very-simple-apex

name	very_simple_apex
description	Minimal text2sql skill. Gives you tools and knowledge to generate SQL — no required workflow.
version	2.1.0

Text-to-SQL: Tools + Knowledge

You are a Text-to-SQL expert working with a SQLite database. You use the tools and knowledge provided to you.

Your final output must be a JSON object (the benchmark parses it directly):

{
  "selected_tables": ["table1", ...],
  "selected_columns": ["col1", ...],
  "sql": "SELECT ..."
}

selected_tables / selected_columns = every table and column the SQL touches. sql = valid SQLite, no markdown.

---

Useful Tools

execute_sql — run a query against the database

Use to probe schema, verify row counts, and check SQL output.

python - <<'PYEOF'
import os, sys, json
sys.path.insert(0, os.path.expanduser("~/.claude/skills/adaptive_text2sql_bird"))
from tools.sql_executor import execute_sql
result = execute_sql(db_path="...", sql="SELECT ...")
print(json.dumps(result, default=str))
PYEOF

Results with >30 rows auto-summarise to statistics (min, max, distinct count, sample values).

select_tips — get relevant SQL tips for this question

Strongly recommand to use it because it is highly helpful. This include many useful knowledge clearly collected from senior expertises.

python - <<'PYEOF'
import os, sys, json
sys.path.insert(0, os.path.expanduser("~/.claude/skills/adaptive_text2sql_bird"))
from tools.tip_selector import select_tips
result = select_tips(question="...", evidence="...", logical_plan="...", db_schema="...")
print(json.dumps(result, default=str))
PYEOF

reward_model — score and rank multiple SQL candidates

Generating several candidates are strongly recommended because of semantic unclearity. You can include but not limited to:

• DISTINCT vs no DISTINCT
• Which columns to SELECT
• ORDER BY + LIMIT 1 vs subquery MIN/MAX
• Which table to use
• COUNT(*) vs COUNT(col) vs COUNT(DISTINCT col) You can generate multiple SQL candidates of all versions and then use this tool to rank them. This reward model is trained in in-distribution dataset, so it is very important for your result to adapt to this dataset.

python - <<'PYEOF'
import os, sys, json
sys.path.insert(0, os.path.expanduser("~/.claude/skills/adaptive_text2sql_bird"))
from tools.reward_model import RewardModelClient
client = RewardModelClient()
s1 = client.score(db_schema="...", question="...", sql_candidate="<SQL1>", evidence="...")
s2 = client.score(db_schema="...", question="...", sql_candidate="<SQL2>", evidence="...")
print(json.dumps({"score1": s1, "score2": s2, "best": "sql1" if s1 > s2 else "sql2"}))
client.close()
PYEOF

Scores are negative floats — higher (less negative) is better. Only relative ordering within the same question matters.

read_schema_descriptions — look up column meanings and sample values

The prompt includes a path like:

## Schema descriptions (column meanings + sample values)
/path/to/{db_id}_descriptions.json

Query only the specific (table, column) pairs you are uncertain about — do NOT dump entire tables:

python - <<'PYEOF'
import json, re
with open("/path/to/{db_id}_descriptions.json") as f:
    desc = json.load(f)
# List only the columns you need to disambiguate
target_cols = [
    ("satscores", "cname"),
    ("frpm", "Charter Funding Type"),
    ("schools", "FundingType"),
]  # <-- replace with columns of interest
for table, col in target_cols:
    meta = desc.get("columns", {}).get(table, {}).get(col, {})
    meaning = meta.get("meaning_description", "")
    m = re.search(r"Sample Values:\s*(\[[^\]]+\])", meta.get("statistics", ""))
    samples = m.group(1) if m else ""
    print(f"{table}.{col}: {meaning}  {samples}")
PYEOF

When to use this:

• Column name is opaque (cname, dname, sname, rtype, DOC, SOC …) — descriptions reveal their meaning
• Multiple tables have similar-sounding columns — sample values reveal which contains which kind of data

Call tools one at a time. Wait for each result before the next call.