d 1 -o su_wang.gexf

Node Attributes (available in Gephi)

name_chn, name_pinyin, gender, dynasty_chn, dynasty, birth_year, death_year, index_year. Place nodes add longitude, latitude, node_type.

Edge Attributes

type (kinship/association/co-office/person-place), relation_chn, relation.

Tips

  • Start with depth=1 — depth=2 can produce very large networks (thousands of nodes)
  • CSV format produces two files: *_nodes.csv and *_edges.csv, directly importable into Gephi via "Import Spreadsheet"
  • GEXF is recommended — preserves all attributes and is Gephi's native format
  • Office codes are dynasty-specific — use --dynasty to narrow results, or omit for cross-dynasty comparison

Important Caveats

  • Index year is artificial: Derived estimate of birth year, not a real date. Many people have 0 (unknown) and are excluded by year filters.
  • Song dynasty = one code (15): Does not distinguish Northern from Southern Song.
  • Office codes are dynasty-specific: A Song office code won't match in a Ming query. Search by Chinese office name for cross-dynasty comparison.
  • Association dates are usually empty: Filter by index year of individuals instead.
  • Extended kinship searches can be huge: Start with conservative distance parameters.

Using in Python

import sys
sys.path.insert(0, "scripts")
from cbdb_query import (
    get_connection, search_person, get_full_profile,
    get_kinship, get_offices, get_associations,
    get_texts, get_institutions, get_posted_addresses, run_sql,
    build_kinship_network, build_association_network,
    build_combined_network, build_office_network,
    build_place_network, export_network,
)

conn = get_connection()

# Search
results = search_person(conn, "王安石")
print(f"Found {len(results)} results")

# Full profile
profile = get_full_profile(conn, 3767)  # Su Shi
for kin in profile["kinship"]:
    print(f"{kin['relation_chn']}: {kin['name_chn']}")

# Raw SQL for advanced queries
rows = run_sql(conn, """
    SELECT b.c_name_chn, COUNT(k.c_kin_id) as kin_count
    FROM BIOG_MAIN b
    JOIN KIN_DATA k ON k.c_personid = b.c_personid
    WHERE b.c_dy = 15
    GROUP BY b.c_personid
    ORDER BY kin_count DESC
    LIMIT 10
""")

# Export network for Gephi
network = build_kinship_network(conn, [3767], depth=2)
export_network(network, "su_shi_kin.gexf", "gexf")

Database Coverage

| Table | Records | Description | |---|---|---| | BIOG_MAIN | 656,436 | Core biographical records | | POSTING_DATA | 601,663 | Career posting records | | POSTED_TO_OFFICE_DATA | 601,002 | Office assignments with titles | | KIN_DATA | 553,330 | Kinship/family relationships | | BIOG_ADDR_DATA | 455,705 | Person-place associations | | ENTRY_DATA | 263,267 | Exam/entry records | | ALTNAME_DATA | 206,132 | Alternative names (zi, hao, etc.) | | ASSOC_DATA | 186,876 | Social associations | | STATUS_DATA | 69,344 | Social status records | | BIOG_TEXT_DATA | ~50,000 | Person-text relations | | BIOG_INST_DATA | ~10,000 | Person-institution relations | | OFFICE_CODES | 34,052 | Office title lookup | | ADDR_CODES | 30,099 | Place name lookup (with coordinates) | | DYNASTIES | 85 | Dynasty lookup |

Key Person IDs for Testing

| Person | ID | Dynasty | |---|---|---| | 蘇軾 Su Shi | 3767 | 宋 | | 王安石 Wang Anshi | 1762 | 宋 | | 歐陽修 Ouyang Xiu | 4766 | 宋 | | 司馬光 Sima Guang | 3692 | 宋 | | 李白 Li Bai | 21646 | 唐 | | 杜甫 Du Fu | 21831 | 唐 |

Comparison with cbdb-api

| Feature | cbdb-local | cbdb-api | |---|---|---| | Setup | Download ~556 MB | None | | Speed | Instant (local SQLite) | Network latency + rate limits | | Offline | Yes | No | | Raw SQL | Yes | No | | Batch queries | Fast | Rate-limited | | Data freshness | Download date | Always current |

Resources

  • scripts/cbdb_query.py — Standalone query tool (Python 3.9+, zero dependencies)
  • references/database_schema.md — Full SQLite schema and query guide
  • Source: cbdb-project/cbdb_sqlite
  • CBDB Project: https://projects.iq.harvard.edu/cbdb
CBDB Local (SQLite) Skill | Agent Skills