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Experiment Design Guide

experiment-designer

alirezarezvani/claude-skills

Guides product teams through hypothesis writing, metric definition, sample-size planning, ICE prioritization, and statistical interpretation so experiments stay practical, defensible, and aligned with business decision thresholds.

Autonomous AI Research Orchestration Engine

Orchestra-Research/AI-Research-SKILLs

This skill orchestrates end-to-end autonomous AI research projects using a sophisticated two-loop architecture. It manages the entire research lifecycle, from initial literature review and hypothesis formation to running constrained experiments and synthesizing findings into publishable papers. It acts as a project manager, autonomously driving the process while structuring the workspace and presenting progress to human users.

Building Threat Hunt Hypothesis Framework

building-threat-hunt-hypothesis-framework

mukul975/Anthropic-Cybersecurity-Skills

This framework provides a systematic methodology for cybersecurity professionals to transform raw threat intelligence, observed attack patterns, and environmental telemetry into verifiable, testable hunting hypotheses. It guides the end-to-end process, from hypothesis formulation and data identification across SIEM/EDR systems, to deep correlation and reporting, enabling proactive threat detection and scope assessment.

Hypothesis-Driven APT Threat Hunting

hunting-advanced-persistent-threats

mukul975/Anthropic-Cybersecurity-Skills

A proactive, hypothesis-driven skill designed to hunt for Advanced Persistent Threats (APT) within complex enterprise environments. It analyzes endpoint telemetry, network logs, and memory artifacts using frameworks like MITRE ATT&CK. Use this skill when investigating anomalous behavior flagged by UEBA, conducting scheduled threat sprints, or validating exposure to known TTPs using tools like Velociraptor and osquery.

Scheduled Task Persistence Hunting Guide

hunting-for-scheduled-task-persistence

mukul975/Anthropic-Cybersecurity-Skills

This guide provides a structured methodology for proactively hunting adversaries who establish persistence using Windows Scheduled Tasks (T1053). It outlines the necessary prerequisites (EDR, SIEM, Sysmon) and a comprehensive workflow—from hypothesis formulation to threat correlation—for detecting suspicious task creation, unusual scheduling patterns, and attacker TTPs during incident response or red teaming.

Research Refinement And Experiment Planning Pipeline

research-refine-pipeline

wanshuiyin/Auto-claude-code-research-in-sleep

This comprehensive pipeline guides users from a vague research concept to a fully structured, testable academic proposal. It first refines the core methodology (research-refine) to establish a stable thesis, and then generates a detailed, claim-driven experiment roadmap (experiment-plan). This end-to-end workflow ensures that the final academic output is both elegant and empirically validated.

Deterministic Startup Screening

hard-screening-startup

davepoon/buildwithclaude

Hard screening skill runs deterministic VC analyses where Claude gathers context, Python scores/formats, and the pipeline produces auditable verdicts with investment thesis guidance.

Consulting Research Reports

consulting-analysis

bytedance/deer-flow

Generates consulting-grade research reports in markdown, guiding users through framework creation and final synthesis across market, finance, brand, and industry domains with strict data traceability.

Generate Conversational Podcast Audio

podcast-generation

bytedance/deer-flow

This skill transforms written content, such as articles and reports, into engaging, two-host conversational podcast audio. It first structures the content into a detailed JSON script (dialogue format) and then uses advanced Text-to-Speech synthesis to generate the final MP3 file. Supports both English and Chinese languages, making content accessible for audio-only listening.

Hypothesis-Driven Debugging: Scientific Root Cause Analysis

hypothesis-testing

rohitg00/skillkit

This skill teaches users to apply the scientific method to software debugging. Instead of random changes, it guides the process through five steps: Observation, Hypothesizing, Prediction, Testing, and Analysis. By systematically forming and testing specific, measurable hypotheses, users can efficiently isolate failing components and confirm the true root cause of technical issues.

Systematic Root Cause Analysis

root-cause-analysis

rohitg00/skillkit

Performs a systematic and structured investigation to determine the true source of bugs, errors, and unexpected system behavior, rather than merely treating symptoms. This method utilizes techniques like the Five Whys, hypothesis-driven testing, stack trace analysis, and gathering evidence (logs, git diffs) across multiple phases. It is ideal for complex, intermittent, or deeply rooted technical issues in software development.

Statistical A/B Testing and Analysis Expert

statistical-analyst

alirezarezvani/claude-skills

This expert tool guides users through rigorous statistical analysis, specializing in A/B testing, hypothesis testing, and interpreting experimental results. It helps define sample sizes, executes appropriate tests (Z-test, T-test, Chi-square), and critically distinguishes between 'statistical significance' and 'practical significance' to ensure data-driven decision-making for product and business strategy.

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