Working effectively with JSON data structures.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionjson-data-handlingExecute the skills CLI command in your project's root directory to begin installation:
Fetches json-data-handling from bobmatnyc/claude-mpm-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate json-data-handling. Access via /json-data-handling in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Working effectively with JSON data structures.
import json
# Parse JSON string
data = json.loads('{"name": "John", "age": 30}')
# Convert to JSON string
json_str = json.dumps(data)
# Pretty print
json_str = json.dumps(data, indent=2)
# Read from file
with open('data.json', 'r') as f:
data = json.load(f)
# Write to file
with open('output.json', 'w') as f:
json.dump(data, f, indent=2)
# Custom encoder for datetime
from datetime import datetime
class DateTimeEncoder(json.JSONEncoder):
def default(self, obj):
if isinstance(obj, datetime):
return obj.isoformat()
return super().default(obj)
json_str = json.dumps({'date': datetime.now()}, cls=DateTimeEncoder)
# Handle None values
json.dumps(data, skipkeys=True)
# Sort keys
json.dumps(data, sort_keys=True)
// Parse JSON string
const data = JSON.parse('{"name": "John", "age": 30}');
// Convert to JSON string
const jsonStr = JSON.stringify(data);
// Pretty print
const jsonStr = JSON.stringify(data, null, 2);
// Read from file (Node.js)
const fs = require('fs');
const data = JSON.parse(fs.readFileSync('data.json', 'utf8'));
// Write to file
fs.writeFileSync('output.json', JSON.stringify(data, null, 2));
// Custom replacer
const jsonStr = JSON.stringify(data, (key, value) => {
if (typeof value === 'bigint') {
return value.toString();
}
return value;
});
// Filter properties
const filtered = JSON.stringify(data, ['name', 'age']);
// Handle circular references
const getCircularReplacer = () => {
const seen = new WeakSet();
return (key, value) => {
if (typeof value === 'object' && value !== null) {
if (seen.has(value)) return;
seen.add(value);
}
return value;
};
};
JSON.stringify(circularObj, getCircularReplacer());
from jsonschema import validate
schema = {
"type": "object",
"properties": {
"name": {"type": "string"},
"age": {"type": "number", "minimum": 0}
},
"required": ["name", "age"]
}
# Validate
validate(instance=data, schema=schema)
def deep_merge(dict1, dict2):
result = dict1.copy()
for key, value in dict2.items():
if key in result and isinstance(result[key], dict) and isinstance(value, dict):
result[key] = deep_merge(result[key], value)
else:
result[key] = value
return result
# Safe nested access
def get_nested(data, *keys, default=None):
for key in keys:
try:
data = data[key]
except (KeyError, TypeError, IndexError):
return default
return data
# Usage
value = get_nested(data, 'user', 'address', 'city', default='Unknown')
# Convert snake_case to camelCase
def to_camel_case(snake_str):
components = snake_str.split('_')Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
diffusionstudio/lottie
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
Registry listing for json-data-handling matched our evaluation — installs cleanly and behaves as described in the markdown.
Solid pick for teams standardizing on skills: json-data-handling is focused, and the summary matches what you get after install.
json-data-handling reduced setup friction for our internal harness; good balance of opinion and flexibility.
json-data-handling fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
I recommend json-data-handling for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
json-data-handling has been reliable in day-to-day use. Documentation quality is above average for community skills.
Keeps context tight: json-data-handling is the kind of skill you can hand to a new teammate without a long onboarding doc.
json-data-handling reduced setup friction for our internal harness; good balance of opinion and flexibility.
Keeps context tight: json-data-handling is the kind of skill you can hand to a new teammate without a long onboarding doc.
json-data-handling has been reliable in day-to-day use. Documentation quality is above average for community skills.
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