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use this skill /analyze-data

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Using the analyze-data skill

The user mentioned wanting to "use this skill," which I interpret as referring to the /analyze-data skill. I need to follow instructions and invoke the Skill tool to start the analysis. However, I'm not seeing any specific questions or data from them. Since we already have the Titanic table loaded, perhaps the skill could profile it. I'll go ahead and call the Skill tool now to get things moving!

Skill

{
  "skill": "analyze-data",
  "args": ""
}

Launching skill: analyze-data

Planning SQL Queries

I'm considering using the parallel tool to run multiple SQL queries at once, which should speed things up. I want to show tables, describe the Titanic table, and check counts, sample data, and null rates. The wrapper only allows certain developer tools, but it looks like I can call multi_tool_use.parallel with five execute_sql commands. After getting the results, I can summarize the schema and quality, but I’ll check with the user for specific interests related to the data.

SQL Query

SHOW TABLES;

name
titanic

1 row(s)

SQL Query

DESCRIBE titanic;

column_name	column_type	null
PassengerId	BIGINT	YES
Survived	BIGINT	YES
Pclass	BIGINT	YES
Name	VARCHAR	YES
Sex	VARCHAR	YES
Age	DOUBLE	YES
SibSp	BIGINT	YES
Parch	BIGINT	YES
Ticket	VARCHAR	YES
Fare	DOUBLE	YES
Cabin	VARCHAR	YES
Embarked	VARCHAR	YES

12 row(s)

SQL Query

SELECT COUNT(*) AS row_count FROM titanic;

row_count
891

1 row(s)

SQL Query

SELECT * FROM titanic LIMIT 5;

PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Ticket	Fare	Cabin	Embarked
1	0	3	Braund, Mr. Owen Harris	male	22	1	A/5 21171	7.25		S
2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Thayer)	female	38	1	PC 17599	71.2833	C85	C
3	1	3	Heikkinen, Miss. Laina	female	26	0	STON/O2. 3101282	7.925		S
4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35	1	113803	53.1	C123	S
5	0	3	Allen, Mr. William Henry	male	35	0	373450	8.05		S

5 row(s)

SQL Query

SELECT 'PassengerId' AS column_name, COUNT(*)-COUNT(PassengerId) AS nulls, ROUND(100.0*(COUNT(*)-COUNT(PassengerId))/COUNT(*),1) AS null_pct FROM titanic
UNION ALL SELECT 'Survived', COUNT(*)-COUNT(Survived), ROUND(100.0*(COUNT(*)-COUNT(Survived))/COUNT(*),1) FROM titanic
UNION ALL SELECT 'Pclass', COUNT(*)-COUNT(Pclass), ROUND(100.0*(COUNT(*)-COUNT(Pclass))/COUNT(*),1) FROM titanic
UNION ALL SELECT 'Name', COUNT(*)-COUNT(Name), ROUND(100.0*(COUNT(*)-COUNT(Name))/COUNT(*),1) FROM titanic
UNION ALL SELECT 'Sex', COUNT(*)-COUNT(Sex), ROUND(100.0*(COUNT(*)-COUNT(Sex))/COUNT(*),1) FROM titanic
UNION ALL SELECT 'Age', COUNT(*)-COUNT(Age), ROUND(100.0*(COUNT(*)-COUNT(Age))/COUNT(*),1) FROM titanic
UNION ALL SELECT 'SibSp', COUNT(*)-COUNT(SibSp), ROUND(100.0*(COUNT(*)-COUNT(SibSp))/COUNT(*),1) FROM titanic
UNION ALL SELECT 'Parch', COUNT(*)-COUNT(Parch), ROUND(100.0*(COUNT(*)-COUNT(Parch))/COUNT(*),1) FROM titanic
UNION ALL SELECT 'Ticket', COUNT(*)-COUNT(Ticket), ROUND(100.0*(COUNT(*)-COUNT(Ticket))/COUNT(*),1) FROM titanic
UNION ALL SELECT 'Fare', COUNT(*)-COUNT(Fare), ROUND(100.0*(COUNT(*)-COUNT(Fare))/COUNT(*),1) FROM titanic
UNION ALL SELECT 'Cabin', COUNT(*)-COUNT(Cabin), ROUND(100.0*(COUNT(*)-COUNT(Cabin))/COUNT(*),1) FROM titanic
UNION ALL SELECT 'Embarked', COUNT(*)-COUNT(Embarked), ROUND(100.0*(COUNT(*)-COUNT(Embarked))/COUNT(*),1) FROM titanic
ORDER BY null_pct DESC, column_name;

column_name	nulls	null_pct
Cabin	687	77.1
Age	177	19.9
Embarked	2	0.2
Fare	0	0
Name	0	0
Parch	0	0
PassengerId	0	0
Pclass	0	0
Sex	0	0
SibSp	0	0
Survived	0	0
Ticket	0	0

12 row(s)

ask_user_question

{
  "question": "What would you like to analyze in the Titanic data next? (You can pick more than one.)",
  "options": [
    {
      "label": "Survival rate breakdowns",
      "description": "Survival rates by sex, class, embarkation port, family size, etc."
    },
    {
      "label": "Key drivers of survival",
      "description": "Quantify how features (sex, class, age, fare) relate to survival (e.g., odds ratios / simple model-style summary)."
    },
    {
      "label": "Distributions & outliers",
      "description": "Age and fare distributions, missing-age patterns, extreme fares, etc."
    },
    {
      "label": "Data quality / missingness",
      "description": "Deeper look at nulls, duplicates, inconsistent values, and suggested cleaning steps."
    }
  ],
  "multi_select": true
}

{"answers": ["Distributions & outliers"]}

SQL Query

WITH bins AS (
  SELECT
    CASE
      WHEN Age IS NULL THEN 'Missing'
      WHEN Age < 10 THEN '0-9'
      WHEN Age < 20 THEN '10-19'
      WHEN Age < 30 THEN '20-29'
      WHEN Age < 40 THEN '30-39'
      WHEN Age < 50 THEN '40-49'
      WHEN Age < 60 THEN '50-59'
      WHEN Age < 70 THEN '60-69'
      ELSE '70+'
    END AS age_bin,
    CASE
      WHEN Age IS NULL THEN 99
      WHEN Age < 10 THEN 0
      WHEN Age < 20 THEN 10
      WHEN Age < 30 THEN 20
      WHEN Age < 40 THEN 30
      WHEN Age < 50 THEN 40
      WHEN Age < 60 THEN 50
      WHEN Age < 70 THEN 60
      ELSE 70
    END AS sort_key
  FROM titanic
)
SELECT age_bin, COUNT(*) AS passengers
FROM bins
GROUP BY age_bin, sort_key
ORDER BY sort_key, age_bin;

age_bin	passengers
0-9	62
10-19	102
20-29	220
30-39	167
40-49	89
50-59	48
60-69	19
70+	7
Missing	177

9 row(s)

SQL Query

WITH fare_bins AS (
  SELECT
    CASE
      WHEN Fare IS NULL THEN 'Missing'
      WHEN Fare = 0 THEN '0'
      WHEN Fare <= 7.91 THEN '(0, 7.91]'
      WHEN Fare <= 14.45 THEN '(7.91, 14.45]'
      WHEN Fare <= 31.00 THEN '(14.45, 31.00]'
      ELSE '31.00+'
    END AS fare_bin,
    CASE
      WHEN Fare IS NULL THEN 99
      WHEN Fare = 0 THEN 0
      WHEN Fare <= 7.91 THEN 1
      WHEN Fare <= 14.45 THEN 2
      WHEN Fare <= 31.00 THEN 3
      ELSE 4
    END AS sort_key
  FROM titanic
)
SELECT fare_bin, COUNT(*) AS passengers
FROM fare_bins
GROUP BY fare_bin, sort_key
ORDER BY sort_key, fare_bin;

fare_bin	passengers
0	15
(0, 7.91]	208
(7.91, 14.45]	217
(14.45, 31.00]	229
31.00+	222

5 row(s)

SQL Query

SELECT
  MIN(Fare) AS min_fare,
  APPROX_QUANTILE(Fare, 0.25) AS p25_fare,
  APPROX_QUANTILE(Fare, 0.50) AS median_fare,
  APPROX_QUANTILE(Fare, 0.75) AS p75_fare,
  MAX(Fare) AS max_fare,
  AVG(Fare) AS avg_fare
FROM titanic;

min_fare	p25_fare	median_fare	p75_fare	max_fare	avg_fare
0	7.910897727272727	14.445692261904762	30.9377125	512.3292	32.2042079685746

1 row(s)

SQL Query

SELECT
  MIN(Age) AS min_age,
  APPROX_QUANTILE(Age, 0.25) AS p25_age,
  APPROX_QUANTILE(Age, 0.50) AS median_age,
  APPROX_QUANTILE(Age, 0.75) AS p75_age,
  MAX(Age) AS max_age,
  AVG(Age) AS avg_age,
  COUNT(*) FILTER (Age IS NULL) AS missing_age,
  COUNT(*) FILTER (Age IS NOT NULL) AS nonmissing_age
FROM titanic;

min_age	p25_age	median_age	p75_age	max_age	avg_age	missing_age	nonmissing_age
0.42	20.351851851851855	28.185950413223143	38.333333333333336	80	29.69911764705882	177	714

1 row(s)

SQL Query

SELECT PassengerId, Name, Pclass, Sex, Age, Fare, Ticket, Cabin, Embarked
FROM titanic
ORDER BY Fare DESC
LIMIT 10;

PassengerId	Name	Pclass	Sex	Age	Fare	Ticket	Cabin	Embarked
259	Ward, Miss. Anna	1	female	35	512.3292	PC 17755		C
680	Cardeza, Mr. Thomas Drake Martinez	1	male	36	512.3292	PC 17755	B51 B53 B55	C
738	Lesurer, Mr. Gustave J	1	male	35	512.3292	PC 17755	B101	C
28	Fortune, Mr. Charles Alexander	1	male	19	263	19950	C23 C25 C27	S
89	Fortune, Miss. Mabel Helen	1	female	23	263	19950	C23 C25 C27	S
342	Fortune, Miss. Alice Elizabeth	1	female	24	263	19950	C23 C25 C27	S
439	Fortune, Mr. Mark	1	male	64	263	19950	C23 C25 C27	S
312	Ryerson, Miss. Emily Borie	1	female	18	262.375	PC 17608	B57 B59 B63 B66	C
743	Ryerson, Miss. Susan Parker "Suzette"	1	female	21	262.375	PC 17608	B57 B59 B63 B66	C
300	Baxter, Mrs. James (Helene DeLaudeniere Chaput)	1	female	50	247.5208	PC 17558	B58 B60	C

10 row(s)

SQL Query

SELECT
  SUM(CASE WHEN Fare IS NULL THEN 1 ELSE 0 END) AS missing_fare,
  SUM(CASE WHEN Fare = 0 THEN 1 ELSE 0 END) AS zero_fare,
  ROUND(100.0 * SUM(CASE WHEN Fare = 0 THEN 1 ELSE 0 END) / COUNT(*), 1) AS zero_fare_pct
FROM titanic;

missing_fare	zero_fare	zero_fare_pct
0	15	1.7

1 row(s)

SQL Query

SELECT
  Sex,
  COUNT(*) AS passengers,
  COUNT(*) FILTER (Age IS NULL) AS missing_age,
  ROUND(100.0 * COUNT(*) FILTER (Age IS NULL) / COUNT(*), 1) AS missing_age_pct
FROM titanic
GROUP BY Sex
ORDER BY passengers DESC;

Sex	passengers	missing_age	missing_age_pct
male	577	124	21.5
female	314	53	16.9

2 row(s)

SQL Query

SELECT
  Pclass,
  COUNT(*) AS passengers,
  COUNT(*) FILTER (Age IS NULL) AS missing_age,
  ROUND(100.0 * COUNT(*) FILTER (Age IS NULL) / COUNT(*), 1) AS missing_age_pct
FROM titanic
GROUP BY Pclass
ORDER BY Pclass;

Pclass	passengers	missing_age	missing_age_pct
1	216	30	13.9
2	184	11	6
3	491	136	27.7

3 row(s)

SQL Query

SELECT
  PassengerId,
  Age,
  (SibSp + Parch) AS family_count,
  Pclass,
  Sex,
  Fare
FROM titanic
WHERE Age IS NOT NULL
ORDER BY Age ASC
LIMIT 10;

PassengerId	Age	family_count	Pclass	Sex	Fare
804	0.42	1	3	male	8.5167
756	0.67	2	2	male	14.5
470	0.75	3	3	female	19.2583
645	0.75	3	3	female	19.2583
79	0.83	2	2	male	29
832	0.83	2	2	male	18.75
306	0.92	3	1	male	151.55
184	1	3	2	male	39
382	1	2	3	female	15.7417
387	1	7	3	male	46.9

10 row(s)

SQL Query

SELECT
  PassengerId,
  Age,
  (SibSp + Parch) AS family_count,
  Pclass,
  Sex,
  Fare
FROM titanic
WHERE Age IS NOT NULL
ORDER BY Age DESC
LIMIT 10;

PassengerId	Age	family_count	Pclass	Sex	Fare
631	80	0	1	male	30
852	74	0	3	male	7.775
97	71	0	1	male	34.6542
494	71	0	1	male	49.5042
117	70.5	0	3	male	7.75
673	70	0	2	male	10.5
746	70	2	1	male	71
34	66	0	2	male	10.5
281	65	0	3	male	7.75
457	65	0	1	male	26.55

10 row(s)

Answered

What would you like to analyze in the Titanic data next? (You can pick more than one.)

Distributions & outliers

Answer

Passenger count by age bin (including missing)

Passenger count by fare bin

Missing Age rate by sex

Missing Age rate by passenger class

Data profile (required)

Tables: titanic
Size: 891 rows, 12 columns
Notable missingness:
- Cabin: 687 nulls (77.1%) — very sparse
- Age: 177 nulls (19.9%)
- Embarked: 2 nulls (0.2%)
- Others: essentially complete

Distributions & outliers

Age distribution (incl. missing)

The age histogram shows the dataset is concentrated in young adults, with a sizable missing-age bucket:

20–29 is the largest observed bin (220 passengers)
30–39 next (167)
Missing age is also large (177)

Summary stats (non-missing ages):

Min: 0.42, Max: 80.0
Median: ~28.19, Avg: ~29.70
25th–75th percentile: ~20.35 to 38.33

Where Age is missing (a data quality “shape” issue)

Missing age is not evenly distributed:

By sex: male 21.5% missing vs female 16.9%
By class: 3rd class 27.7% missing (highest), 2nd class 6.0% (lowest), 1st class 13.9%

That means any age-based analysis can be biased toward the groups with better-recorded ages (notably 2nd class).

Fare distribution (and outliers)

The fare chart (quartile-ish bins + a zero bin) shows fares are fairly spread across low-to-mid ranges, with a heavy right tail.

Fare summary stats:

Min: 0.0
25th percentile: ~7.91
Median: ~14.45
75th percentile: ~30.94
Mean: ~32.20 (higher than median → right-skew)
Max: 512.33 (very large outlier)

Outlier notes:

The very top fare (512.33) appears multiple times and shares the same ticket (PC 17755), suggesting a group/party ticketing pattern rather than independent purchases.
Zero fare occurs 15 times (1.7% of passengers). That’s worth sanity-checking (e.g., special cases, crew-like records, ticketing artifacts).

Suggested next “outliers” follow-ups (pick one)

Compare fare distribution by Pclass (to see whether outliers are almost entirely 1st class, and how separated the classes are).
Inspect zero-fare passengers (who they are, class, embarked, survival).
If you’re planning modeling: decide on Age handling (drop, impute, or add “Age missing” flag—missingness is clearly structured).