976 skills · sorted by relevance
Measure and optimize claude-flow v3 startup, search, attention processing, and memory use. Compare benchmarks against performance targets, track changes over time, and identify regressions to guide further optimization.
Measure code performance and run benchmarks to investigate slow operations, regressions, or memory pressure. Establish a baseline, compare results under matching conditions, report variability, and rerun correctness tests after optimization.
Check a codebase for known dependency vulnerabilities, hard-coded secrets, and risky code patterns using Ruflo security tools. Choose a scan depth, inspect matching lines, and report verified concerns with a clear account of what was checked.
Develop features through a test-first workflow using mocks, or stand-ins for dependencies. Write a failing behavior test, implement the minimum code, refactor, and record observed results from final target and regression tests.
Compare two saved cost-summary JSON snapshots to identify changes in total spending, sessions, and model costs. Set limits on spending increases or token growth to flag cost regressions during pull request checks.
Compare two recorded browser sessions at matching step IDs using screenshots and accessibility snapshots, which describe page elements. Report the first difference and overall similarity to check interface changes or verify replayed sessions.
Measure and optimize claude-flow v3 startup, search, attention processing, and memory use. Compare benchmarks against performance targets, track changes over time, and identify regressions to guide further optimization.
Clean up AI-generated code in branch changes or selected files while preserving behavior. Establish passing regression tests first, then remove unnecessary code and complexity, run project quality checks, and report both changes and skipped issues.
Reproduce and fix LazyCodex or Codex defects in a temporary workspace. Verify the change with regression tests and real usage, then deliver an upstream Codex pull request or a LazyCodex issue containing the tested patch.
Guide test writing, updates, and reviews in Remotion by checking observable user outcomes. Choose integration, unit, visual, or end-to-end tests that detect broken workflows, and verify that bug fixes address the original regression.
Build an evaluation process for AI agents using success, reliability, cost, and latency measures. Define scoring criteria, repeatable environments, failure analysis, and regression tests that check for recurring problems to support model selection and system improvements.
Create static statistical charts from pandas tables to explore distributions, relationships, and group differences. Supports scatter plots, regression displays, pair plots, and heatmaps, with explicit choices for aggregation, uncertainty, missing data, and visual styling.
Fit regression and time series models in Python, interpret coefficients, compare alternatives, and check model assumptions. Supports statistical diagnostics and forecasting, with confidence intervals and validation steps to assess uncertainty and predictive performance.
Plan study sample sizes, estimate statistical power—the chance of detecting an assumed effect—and find the smallest detectable effect. Supports standard tests and simulations for complex designs, with sensitivity analyses and sample size justifications.
Build Bayesian models in PyMC to estimate effects and quantify uncertainty. Fit regression or hierarchical models, check sampling diagnostics, assess predictions against observed data, predict new groups, and compare models using predictive performance.
Analyze time series with aeon, a Python toolkit for data that changes over time. Supports classification, regression, clustering, forecasting, anomaly detection, and pattern search, with guidance on preprocessing, algorithm selection, and evaluation.
Build repeatable evaluations for AI agent responses, tool choices, and multi-step tasks. Use reference datasets and scoring criteria to compare changes, detect quality regressions, and set automated checks that must pass before deployment.
Review pull requests, branches, commit ranges, or local changes with GitNexus. Combine code relationship analysis with source and test inspection to identify concrete defects, missing tests, affected behavior, and risks before merging.
Assess authorized APIs through a ten-stage workflow covering endpoint discovery, authentication, access permissions, input handling, and request limits. Collect reproducible evidence and turn findings into regression checks that can run in a development pipeline.
Simplify bloated AI-generated code by removing dead code, duplicated logic, and unnecessary layers while checking that existing behavior stays intact. Use a limited cleanup scope, verify changes with relevant checks, or request a review without edits.
Assess skills CLI pull requests for focused bug fixes using visible patches and regression tests. Surface well-supported candidates with a label and brief maintainer comment, while keeping uncertain findings out of public updates.
Create and review Mastra tests that reproduce user-visible failures through public APIs, real network requests, and actual storage. Verify that tests fail before a fix and cover the different execution paths affected by the bug.
Re-review a pull request after new commits arrive. Check every previous finding, inspect the new changes for regressions, and review the full current change. Run relevant verification and publish a test report and review verdict within Factory.
Fix bugs reported in GitHub issues through a reproduce-first process. The workflow guides code changes, build checks, verification, regression tests that check for recurring failures, and review before considering the fix complete.