Results for “inputs”

Generate randomized and edge-case inputs to detect unexpected failures, bugs, and security vulnerabilities through fuzz testing. Use when creating test cases for robustness testing, generating adversarial inputs, testing error handling, finding edge cases, or security testing. Produces Python test code with fuzzing inputs for strings, numbers, and structured data focusing on edge cases, invalid inputs, and random valid inputs. Triggers when users ask to generate fuzz tests, create randomized test inputs, test edge cases, find bugs through fuzzing, or generate adversarial test cases.

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Diagnose unnecessary rebuilds in MSBuild by examining build logs, file timestamps, and declared inputs and outputs. Identify why work repeats, guide fixes when requested, and verify that a second unchanged build skips the expected targets.

• Investigating rebuilds when no files changed
• Reviewing inputs and outputs on custom targets
• Checking generated files and cleanup behavior

Pre-pipeline aggregator that scans AI agent cache directories (.claude, .cursor, .antigravity, .openclaw) or any user-specified directory for experimentation logs, extracts insights and numeric results, and formats them as PaperOrchestra-ready inputs (idea.md + experimental_log.md). TRIGGER when the user says "aggregate my agent logs for paper writing", "extract experiments from my coding agent history", "prepare PaperOrchestra inputs from my cache", "turn my agent logs into a paper", mentions a folder or directory they want to use as the basis for a paper, or wants to run PaperOrchestra but only has scattered agent experiment histories rather than structured inputs. Run this BEFORE paper-orchestra. Also called automatically by paper-orchestra when workspace/inputs/idea.md or workspace/inputs/experimental_log.md are missing.

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Pre-pipeline aggregator that scans AI agent cache directories (.claude, .cursor, .antigravity, .openclaw) or any user-specified directory for experimentation logs, extracts insights and numeric results, and formats them as PaperOrchestra-ready inputs (idea.md + experimental_log.md). TRIGGER when the user says "aggregate my agent logs for paper writing", "extract experiments from my coding agent history", "prepare PaperOrchestra inputs from my cache", "turn my agent logs into a paper", mentions a folder or directory they want to use as the basis for a paper, or wants to run PaperOrchestra but only has scattered agent experiment histories rather than structured inputs. Run this BEFORE paper-orchestra. Also called automatically by paper-orchestra when workspace/inputs/idea.md or workspace/inputs/experimental_log.md are missing.

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How to design contextual inputs for an always-on AI agent with episodic memory. Covers what data to feed the agent, how to structure observations and triggers, ambient context capture (screen, audio, calendar), context window budgeting, and retrieval strategies that keep the agent grounded in what's actually happening. Activate on: "what should the agent observe", "context inputs for agent", "ambient context capture", "agent triggers", "agent input design", "screenpipe integration", "context window budget", "what data to feed my agent", "/always-on-agent-inputs". NOT for: memory architecture and storage (use always-on-agent-architecture), application ideas (use always-on-agent-applications), safety concerns (use always-on-agent-safety).

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How to design contextual inputs for an always-on AI agent with episodic memory. Covers what data to feed the agent, how to structure observations and triggers, ambient context capture (screen, audio, calendar), context window budgeting, and retrieval strategies that keep the agent grounded in what's actually happening. Activate on: "what should the agent observe", "context inputs for agent", "ambient context capture", "agent triggers", "agent input design", "screenpipe integration", "context window budget", "what data to feed my agent", "/always-on-agent-inputs". NOT for: memory architecture and storage (use always-on-agent-architecture), application ideas (use always-on-agent-applications), safety concerns (use always-on-agent-safety).

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Strengthen existing tests with fast-check by checking properties across generated inputs. Find bugs that example tests miss and reduce failing inputs to small, reproducible cases. Property testing is available; the other listed workflows remain planned.

• Checking parser behavior across generated inputs
• Testing serialization and deserialization round trips
• Finding pure functions covered only by example tests

List the Unity-MCP tools available in the connected Unity Editor. Search names, descriptions, and input details with a regular expression, a text matching pattern, and choose whether the results include descriptions and input information.

• Checking available Unity-MCP tools
• Finding tools by name, description, or input details
• Reviewing input names and descriptions before calling a tool

Review form fields for code that blocks pasting, then remove those restrictions. The skill helps check password manager use, keyboard interactions, and accessibility so people can paste passwords and other long values into inputs.

• Checking why passwords cannot be pasted
• Reviewing credit card fields for paste restrictions
• Improving form accessibility

Generate an Excel workbook file in an agent session without an open Office app. Use Python and openpyxl to organize inputs, formulas, and checks, then save the .xlsx file and return its relative path.

• Creating an Excel file without opening Office
• Keeping model inputs and calculations on separate sheets
• Adding a sheet for financial consistency checks

Check training data and machine learning models for malicious changes, including backdoors that cause unexpected behavior on specific inputs. Combine data quality checks, model analysis, and file integrity verification, then document findings and retest after remediation.

• Screening third-party data before training
• Checking downloaded model weights before deployment
• Investigating unexpected behavior on specific inputs

Build reusable parts for n8n workflows instead of copying shared logic. This skill guides typed inputs, consistent outputs, naming, and execution settings so workflows and AI agents can call the same processing steps.

• Sharing common logic across workflows
• Splitting a large workflow into reusable parts
• Exposing shared processing as an AI agent tool

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.

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

Build and publish a browser demo for a supplied LoRA, a model adaptation for image or video tasks. Tailor the Gradio interface to its inputs and follow the model’s recommended settings on Hugging Face Spaces.

• Share a trained LoRA through a browser demo
• Build an interface tailored to image editing or video generation
• Apply recommended trigger words and generation settings

Validate Claude Ads data contracts, scoring inputs, run outputs, installation procedures, and release readiness. Return machine-readable pass or fail results with evidence, the most urgent blocker, and recovery steps, while checking source freshness and safe removal boundaries.

• Checking Claude Ads run outputs and scoring inputs
• Auditing evidence and checks before a release
• Verifying installation instructions and archive checksums

Build a new agent skill from a structured template. Copy the directory and edit SKILL.md to document its purpose, inputs, outputs, processing steps, precautions, and examples, replacing placeholders with concrete content.

• Draft the documentation for a new agent skill
• Define a skill's inputs and output format
• Organize processing steps and precautions

Helps set up and run cargo-fuzz for Rust projects using Cargo. Guides fuzz testing, which tries varied inputs, from writing test harnesses to detecting memory errors, reproducing crashes, and checking which code was exercised.

• Adding fuzz tests to a Rust crate
• Generating structured test inputs
• Testing unsafe blocks or foreign-function calls

Builds and improves C/C++ and Rust fuzzing harnesses, the entry points that feed generated test data into code. Helps structure inputs, reach more code paths, and address crashes caused by inconsistent test behavior.

• Writing a first fuzzing harness
• Building structured inputs for an API
• Investigating crashes that cannot be reproduced

Measure which code fuzzing reaches as it tries many generated inputs. Use coverage reports to find unreachable areas, compare progress, and guide improvements to test harnesses, starting inputs, or dictionaries of useful input values.

• Establish initial coverage for a fuzzing run
• Investigate why fuzzing stops reaching new code
• Compare coverage before and after harness changes

Build reusable parts for n8n workflows instead of copying shared logic. This skill guides typed inputs, consistent outputs, naming, and execution settings so workflows and AI agents can call the same processing steps.

• Sharing common logic across workflows
• Splitting a large workflow into reusable parts
• Exposing shared processing as an AI agent tool

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.

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

Investigate API requests that fail to replay because of client-generated signatures or anti-bot tokens. Start with captured traffic, identify the blocking inputs or signing function, and validate a reproducible request for further security testing.

• Investigate signature errors in replayed API requests
• Trace signing functions and their inputs
• Validate a reproduced signer against browser output

Build tests with fast-check for TypeScript and JavaScript or Hypothesis for Python. Generate varied inputs to check rules code should always satisfy, discover edge cases, and simplify failing inputs into small examples for debugging.

• Checking that encoding and decoding restore the original data
• Testing rules that sorting must preserve
• Generating inputs with complex constraints

Use when writing MATLAB functions with arguments blocks — repeating arguments (arguments (Repeating)), .?ClassName property import in constructors, name-value forwarding with namedargs2cell, or migrating from inputParser or validateattributes. Also when reviewing signatures for implicit-conversion pitfalls (size reshaping, class coercion, computed defaults), or when restricting, constraining, or validating function inputs — scalar vs vector enforcement, type rejection, size checking. Also when asked to harden inputs, make a function more robust, tighten input checking, or rewrite for safer input acceptance — even without "validation" or "arguments block" wording. Triggers on: arguments block, mustBe validators, name-value arguments, varargin, inputParser, nargin/narginchk, restrict input, type checking, scalar constraint, harden inputs, defensive programming. Not for: App Designer callbacks, Simulink mask parameters, class inheritance, or runtime validation outside function or property declarations.

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