327 skills · sorted by relevance
Turn speedup requests into a measured optimization process with limits on time, cost, and variants. Compare each change against a baseline and correctness checks, then retain the fastest safe option with reproducible commands and measurements.
Guides debugging through reproduction, comparison with working examples, hypothesis testing, and a focused fix. It starts with a repeatable check for the exact symptom and uses regression tests to verify the result.
Investigate software errors and unexpected behavior through four stages: investigation, analysis, hypothesis, and implementation. It requires identifying the root cause before making fixes and distinguishes evidence from code inspection, tests, and live runs when reporting findings.
Investigate crashes, hangs, incorrect responses, and other failures by testing hypotheses against observed runtime behavior. Confirm the cause, verify the fix with a reproducing test, check the real workflow, and remove temporary debugging artifacts.
Plan A/B tests to compare page changes using clear hypotheses and meaningful measures. The skill helps define sample size, duration, traffic allocation, and result interpretation, and organize a continuing program of prioritized experiments.
Analyze experimental or survey data with guidance on choosing statistical tests, checking assumptions, estimating effect sizes and uncertainty, planning sample sizes, and writing results in APA style, a standard format for research reporting.
Turn observations or preliminary findings into testable research plans. The skill separates candidate hypotheses from evidence, develops competing explanations and distinguishing predictions, and helps specify measurements and analysis plans for preregistration, a research plan recorded before testing.
Organize repeated experiments to improve models, prompts, or agent systems against measurable goals. The skill records hypotheses and lessons in a persistent tree, isolates trials, and compares promising candidates using evaluation data kept separate from development.
Investigate signs of intrusion through hypothesis-led log searches, then turn findings into detection rules. Supports Sigma, YARA, and SIEM queries, with attention to false positives, data field mapping, and validation against historical logs.
Investigate bugs and slow applications through reproducible examples, logs, environment comparisons, and hypothesis testing. The skill structures root cause analysis and performance measurement, helping developers narrow down failures and verify that a fix works.
Diagnose problems in OMC or Claude Code sessions using logs, execution traces, saved state, and focused reproduction. Receive a clear account of the failure, a supported cause hypothesis, and the smallest next corrective action.
Apply statistical tests to financial time series, model changing volatility, and diagnose regression problems. Estimate uncertainty in strategy metrics through bootstrap resampling, which repeatedly samples existing data, and organize findings into a statistical testing report.
Investigate attempts to gain higher privileges on Windows and Linux using endpoint and security logs. Guide hypothesis testing, evidence analysis, false-positive checks, and reporting to help assess suspicious privilege changes and the scope of a compromise.
Plan A/B tests that compare page or product changes, with clear hypotheses, sample sizes, success metrics, and traffic allocation. Get a structured test plan and guidance for interpreting results and deciding what to try next.
Troubleshoot Zoom integration failures across authentication, API requests, event notifications, and SDK behavior. Get likely causes ranked by probability, relevant reference links, targeted fix steps, and a short checklist to verify the result.
Analyze data distributions, trends, unusual values, and differences between groups using suitable statistical methods. The skill guides interpretation of effect sizes, meaning the magnitude of differences, alongside uncertainty and business impact, while checking for misleading conclusions.
Investigate difficult bugs through explicit hypotheses, targeted logs, and repeated checks against actual data. Keep an investigation journal to track findings, examine environment differences and leftover state, and confirm the root cause before applying a fix.
Plan and evaluate product A/B tests with clear hypotheses, success metrics, sample size estimates, and stopping rules. Rank proposed experiments and interpret results using effect sizes and uncertainty to support practical product decisions.
Investigate hidden attacker activity using focused hypotheses, indicators of compromise such as malicious domains, and statistical checks on logs. Rank investigation priorities, generate indicator sweep targets, and flag unusual behavior for human review.
Map repeatable business processes by owner and analyze time spent working, waiting, or reworking. Using stage-level timing data, produce a process diagram, ranked bottlenecks, and cycle-time measures to focus improvements on the main constraint.
Turn a product idea into a Lean Canvas, a nine-part outline of business assumptions. Define customer problems, solutions, value, channels, revenue, costs, and metrics, then identify risky assumptions and suggest experiments to test them.
Turn a new product idea into a testable hypothesis and two or three simple experiments. Define target customers, expected actions, measurement methods, and success thresholds to assess demand through behavior such as sign-ups, pre-orders, or paid commitments.
Search Google SecOps events for threats that have not triggered alerts. Test attack hypotheses, check historical records for compromise indicators, and examine unusual activity, then document findings and add them to relevant security cases.
Investigate security incidents in Google SecOps by connecting host, user, and network events. Reconstruct attack timelines, trace movement between systems, assess affected assets, and organize findings into case notes and investigation reports.