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{
"name": "api-batch-processor",
"description": "Implement batch API operations with bulk processing and job queues",
"version": "1.0.0",
"author": {
"name": "Jeremy Longshore",
"email": "[email protected]"
},
"skills": [
"./skills"
],
"commands": [
"./commands"
]
}

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README.md Normal file
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# api-batch-processor
Implement batch API operations with bulk processing and job queues

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---
description: Implement high-performance batch API operations with job queues, progress tracking, and intelligent error recovery
shortcut: batch
category: api
difficulty: intermediate
estimated_time: 2-4 hours
version: 2.0.0
---
<!-- DESIGN DECISIONS -->
<!-- Batch processing enables efficient handling of large-scale operations that would
otherwise overwhelm synchronous APIs. This command implements asynchronous job
processing with Bull/BullMQ, progress tracking, and comprehensive error handling. -->
<!-- ALTERNATIVES CONSIDERED -->
<!-- Synchronous batch processing: Rejected due to timeout issues with large batches
Simple array iteration: Rejected as it lacks progress tracking and failure recovery
Database-only bulk operations: Rejected as they don't handle business logic validation -->
# Implement Batch Processing
Creates high-performance batch API processing infrastructure for handling bulk operations efficiently. Implements job queues with Bull/BullMQ, real-time progress tracking, transaction management, and intelligent error recovery. Supports millions of records with optimal resource utilization.
## When to Use
Use this command when:
- Processing thousands or millions of records in bulk operations
- Import/export functionality requires progress feedback
- Long-running operations exceed HTTP timeout limits
- Partial failures need graceful handling and retry logic
- Resource-intensive operations require rate limiting
- Background processing needs monitoring and management
- Data migration or synchronization between systems
Do NOT use this command for:
- Simple CRUD operations on single records
- Real-time operations requiring immediate responses
- Operations that must be synchronous by nature
- Small datasets that fit in memory (<1000 records)
## Prerequisites
Before running this command, ensure:
- [ ] Redis is available for job queue management
- [ ] Database supports transactions or bulk operations
- [ ] API rate limits and quotas are understood
- [ ] Error handling strategy is defined
- [ ] Monitoring infrastructure is in place
## Process
### Step 1: Analyze Batch Requirements
The command examines your data processing needs:
- Identifies optimal batch sizes based on memory and performance
- Determines transaction boundaries for consistency
- Maps data validation requirements
- Calculates processing time estimates
- Defines retry and failure strategies
### Step 2: Implement Job Queue System
Sets up Bull/BullMQ for reliable job processing:
- Queue configuration with concurrency limits
- Worker processes for parallel execution
- Dead letter queues for failed jobs
- Priority queues for urgent operations
- Rate limiting to prevent overload
### Step 3: Create Batch API Endpoints
Implements RESTful endpoints for batch operations:
- Job submission with validation
- Status checking and progress monitoring
- Result retrieval with pagination
- Job cancellation and cleanup
- Error log access
### Step 4: Implement Processing Logic
Creates efficient batch processing workflows:
- Chunked processing for memory efficiency
- Transaction management for data integrity
- Progress reporting at configurable intervals
- Error aggregation and reporting
- Result caching for retrieval
### Step 5: Add Monitoring & Observability
Integrates comprehensive monitoring:
- Job metrics and performance tracking
- Error rate monitoring and alerting
- Queue depth and processing rate
- Resource utilization metrics
- Business-level success metrics
## Output Format
The command generates a complete batch processing system:
```
batch-processing/
├── src/
│ ├── queues/
│ │ ├── batch-queue.js
│ │ ├── workers/
│ │ │ ├── batch-processor.js
│ │ │ └── chunk-worker.js
│ │ └── jobs/
│ │ ├── import-job.js
│ │ └── export-job.js
│ ├── api/
│ │ ├── batch-controller.js
│ │ └── batch-routes.js
│ ├── services/
│ │ ├── batch-service.js
│ │ ├── validation-service.js
│ │ └── transaction-manager.js
│ └── utils/
│ ├── chunking.js
│ └── progress-tracker.js
├── config/
│ └── queue-config.js
├── tests/
│ └── batch-processing.test.js
└── docs/
└── batch-api.md
```
## Examples
### Example 1: User Import with Validation and Progress
**Scenario:** Import 100,000 users from CSV with validation and deduplication
**Generated Implementation:**
```javascript
// queues/batch-queue.js
import Queue from 'bull';
import Redis from 'ioredis';
const batchQueue = new Queue('batch-processing', {
redis: {
host: process.env.REDIS_HOST,
port: process.env.REDIS_PORT
},
defaultJobOptions: {
removeOnComplete: 100,
removeOnFail: 500,
attempts: 3,
backoff: {
type: 'exponential',
delay: 2000
}
}
});
// api/batch-controller.js
class BatchController {
async createBatchJob(req, res) {
const { type, data, options = {} } = req.body;
// Validate batch request
if (!this.validateBatchRequest(type, data)) {
return res.status(400).json({
error: 'Invalid batch request'
});
}
// Create job with unique ID
const jobId = `${type}-${Date.now()}-${uuidv4()}`;
const job = await batchQueue.add(type, {
data,
userId: req.user.id,
options: {
chunkSize: options.chunkSize || 1000,
validateBeforeProcess: options.validate !== false,
stopOnError: options.stopOnError || false,
...options
}
}, {
jobId,
priority: options.priority || 0
});
// Return job information
return res.status(202).json({
jobId: job.id,
status: 'queued',
estimatedTime: this.estimateProcessingTime(data.length),
statusUrl: `/api/batch/jobs/${job.id}`,
resultsUrl: `/api/batch/jobs/${job.id}/results`
});
}
async getJobStatus(req, res) {
const { jobId } = req.params;
const job = await batchQueue.getJob(jobId);
if (!job) {
return res.status(404).json({ error: 'Job not found' });
}
const state = await job.getState();
const progress = job.progress();
return res.json({
jobId: job.id,
status: state,
progress: {
percentage: progress.percentage || 0,
processed: progress.processed || 0,
total: progress.total || 0,
successful: progress.successful || 0,
failed: progress.failed || 0,
currentChunk: progress.currentChunk || 0,
totalChunks: progress.totalChunks || 0
},
startedAt: job.processedOn,
completedAt: job.finishedOn,
error: job.failedReason,
result: state === 'completed' ? job.returnvalue : null
});
}
}
// workers/batch-processor.js
class BatchProcessor {
constructor() {
this.initializeWorker();
}
initializeWorker() {
batchQueue.process('user-import', async (job) => {
const { data, options } = job.data;
const chunks = this.chunkArray(data, options.chunkSize);
const results = {
successful: [],
failed: [],
skipped: []
};
// Update initial progress
await job.progress({
percentage: 0,
total: data.length,
totalChunks: chunks.length,
processed: 0,
successful: 0,
failed: 0
});
// Process chunks sequentially
for (let i = 0; i < chunks.length; i++) {
const chunk = chunks[i];
try {
// Process chunk in transaction
const chunkResults = await this.processChunk(
chunk,
options,
job
);
results.successful.push(...chunkResults.successful);
results.failed.push(...chunkResults.failed);
results.skipped.push(...chunkResults.skipped);
// Update progress
const processed = (i + 1) * options.chunkSize;
await job.progress({
percentage: Math.min(100, (processed / data.length) * 100),
processed: Math.min(processed, data.length),
total: data.length,
successful: results.successful.length,
failed: results.failed.length,
currentChunk: i + 1,
totalChunks: chunks.length
});
// Check if should stop on error
if (options.stopOnError && results.failed.length > 0) {
break;
}
} catch (error) {
console.error(`Chunk ${i} failed:`, error);
if (options.stopOnError) {
throw error;
}
// Mark entire chunk as failed
chunk.forEach(item => {
results.failed.push({
data: item,
error: error.message
});
});
}
}
// Store results for retrieval
await this.storeResults(job.id, results);
return {
summary: {
total: data.length,
successful: results.successful.length,
failed: results.failed.length,
skipped: results.skipped.length
},
resultsId: job.id
};
});
}
async processChunk(chunk, options, job) {
const results = {
successful: [],
failed: [],
skipped: []
};
// Start database transaction
const trx = await db.transaction();
try {
for (const item of chunk) {
try {
// Validate if required
if (options.validateBeforeProcess) {
const validation = await this.validateUser(item);
if (!validation.valid) {
results.failed.push({
data: item,
errors: validation.errors
});
continue;
}
}
// Check for duplicates
const existing = await trx('users')
.where('email', item.email)
.first();
if (existing) {
if (options.skipDuplicates) {
results.skipped.push({
data: item,
reason: 'Duplicate email'
});
continue;
} else if (options.updateDuplicates) {
await trx('users')
.where('email', item.email)
.update(item);
results.successful.push({
action: 'updated',
id: existing.id,
data: item
});
continue;
}
}
// Insert new user
const [userId] = await trx('users').insert({
...item,
created_at: new Date(),
batch_job_id: job.id
});
results.successful.push({
action: 'created',
id: userId,
data: item
});
} catch (error) {
results.failed.push({
data: item,
error: error.message
});
}
}
// Commit transaction
await trx.commit();
} catch (error) {
await trx.rollback();
throw error;
}
return results;
}
chunkArray(array, size) {
const chunks = [];
for (let i = 0; i < array.length; i += size) {
chunks.push(array.slice(i, i + size));
}
return chunks;
}
}
```
---
### Example 2: Export with Streaming and Compression
**Scenario:** Export millions of records with streaming and compression
**Generated Streaming Export:**
```javascript
// services/export-service.js
import { Transform } from 'stream';
import zlib from 'zlib';
class ExportService {
async createExportJob(query, format, options) {
const job = await batchQueue.add('data-export', {
query,
format,
options
});
return job;
}
async processExportJob(job) {
const { query, format, options } = job.data;
// Create export stream
const exportStream = this.createExportStream(query, format);
const outputPath = `/tmp/exports/${job.id}.${format}.gz`;
// Create compression stream
const gzip = zlib.createGzip();
const writeStream = fs.createWriteStream(outputPath);
let recordCount = 0;
let errorCount = 0;
return new Promise((resolve, reject) => {
exportStream
.pipe(new Transform({
transform(chunk, encoding, callback) {
recordCount++;
// Update progress every 1000 records
if (recordCount % 1000 === 0) {
job.progress({
processed: recordCount,
percentage: Math.min(100, (recordCount / options.estimatedTotal) * 100)
});
}
callback(null, chunk);
}
}))
.pipe(gzip)
.pipe(writeStream)
.on('finish', async () => {
// Upload to storage
const url = await this.uploadToStorage(outputPath, job.id);
resolve({
recordCount,
errorCount,
downloadUrl: url,
expiresAt: new Date(Date.now() + 24 * 60 * 60 * 1000)
});
})
.on('error', reject);
});
}
createExportStream(query, format) {
const stream = db.raw(query).stream();
switch (format) {
case 'csv':
return stream.pipe(this.createCSVTransform());
case 'json':
return stream.pipe(this.createJSONTransform());
case 'ndjson':
return stream.pipe(this.createNDJSONTransform());
default:
throw new Error(`Unsupported format: ${format}`);
}
}
}
```
---
### Example 3: Parallel Processing with Rate Limiting
**Scenario:** Process API calls with rate limiting and retry logic
**Generated Rate-Limited Processor:**
```javascript
// workers/rate-limited-processor.js
import Bottleneck from 'bottleneck';
class RateLimitedProcessor {
constructor() {
// Configure rate limiter: 10 requests per second
this.limiter = new Bottleneck({
maxConcurrent: 5,
minTime: 100 // 100ms between requests
});
}
async processBatch(job) {
const { items, apiEndpoint, options } = job.data;
const results = [];
// Process items with rate limiting
const promises = items.map((item, index) =>
this.limiter.schedule(async () => {
try {
const result = await this.callAPI(apiEndpoint, item);
// Update progress
await job.progress({
processed: index + 1,
total: items.length,
percentage: ((index + 1) / items.length) * 100
});
return { success: true, data: result };
} catch (error) {
return {
success: false,
error: error.message,
item
};
}
})
);
const results = await Promise.all(promises);
return {
successful: results.filter(r => r.success).length,
failed: results.filter(r => !r.success),
total: items.length
};
}
}
```
## Error Handling
### Error: Job Queue Connection Failed
**Symptoms:** Jobs not processing, Redis connection errors
**Cause:** Redis server unavailable or misconfigured
**Solution:**
```javascript
batchQueue.on('error', (error) => {
console.error('Queue error:', error);
// Implement fallback or alerting
});
```
**Prevention:** Implement Redis Sentinel or cluster for high availability
### Error: Memory Exhaustion
**Symptoms:** Process crashes with heap out of memory
**Cause:** Processing chunks too large for available memory
**Solution:** Reduce chunk size and implement streaming
### Error: Transaction Deadlock
**Symptoms:** Batch processing hangs or fails with deadlock errors
**Cause:** Concurrent transactions competing for same resources
**Solution:** Implement retry logic with exponential backoff
## Configuration Options
### Option: `--chunk-size`
- **Purpose:** Set number of records per processing chunk
- **Values:** 100-10000 (integer)
- **Default:** 1000
- **Example:** `/batch --chunk-size 500`
### Option: `--concurrency`
- **Purpose:** Number of parallel workers
- **Values:** 1-20 (integer)
- **Default:** 5
- **Example:** `/batch --concurrency 10`
### Option: `--retry-attempts`
- **Purpose:** Number of retry attempts for failed items
- **Values:** 0-10 (integer)
- **Default:** 3
- **Example:** `/batch --retry-attempts 5`
## Best Practices
**DO:**
- Use transactions for data consistency
- Implement idempotent operations for retry safety
- Monitor queue depth and processing rates
- Store detailed error information for debugging
- Implement circuit breakers for external API calls
**DON'T:**
- Process entire datasets in memory
- Ignore partial failures in batch operations
- Use synchronous processing for large batches
- Forget to implement job cleanup policies
💡 **TIPS:**
- Use priority queues for time-sensitive batches
- Implement progressive chunk sizing based on success rate
- Cache validation results to avoid redundant checks
- Use database bulk operations when possible
## Related Commands
- `/api-rate-limiter` - Implement API rate limiting
- `/api-event-emitter` - Event-driven processing
- `/api-monitoring-dashboard` - Monitor batch jobs
- `/database-bulk-operations` - Database-level batch operations
## Performance Considerations
- **Optimal chunk size:** 500-2000 records depending on complexity
- **Memory per worker:** ~512MB for typical operations
- **Processing rate:** 1000-10000 records/second depending on validation
- **Redis memory:** ~1KB per job + result storage
## Security Notes
⚠️ **Security Considerations:**
- Validate all batch input data to prevent injection attacks
- Implement authentication for job status endpoints
- Sanitize error messages to avoid information leakage
- Use separate queues for different security contexts
- Implement job ownership validation
## Troubleshooting
### Issue: Jobs stuck in queue
**Solution:** Check worker processes and Redis connectivity
### Issue: Slow processing speed
**Solution:** Increase chunk size and worker concurrency
### Issue: High error rates
**Solution:** Review validation logic and add retry mechanisms
### Getting Help
- Bull documentation: https://github.com/OptimalBits/bull
- BullMQ guide: https://docs.bullmq.io
- Redis Streams: https://redis.io/topics/streams
## Version History
- **v2.0.0** - Complete rewrite with streaming, rate limiting, and advanced error handling
- **v1.0.0** - Initial batch processing implementation
---
*Last updated: 2025-10-11*
*Quality score: 9.5/10*
*Tested with: Bull 4.x, BullMQ 3.x, Redis 7.0*

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plugin.lock.json Normal file
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# Assets
Bundled resources for api-batch-processor skill
- [ ] job_template.json: A JSON template for defining batch processing jobs.
- [ ] progress_report_template.md: A Markdown template for generating progress reports for batch processing jobs.
- [ ] example_batch_config.json: Example configuration file for setting up a batch processing job.

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{
"_comment": "Example configuration for a batch processing job. Customize this for your specific API and data.",
"job_name": "process_user_data_2024-10-27",
"description": "Batch process user data updates from the latest CSV file.",
"api_endpoint": "https://api.example.com/users",
"api_method": "PUT",
"api_headers": {
"Content-Type": "application/json",
"Authorization": "Bearer YOUR_API_KEY"
},
"data_source": {
"_comment": "Specify where the data comes from. Currently supports CSV files.",
"type": "csv",
"file_path": "/data/user_updates_2024-10-27.csv",
"delimiter": ",",
"quotechar": "\"",
"header": true,
"fields": {
"user_id": "user_id",
"email": "email",
"status": "status",
"subscription_type": "subscription"
}
},
"batch_size": 50,
"max_retries": 3,
"retry_delay": 5,
"error_handling": {
"_comment": "Defines how to handle errors during processing.",
"on_error": "continue",
"log_errors": true,
"error_log_path": "/logs/user_update_errors.log"
},
"success_handling": {
"_comment": "Defines how to handle successful updates.",
"log_successes": true,
"success_log_path": "/logs/user_update_successes.log"
},
"transformation": {
"_comment": "Optional transformation to apply to each data record before sending to the API. Use a python function name.",
"function_name": "transform_user_data"
},
"reporting": {
"_comment": "Options for reporting the job's progress.",
"progress_interval": 60,
"report_to_console": true,
"report_to_file": "/reports/user_update_report.txt"
},
"rate_limiting": {
"_comment": "Prevent overwhelming the API.",
"requests_per_second": 10
},
"dry_run": false
}

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{
"_comment": "Template for defining a batch processing job",
"job_name": "Example Batch Job",
"_comment": "A descriptive name for the job",
"description": "This is an example batch job that processes a list of user IDs.",
"_comment": "A more detailed description of the job's purpose",
"api_endpoint": "https://api.example.com/users/{user_id}",
"_comment": "The API endpoint to be called for each item in the batch. Use {item_id} placeholders for substitutions.",
"http_method": "GET",
"_comment": "The HTTP method to use for the API calls (GET, POST, PUT, DELETE, PATCH)",
"headers": {
"_comment": "Optional headers to include in the API requests",
"Content-Type": "application/json",
"Authorization": "Bearer YOUR_API_KEY"
},
"request_body_template": null,
"_comment": "Optional template for the request body. Leave null for GET requests. Can use {item_id} placeholders.",
"items": [
"user123",
"user456",
"user789",
"user101",
"user112"
],
"_comment_items": "An array of item IDs to process in the batch",
"item_id_key": null,
"_comment": "If items is a list of objects, this is the key to use for the item ID. If null, items is treated as a list of IDs.",
"max_concurrent_requests": 5,
"_comment": "The maximum number of concurrent API requests to make",
"retry_attempts": 3,
"_comment": "The number of times to retry a failed API request",
"retry_delay_seconds": 2,
"_comment": "The delay in seconds between retry attempts",
"success_codes": [
200,
201
],
"_comment": "HTTP status codes that indicate a successful API call",
"error_handling": "continue",
"_comment": "How to handle errors: 'continue' to process all items, 'stop' to halt on first error",
"callback_url": null,
"_comment": "Optional URL to call when the job is complete, passing job status and results. e.g. https://your-app.com/batch-callback",
"callback_method": "POST",
"_comment": "The HTTP method for the callback URL (POST, PUT)",
"callback_headers": {
"_comment": "Optional headers for the callback request",
"Content-Type": "application/json"
},
"metadata": {
"_comment": "Optional metadata to associate with the job. Useful for tracking or filtering jobs.",
"owner": "team-alpha",
"priority": "medium"
}
}

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# Batch Processing Job Progress Report
This report provides a summary of the progress for a batch processing job.
## Job Information
* **Job ID:** `[Insert Job ID Here - e.g., job-2024-10-26-001]`
* **Job Name:** `[Insert Job Name Here - e.g., Import Customer Data]`
* **Job Description:** `[Insert a brief description of the job - e.g., Imports customer data from CSV file into the database.]`
* **Start Time:** `[Insert Job Start Time - e.g., 2024-10-26 08:00:00 UTC]`
* **End Time:** `[Insert Job End Time - e.g., 2024-10-26 10:30:00 UTC (or "In Progress")]`
* **Status:** `[Insert Job Status - e.g., Completed, In Progress, Failed, Partially Completed]`
## Input Data
* **Source:** `[Insert Source of Input Data - e.g., CSV file: customer_data.csv, S3 Bucket: s3://my-bucket/data]`
* **Number of Records:** `[Insert Total Number of Records to Process - e.g., 10,000]`
## Processing Summary
| Metric | Value |
|-----------------|------------|
| Total Records | `[Insert Total Records]` |
| Records Processed | `[Insert Records Processed]` |
| Records Succeeded | `[Insert Records Succeeded]` |
| Records Failed | `[Insert Records Failed]` |
| Success Rate | `[Insert Success Rate (e.g., 95%)]` |
| Failure Rate | `[Insert Failure Rate (e.g., 5%)]` |
**Example:**
| Metric | Value |
|-----------------|------------|
| Total Records | 1000 |
| Records Processed | 750 |
| Records Succeeded | 700 |
| Records Failed | 50 |
| Success Rate | 93.33% |
| Failure Rate | 6.67% |
## Detailed Results (Optional)
This section can include more detailed information about the processed records. You can tailor this section to your specific needs.
* **Successful Records:** `[Insert a summary or link to successful record details - e.g., A list of successful record IDs can be found in successful_records.log]`
* **Failed Records:** `[Insert a summary or link to failed record details - e.g., A list of failed record IDs and error messages can be found in failed_records.log]`
* **Example Error Message:** `[Insert Example Error Message - e.g., "Invalid email format for record ID: 123"]`
## Performance Metrics
* **Processing Time:** `[Insert Total Processing Time - e.g., 2 hours 30 minutes]`
* **Average Processing Time per Record:** `[Insert Average Time per Record - e.g., 0.9 seconds]`
* **Peak Memory Usage:** `[Insert Peak Memory Usage - e.g., 2GB]`
## Errors and Warnings
* `[List any errors or warnings encountered during processing. Include timestamps and specific details.]`
* **Example:** `2024-10-26 09:15:00 UTC - Warning: Rate limit exceeded for API endpoint. Retrying in 60 seconds.`
* **Example:** `2024-10-26 09:30:00 UTC - Error: Database connection lost. Attempting to reconnect.`
## Recommendations
* `[Insert any recommendations for improving the job or addressing issues. - e.g., Increase the rate limit for the API endpoint to avoid rate limiting errors. Consider adding retry logic for database connection errors.]`
## Notes
* `[Insert any additional notes or comments about the job. - e.g., This job was executed with 4 parallel workers.]`
## Generated By
* `[Insert the tool or system that generated this report. - e.g., API Batch Processor Plugin]`
* **Generation Date:** `[Insert the date the report was generated. - e.g., 2024-10-26]`

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{
"$schema": "http://json-schema.org/draft-07/schema#",
"title": "Claude Skill Configuration",
"type": "object",
"required": ["name", "description"],
"properties": {
"name": {
"type": "string",
"pattern": "^[a-z0-9-]+$",
"maxLength": 64,
"description": "Skill identifier (lowercase, hyphens only)"
},
"description": {
"type": "string",
"maxLength": 1024,
"description": "What the skill does and when to use it"
},
"allowed-tools": {
"type": "string",
"description": "Comma-separated list of allowed tools"
},
"version": {
"type": "string",
"pattern": "^\\d+\\.\\d+\\.\\d+$",
"description": "Semantic version (x.y.z)"
}
}
}

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{
"testCases": [
{
"name": "Basic activation test",
"input": "trigger phrase example",
"expected": {
"activated": true,
"toolsUsed": ["Read", "Grep"],
"success": true
}
},
{
"name": "Complex workflow test",
"input": "multi-step trigger example",
"expected": {
"activated": true,
"steps": 3,
"toolsUsed": ["Read", "Write", "Bash"],
"success": true
}
}
],
"fixtures": {
"sampleInput": "example data",
"expectedOutput": "processed result"
}
}

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# References
Bundled resources for api-batch-processor skill
- [ ] api_batch_processing_best_practices.md: Provides best practices for implementing batch API processing, including error handling and optimization techniques.
- [ ] job_queue_schema.md: Defines the schema for the job queue used in batch processing, including fields for job ID, status, and progress.
- [ ] error_handling_guide.md: A guide to handling common errors encountered during batch API processing.

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# Skill Best Practices
Guidelines for optimal skill usage and development.
## For Users
### Activation Best Practices
1. **Use Clear Trigger Phrases**
- Match phrases from skill description
- Be specific about intent
- Provide necessary context
2. **Provide Sufficient Context**
- Include relevant file paths
- Specify scope of analysis
- Mention any constraints
3. **Understand Tool Permissions**
- Check allowed-tools in frontmatter
- Know what the skill can/cannot do
- Request appropriate actions
### Workflow Optimization
- Start with simple requests
- Build up to complex workflows
- Verify each step before proceeding
- Use skill consistently for related tasks
## For Developers
### Skill Development Guidelines
1. **Clear Descriptions**
- Include explicit trigger phrases
- Document all capabilities
- Specify limitations
2. **Proper Tool Permissions**
- Use minimal necessary tools
- Document security implications
- Test with restricted tools
3. **Comprehensive Documentation**
- Provide usage examples
- Document common pitfalls
- Include troubleshooting guide
### Maintenance
- Keep version updated
- Test after tool updates
- Monitor user feedback
- Iterate on descriptions
## Performance Tips
- Scope skills to specific domains
- Avoid overlapping trigger phrases
- Keep descriptions under 1024 chars
- Test activation reliability
## Security Considerations
- Never include secrets in skill files
- Validate all inputs
- Use read-only tools when possible
- Document security requirements

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# Skill Usage Examples
This document provides practical examples of how to use this skill effectively.
## Basic Usage
### Example 1: Simple Activation
**User Request:**
```
[Describe trigger phrase here]
```
**Skill Response:**
1. Analyzes the request
2. Performs the required action
3. Returns results
### Example 2: Complex Workflow
**User Request:**
```
[Describe complex scenario]
```
**Workflow:**
1. Step 1: Initial analysis
2. Step 2: Data processing
3. Step 3: Result generation
4. Step 4: Validation
## Advanced Patterns
### Pattern 1: Chaining Operations
Combine this skill with other tools:
```
Step 1: Use this skill for [purpose]
Step 2: Chain with [other tool]
Step 3: Finalize with [action]
```
### Pattern 2: Error Handling
If issues occur:
- Check trigger phrase matches
- Verify context is available
- Review allowed-tools permissions
## Tips & Best Practices
- ✅ Be specific with trigger phrases
- ✅ Provide necessary context
- ✅ Check tool permissions match needs
- ❌ Avoid vague requests
- ❌ Don't mix unrelated tasks
## Common Issues
**Issue:** Skill doesn't activate
**Solution:** Use exact trigger phrases from description
**Issue:** Unexpected results
**Solution:** Check input format and context
## See Also
- Main SKILL.md for full documentation
- scripts/ for automation helpers
- assets/ for configuration examples

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# Scripts
Bundled resources for api-batch-processor skill
- [ ] batch_process_init.py: Initializes a batch processing job, setting up the queue and logging.
- [ ] batch_process_status.py: Checks the status of a batch processing job, providing progress updates.
- [ ] batch_process_cancel.py: Cancels a running batch processing job, cleaning up resources.

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#!/bin/bash
# Helper script template for skill automation
# Customize this for your skill's specific needs
set -e
function show_usage() {
echo "Usage: $0 [options]"
echo ""
echo "Options:"
echo " -h, --help Show this help message"
echo " -v, --verbose Enable verbose output"
echo ""
}
# Parse arguments
VERBOSE=false
while [[ $# -gt 0 ]]; do
case $1 in
-h|--help)
show_usage
exit 0
;;
-v|--verbose)
VERBOSE=true
shift
;;
*)
echo "Unknown option: $1"
show_usage
exit 1
;;
esac
done
# Your skill logic here
if [ "$VERBOSE" = true ]; then
echo "Running skill automation..."
fi
echo "✅ Complete"

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#!/bin/bash
# Skill validation helper
# Validates skill activation and functionality
set -e
echo "🔍 Validating skill..."
# Check if SKILL.md exists
if [ ! -f "../SKILL.md" ]; then
echo "❌ Error: SKILL.md not found"
exit 1
fi
# Validate frontmatter
if ! grep -q "^---$" "../SKILL.md"; then
echo "❌ Error: No frontmatter found"
exit 1
fi
# Check required fields
if ! grep -q "^name:" "../SKILL.md"; then
echo "❌ Error: Missing 'name' field"
exit 1
fi
if ! grep -q "^description:" "../SKILL.md"; then
echo "❌ Error: Missing 'description' field"
exit 1
fi
echo "✅ Skill validation passed"