Skill UI

Arbor executes an Autonomous Optimization (AO) loop, allowing users to iteratively improve complex artifacts—such as models, agents, or data pipelines—against a measurable objective. It implements Hypothesis Tree Refinement (HTR), structuring research into a persistent tree to track hypotheses, insights, and evidence. This process is crucial for long-horizon optimization tasks (e.g., Kaggle-style benchmarking) and utilizes a held-out test set to prevent overfitting and ensure cumulative, auditable progress.

This skill outlines a systematic, multi-phase workflow for diagnosing hard bugs and performance regressions in complex codebases. It emphasizes the critical necessity of building a tight, deterministic feedback loop (Phase 1) before moving to hypothesis generation. The process guides users through reproducing the failure, minimizing the scope of the issue, and forming actionable, ranked hypotheses.

Chainsaw is a fast, Rust-based tool for digital forensics and threat hunting. It enables analysts to rapidly perform Sigma rule checks and keyword searches across collected Windows Event Logs (.evtx) and specialized artifacts (like shimcache and SRUM). Ideal for first-response and offline analysis, it provides detection capabilities without needing a SIEM ingestion pipeline, allowing quick hypothesis testing in the field or air-gapped environments.

A systematic, non-mutating investigation designed for complex software failures, including bugs, regressions, crashes, and flaky behavior. This skill launches four specialized, read-only sub-agents in parallel to perform deep root-cause analysis, trace code paths, pinpoint the smallest failing seam, and predict the quickest path to confirming or fixing the issue. It is designed purely for diagnosis and hypothesis generation.

A comprehensive, four-phase methodology for debugging complex software issues. It guides users through root cause investigation, pattern analysis, hypothesis testing, and controlled implementation. The core principle emphasizes finding the underlying cause rather than applying quick, symptomatic fixes, ensuring robust and reliable code improvements across various failure scenarios.

This template guides the process of turning raw A/B test results, growth experiments, or product feature data into a clear, actionable decision report. It structures the analysis beyond mere metrics by forcing the inclusion of hypothesis, setup details, result interpretation, and a final, justified decision (Ship/Stop/Iterate). Essential for Product Managers and Growth Teams.

This skill guides researchers on how to triage numerous robustness checks for short-format academic manuscripts (e.g., AER: Insights) constrained by word and exhibit limits. It teaches the 'minimum credible set' approach: identifying the 1-2 core checks that defend the central hypothesis for the main text, while systematically moving all supplementary checks to the Appendix and properly signposting them in one sentence to maintain credibility.

This guide provides late-stage polishing for academic manuscripts, specifically targeting the Academy of Management Review (AMR) house style. It helps transform drafts that read like literature reviews or essays into tightly structured, argument-driven theoretical pieces. Key focus areas include establishing a clear theoretical puzzle in the introduction, maintaining continuous argumentative flow, ensuring precise definition of constructs, and articulating propositions without using hypothesis language. It is crucial for authors who need to refine their prose and structure before final subm...

This skill guides researchers through the critical pre-writing process of evaluating a scientific finding's scope, mechanistic depth, and significance. It stress-tests whether the work constitutes a complete, hypothesis-driven story suitable for top-tier journals, helping authors determine if they need to strengthen the mechanism, collect more converging evidence, or adjust their target journal venue.

A comprehensive guide to restructuring scientific manuscripts from a collection of isolated findings into a single, compelling, and cohesive narrative arc. It forces the story into a Hypothesis → Mechanism → Significance structure, ensuring that every figure advances a central, airtight argument, crucial for top-tier journal submissions.

A detailed guide for scrutinizing statistical inference in high-level econometric manuscripts, designed to withstand rigorous technical peer review. It covers critical areas such as selecting appropriate clustering levels, handling weak instrumental variables (IV), correcting for multiple hypothesis testing, and ensuring accurate standard error reporting for panel data and causal inference models.

A comprehensive guide for designing rigorous, multi-experiment cognitive psychology studies. This framework ensures that experiments are engineered to discriminate between competing theoretical models by tightly controlling confounds, optimizing stimulus construction, and providing adequate power for the critical contrast, rather than just the main effect. It guides the entire planning process from hypothesis formulation to pre-data lockdown.