file-processing

Name: file-processing
Author: aws-samples

88stars9forks

How to install file-processing?

aws-samples/sample-strands-agents-agentskills/file-processing

$npx -y skills add aws-samples/sample-strands-agents-agentskills --skill file-processing

Installs into the current project.

Summary

Process and analyze CSV, JSON, and text files with data transformation, cleaning, analysis, and visualization capabilities

Files · 1

View on GitHub

SKILL.md

1	# File Processing Skill
2
3	## Purpose
4
5	Process structured data files (CSV, JSON, text) with comprehensive capabilities for data cleaning, transformation, analysis, and export. This skill enables working with data files without requiring users to write code.
6
7	## When to Use This Skill
8
9	Use this skill when you need to:
10	- Load and parse CSV or JSON files
11	- Clean and transform data
12	- Perform statistical analysis
13	- Filter, sort, or aggregate data
14	- Merge or join datasets
15	- Convert between formats (CSV ↔ JSON)
16	- Generate summary reports
17
18	## Capabilities
19
20	### 1. Data Loading
21
22	Supported formats:
23	- CSV files: Any delimiter (comma, tab, semicolon, etc.)
24	- JSON files: Single objects or arrays of objects
25	- Text files: Custom delimited formats
26
27	### 2. Data Cleaning
28
29	Available operations:
30	- Remove duplicate rows
31	- Handle missing values (drop, fill, interpolate)
32	- Normalize text (trim whitespace, standardize case)
33	- Convert data types
34	- Remove outliers
35	- Validate data against rules
36
37	### 3. Data Transformation
38
39	Available operations:
40	- Filter: Select rows based on conditions
41	- Select: Choose specific columns
42	- Sort: Order by one or more columns
43	- Group: Aggregate data by categories
44	- Pivot: Reshape data (wide ↔ long format)
45	- Merge: Combine multiple datasets
46	- Calculate: Add derived columns
47
48	### 4. Data Analysis
49
50	Available analyses:
51	- Descriptive statistics (mean, median, std, etc.)
52	- Frequency distributions
53	- Correlation analysis
54	- Trend detection
55	- Missing data analysis
56	- Data quality assessment
57
58	### 5. Export
59
60	Output formats:
61	- CSV files
62	- JSON files (objects or arrays)
63	- Markdown tables
64	- Summary reports
65
66	## Instructions for Execution
67
68	When this skill is activated, follow these steps:
69
70	### Step 1: Understand the Request
71
72	Ask clarifying questions if needed:
73	- What file(s) need to be processed?
74	- What specific analysis or transformation is required?
75	- What output format is desired?
76	- Are there any specific requirements or constraints?
77
78	### Step 2: Load the Data
79
80	Use `shell` to load and process data:
81
82	```python
83	# For CSV files
84	import csv
85
86	# Read from file path
87	with open('data.csv', 'r') as f:
88	reader = csv.DictReader(f)
89	data = list(reader)
90
91	# For JSON files
92	import json
93	with open('data.json', 'r') as f:
94	data = json.load(f)
95	```
96
97	Alternatively, use the supporting scripts:
98	```python
99	# Execute the helper script
100	("scripts/process.py")
101	```
102
103	### Step 3: Perform Operations
104
105	Apply the requested transformations or analyses:
106
107	```python
108	# Example: Filter and aggregate
109	filtered = [row for row in data if float(row['amount']) > 100]
110
111	# Example: Calculate statistics
112	from statistics import mean, median
113	amounts = [float(row['amount']) for row in data]
114	avg = mean(amounts)
115	med = median(amounts)
116	```
117
118	### Step 4: Generate Output
119
120	Format results according to user needs:
121
122	```python
123	# As markdown table
124	def to_markdown_table(data, columns=None):
125	if not data:
126	return "No data"
127
128	if columns is None:
129	columns = list(data[0].keys())
130
131	# Header
132	header = "\| " + " \| ".join(columns) + " \|"
133	separator = "\| " + " \| ".join(["---"] * len(columns)) + " \|"
134
135	# Rows
136	rows = []
137	for row in data:
138	row_str = "\| " + " \| ".join(str(row.get(col, "")) for col in columns) + " \|"
139	rows.append(row_str)
140
141	return "\n".join([header, separator] + rows)
142
143	print(to_markdown_table(filtered))
144	```
145
146	## Common Use Cases
147
148	### Use Case 1: CSV Analysis
149
150	```python
151	# Example: Analyze sales data
152	import csv
153	from io import StringIO
154	from statistics import mean, sum as total
155
156	# Load CSV
157	reader = csv.DictReader(StringIO(file_content))
158	data = list(reader)
159
160	# Calculate metrics
161	total_sales = sum(float(row['amount']) for row in data)
162	avg_sales = mean(float(row['amount']) for row in data)
163	unique_customers = len(set(row['customer_id'] for row in data))
164
165	print(f"Total Sales: ${total_sales:,.2f}")
166	print(f"Average Sale: ${avg_sales:,.2f}")
167	print(f"Unique Customers: {unique_customers}")
168	```
169
170	### Use Case 2: Data Filtering
171
172	```python
173	# Example: Filter records by criteria
174	filtered = [
175	row for row in data
176	if row['status'] == 'active' and float(row['score']) >= 80
177	]
178
179	print(f"Found {len(filtered)} matching records")
180	```
181
182	### Use Case 3: Data Grouping
183
184	```python
185	# Example: Group and aggregate
186	from collections import defaultdict
187
188	grouped = defaultdict(list)
189	for row in data:
190	grouped[row['category']].append(float(row['value']))
191
192	summary = {}
193	for category, values in grouped.items():
194	summary[category] = {
195	'count': len(values),
196	'total': sum(values),
197	'average': sum(values) / len(values)
198	}
199
200	for category, stats in summary.items():
201	print(f"{category}: {stats['count']} items, avg = {stats['average']:.2f}")
202	```
203
204	### Use Case 4: Format Conversion
205
206	```python
207	# Example: CSV to JSON
208	impor

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file-processing

88stars9forks

View on GitHub

How to install file-processing?

aws-samples/sample-strands-agents-agentskills/file-processing

$npx -y skills add aws-samples/sample-strands-agents-agentskills --skill file-processing

Installs into the current project.

Summary

Process and analyze CSV, JSON, and text files with data transformation, cleaning, analysis, and visualization capabilities

Files · 1

View on GitHub

SKILL.md

1	# File Processing Skill
2
3	## Purpose
4
5	Process structured data files (CSV, JSON, text) with comprehensive capabilities for data cleaning, transformation, analysis, and export. This skill enables working with data files without requiring users to write code.
6
7	## When to Use This Skill
8
9	Use this skill when you need to:
10	- Load and parse CSV or JSON files
11	- Clean and transform data
12	- Perform statistical analysis
13	- Filter, sort, or aggregate data
14	- Merge or join datasets
15	- Convert between formats (CSV ↔ JSON)
16	- Generate summary reports
17
18	## Capabilities
19
20	### 1. Data Loading
21
22	Supported formats:
23	- CSV files: Any delimiter (comma, tab, semicolon, etc.)
24	- JSON files: Single objects or arrays of objects
25	- Text files: Custom delimited formats
26
27	### 2. Data Cleaning
28
29	Available operations:
30	- Remove duplicate rows
31	- Handle missing values (drop, fill, interpolate)
32	- Normalize text (trim whitespace, standardize case)
33	- Convert data types
34	- Remove outliers
35	- Validate data against rules
36
37	### 3. Data Transformation
38
39	Available operations:
40	- Filter: Select rows based on conditions
41	- Select: Choose specific columns
42	- Sort: Order by one or more columns
43	- Group: Aggregate data by categories
44	- Pivot: Reshape data (wide ↔ long format)
45	- Merge: Combine multiple datasets
46	- Calculate: Add derived columns
47
48	### 4. Data Analysis
49
50	Available analyses:
51	- Descriptive statistics (mean, median, std, etc.)
52	- Frequency distributions
53	- Correlation analysis
54	- Trend detection
55	- Missing data analysis
56	- Data quality assessment
57
58	### 5. Export
59
60	Output formats:
61	- CSV files
62	- JSON files (objects or arrays)
63	- Markdown tables
64	- Summary reports
65
66	## Instructions for Execution
67
68	When this skill is activated, follow these steps:
69
70	### Step 1: Understand the Request
71
72	Ask clarifying questions if needed:
73	- What file(s) need to be processed?
74	- What specific analysis or transformation is required?
75	- What output format is desired?
76	- Are there any specific requirements or constraints?
77
78	### Step 2: Load the Data
79
80	Use `shell` to load and process data:
81
82	```python
83	# For CSV files
84	import csv
85
86	# Read from file path
87	with open('data.csv', 'r') as f:
88	reader = csv.DictReader(f)
89	data = list(reader)
90
91	# For JSON files
92	import json
93	with open('data.json', 'r') as f:
94	data = json.load(f)
95	```
96
97	Alternatively, use the supporting scripts:
98	```python
99	# Execute the helper script
100	("scripts/process.py")
101	```
102
103	### Step 3: Perform Operations
104
105	Apply the requested transformations or analyses:
106
107	```python
108	# Example: Filter and aggregate
109	filtered = [row for row in data if float(row['amount']) > 100]
110
111	# Example: Calculate statistics
112	from statistics import mean, median
113	amounts = [float(row['amount']) for row in data]
114	avg = mean(amounts)
115	med = median(amounts)
116	```
117
118	### Step 4: Generate Output
119
120	Format results according to user needs:
121
122	```python
123	# As markdown table
124	def to_markdown_table(data, columns=None):
125	if not data:
126	return "No data"
127
128	if columns is None:
129	columns = list(data[0].keys())
130
131	# Header
132	header = "\| " + " \| ".join(columns) + " \|"
133	separator = "\| " + " \| ".join(["---"] * len(columns)) + " \|"
134
135	# Rows
136	rows = []
137	for row in data:
138	row_str = "\| " + " \| ".join(str(row.get(col, "")) for col in columns) + " \|"
139	rows.append(row_str)
140
141	return "\n".join([header, separator] + rows)
142
143	print(to_markdown_table(filtered))
144	```
145
146	## Common Use Cases
147
148	### Use Case 1: CSV Analysis
149
150	```python
151	# Example: Analyze sales data
152	import csv
153	from io import StringIO
154	from statistics import mean, sum as total
155
156	# Load CSV
157	reader = csv.DictReader(StringIO(file_content))
158	data = list(reader)
159
160	# Calculate metrics
161	total_sales = sum(float(row['amount']) for row in data)
162	avg_sales = mean(float(row['amount']) for row in data)
163	unique_customers = len(set(row['customer_id'] for row in data))
164
165	print(f"Total Sales: ${total_sales:,.2f}")
166	print(f"Average Sale: ${avg_sales:,.2f}")
167	print(f"Unique Customers: {unique_customers}")
168	```
169
170	### Use Case 2: Data Filtering
171
172	```python
173	# Example: Filter records by criteria
174	filtered = [
175	row for row in data
176	if row['status'] == 'active' and float(row['score']) >= 80
177	]
178
179	print(f"Found {len(filtered)} matching records")
180	```
181
182	### Use Case 3: Data Grouping
183
184	```python
185	# Example: Group and aggregate
186	from collections import defaultdict
187
188	grouped = defaultdict(list)
189	for row in data:
190	grouped[row['category']].append(float(row['value']))
191
192	summary = {}
193	for category, values in grouped.items():
194	summary[category] = {
195	'count': len(values),
196	'total': sum(values),
197	'average': sum(values) / len(values)
198	}
199
200	for category, stats in summary.items():
201	print(f"{category}: {stats['count']} items, avg = {stats['average']:.2f}")
202	```
203
204	### Use Case 4: Format Conversion
205
206	```python
207	# Example: CSV to JSON
208	impor

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