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Review JavaScript JSON parsing for malformed input and unexpected data structures. Add error handling and validate parsed data before use, helping prevent failures when reading API responses, browser storage, or user-provided content.

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• Reviewing JSON parsing in API response handling
• Handling malformed data from localStorage
• Validating data structures before accessing properties

Helps integrate YAML output into json-render, turning incoming text into incremental UI updates. Covers prompt generation, streaming parsing, and targeted edits to existing UI specifications through merge, patch, or diff formats.

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• Render UI updates as YAML arrives
• Generate prompts for YAML output
• Apply partial edits to existing UI specifications

Add URL handlers to a TypeScript server built with Node's native HTTP module. The skill follows an existing switch/case router, connects named asynchronous handlers, and covers JSON responses, error handling, and optional shared request body parsing.

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• Adding an API endpoint to a native Node HTTP server
• Connecting a route file to an existing switch/case router
• Sharing JSON request body parsing across routes

Extract text and its page coordinates locally from PDFs, Office documents, and images. Use OCR to read scans, export structured JSON or Markdown, process document folders, and render pages as PNG images for agents that analyze visual content.

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• Extracting text from scanned PDFs and images
• Preparing document text and coordinates for retrieval-based AI answers
• Batch-processing folders of research papers

Convert laboratory instrument files into Allotrope Simple Model (ASM) JSON, a standardized data format, or tabular CSV. Detect instrument types, validate converted data, and export Python parsing code for analysis workflows or handoff to data engineers.

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• Standardizing instrument data for a laboratory management system
• Converting ELISA results into an importable CSV
• Exporting Python code to parse NanoDrop files

Python JSON parsing best practices covering performance optimization (orjson/msgspec), handling large files (streaming/JSONL), security (injection prevention), and advanced querying (JSONPath/JMESPath).

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Find JavaScript that executes strings as code, including eval(), new Function(), and string-based timers. Review how it runs in the browser and suggest alternatives for parsing data, choosing operations, and scheduling functions to reduce code injection risks.

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• Finding eval() and other string execution
• Replacing eval() used to parse JSON
• Changing string-based timers to function references

High-performance C JSON parsing, creation, and modification with yyjson. Use when working with yyjson documents, string copy-vs-borrow ownership, dangling c_str() bugs, yyjson_alc allocators, or writer output freeing.

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Analyze a software bill of materials, an inventory of software components, against known vulnerability records. Trace indirect dependencies, assess risk, and produce reports that help teams prioritize fixes and review supplier software.

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• Assessing vulnerabilities in vendor-provided software inventories
• Checking whether a newly disclosed CVE affects deployed software
• Adding SBOM vulnerability checks to CI/CD

Analyze Cobalt Strike communication profiles and Beacon payload configurations to extract request paths, headers, timing, and process injection settings. Turn these indicators into a JSON report and network detection rules for Suricata or Snort.

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• Analyzing a captured Malleable C2 profile
• Extracting communication settings from a Beacon payload
• Creating Suricata or Snort detection rules

Build custom calculation and parsing tools that an n8n AI Agent can call. This skill covers JavaScript or Python inputs, structured arguments, string results, tool descriptions, and fixes for invocation and output errors.

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• Adding a calculation tool to an AI Agent
• Defining typed inputs for multiple arguments
• Fixing tool output type errors

Build TypeScript command-line tools for API operations, file conversion, and automation. The skill selects a suitable structure and provides implementation, configuration, usage documentation, JSON output, and error handling with appropriate exit codes.

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• Creating a command-line tool for an API
• Replacing a complex bash script
• Adding commands to an existing tool

Implement Swift Codable models for JSON and property-list encoding and decoding with JSONDecoder, JSONEncoder, CodingKeys, and custom init(from:) or encode(to:). Use when parsing API responses, remapping keys, flattening nested JSON, handling date or data decoding strategies, decoding heterogeneous arrays, or integrating Codable with URLSession, SwiftData, or UserDefaults.

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Implement Swift Codable models for JSON and property-list encoding and decoding with JSONDecoder, JSONEncoder, CodingKeys, and custom init(from:) or encode(to:). Use when parsing API responses, remapping keys, flattening nested JSON, handling date or data decoding strategies, decoding heterogeneous arrays, or integrating Codable with URLSession, SwiftData, or UserDefaults.

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Analyze Windows authentication logs for repeated password guesses against Remote Desktop services. Group failures by source IP and account, check for later successful logins, and produce a JSON report highlighting attack patterns and possible compromise.

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• Investigate repeated login failures on exposed RDP services
• Identify accounts targeted by password guessing
• Check for successful logins after repeated failures

Build workflows that extract structured data from LLM responses, check field types and values with Pydantic, and retry failed extractions. Includes examples for nested data, classification, and receiving results as they are generated.

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• Extracting contact and company details from text
• Classifying articles into predefined categories
• Checking review analysis against validation rules

Build workflows that extract structured data from LLM responses, check field types and values with Pydantic, and retry failed extractions. Includes examples for nested data, classification, and receiving results as they are generated.

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• Extracting contact and company details from text
• Classifying articles into predefined categories
• Checking review analysis against validation rules

Create dictionaries of keywords and identifying byte sequences for fuzzing, or testing with generated inputs. Extract useful entries from code, binaries, or specifications, then configure fuzzers to explore parsing paths beyond initial input validation.

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• Build a dictionary for JSON or XML parser fuzzing
• Add protocol keywords to generated test inputs
• Test image parsers with file signatures and chunk names

Write idiomatic application code with the ClickHouse Node.js client (`@clickhouse/client`). Use this skill whenever a user is *building* against the Node.js client - configuring the client, pinging, inserting rows in JSON or raw formats, selecting and parsing results, binding query parameters, managing sessions and temporary tables, working with data types or customizing JSON parsing. Do NOT use for browser/Web client code.

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Zod 4 — TypeScript-first schema validation with static type inference. Use when writing Zod schemas, validating data, defining types with Zod, parsing input, creating form validation schemas, defining API request/response schemas, working with z.object, z.string, z.number, z.enum, z.array, z.union, z.discriminatedUnion, z.file, z.jwt, z.email, z.uuid, z.url, z.codec, z.toJSONSchema, z.fromJSONSchema, z.int, z.stringbool, z.templateLiteral, z.record, z.partialRecord, or any other Zod API. Also use when migrating from Zod 3 to Zod 4, or when the user's package.json shows zod@^4. CRITICAL: Always use Zod 4 APIs. Never use deprecated Zod 3 patterns unless user explicitly requests Zod 3 compatibility.

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Processes JSON on the command line with jq. Use when parsing, filtering, or validating JSON from files or APIs.

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Send proofs, planning questions, or difficult debugging tasks to DeepSeek's reasoning model. Receive the final answer and model-provided reasoning text in separate fields, with token usage reported for reviewing responses and costs.

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• Requesting a mathematical proof
• Planning work that requires several reasoning steps
• Investigating root causes of difficult bugs

Set up and customize Pi, a terminal coding agent framework. This skill guides model configuration, extension development, terminal interfaces, and integration with other applications through its SDK, a software development kit, or process messaging.

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• Install Pi and configure model access
• Create custom tools and terminal interfaces
• Embed Pi in another application

Design and review API contract tests, which check agreed request and response behavior. Examine schemas, server output, and client parsing to identify compatibility problems, reproduce failures locally, and report gaps in test coverage.

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• Checking client impact before changing an API response
• Investigating client and server disagreements at runtime
• Reproducing compatibility failures with local test data