432 skills · sorted by relevance
Plan bug bounty sessions around a clear goal and selected vulnerability types. Guides movement through reconnaissance, mapping, discovery, proof, and reporting, with prompts and routing to help decide what to investigate next.
Prepare a release recap from changes merged since the last release tag. Group work by author, count features, bug fixes, and maintenance tasks, and post the reviewed summary to Slack for final quality checks.
Compare how different attribution models, or ways of assigning conversion credit, change campaign results across Google and Meta. The skill highlights shifts in credited conversions and return on ad spend, then suggests budget adjustments based on the comparison.
Organize a bug bounty investigation from reconnaissance through reporting. The skill helps choose a focused objective, identify the current phase, select relevant techniques and tools, and distinguish reproducible security impact from misleading test results.
Write MATLAB or GNU Octave scripts for matrix calculations, data analysis, signal processing, and scientific plots. The skill also helps explain syntax and functions, convert code between MATLAB and Python, and run scripts when an interpreter is installed.
Choose and implement machine learning methods for estimating causal effects with many control variables or effects that differ across groups. Guidance covers DML, causal forests, and related methods, including diagnostics and limits on interpretation.
Estimate effects by comparing observations just above and below an eligibility threshold using regression discontinuity design. Covers plots, comparison range selection, sharp and fuzzy designs, validity checks, and sensitivity analysis for reporting research findings.
Plan and implement instrumental variable estimation and propensity score matching, methods for estimating causal effects under different assumptions. Includes Python, R, and Stata examples, diagnostic checks, and guidance on interpreting and reporting results.
Build a Python workflow for empirical research, from data cleaning and statistical checks to estimation, robustness checks, and paper-ready tables and figures. Economics is the default, with additional modes for epidemiology, public health, and machine learning for causal inference.
Work through matrix eigenvalue problems, from the characteristic polynomial, whose roots give the eigenvalues, to finding eigenvectors. Follow a structured process to check the results and examine algebraic and geometric multiplicities.
Work through convex optimization problems by checking convexity, putting objectives and constraints into a standard form, and choosing a solver. The skill provides SciPy examples and outlines checks for constraint satisfaction and mathematical conditions used to assess optimality.
Find numerical solutions where a function equals zero. The skill helps choose a method based on known intervals and available derivatives, then guides SciPy implementation, searches for multiple solutions, and checks accuracy and valid ranges.
Work through graduate-level problems using Rudin's Real and Complex Analysis. The skill guides theorem selection for measure theory, integration, and complex analysis, with commands for textbook searches, symbolic calculations, and verification.
Use UMAP to reduce data with many features into fewer dimensions for plotting or further analysis. The skill guides parameter choices, preprocessing for clustering, label-guided projections, and transforming new data with an already trained model.
Explain machine learning predictions with SHAP values, which measure how each input contributes to an output. Create charts for individual predictions and overall feature importance, investigate errors, compare models, and examine differences between groups.
Write MATLAB or GNU Octave scripts for matrix calculations, statistics, signal processing, and scientific plots. Get help with language syntax, import and export data, or convert code between MATLAB and Python for numerical analysis tasks.
Perform arithmetic, algebra, calculus, matrix operations, number theory, and statistics with SymPy, a symbolic mathematics library. Return exact expressions, numerical values, and LaTeX notation for checking calculations or using results in other work.
Cross-platform issue triage. Pulls from Sentry (MCP), Linear (MCP), GitHub Issues (gh). Cross-references against code to find already-fixed issues. Auto-resolves fixed ones. Dispatches agents for active issues.
The English summary is being prepared. Showing the original description.
Business-level "what should I do next". Priority stack — fires > unread comms > ready-to-merge PRs > Linear sprint > revenue-generating GSD work. Uses pre-gathered data and routes to the right skill.
The English summary is being prepared. Showing the original description.
Business operations command center. Routes to the right ops command based on what you need — briefing, inbox, fires, projects, comms, triage, linear, revenue, deploy, or yolo mode.
The English summary is being prepared. Showing the original description.
Interactive setup wizard for the claude-ops plugin. Installs missing CLIs, configures env vars for each channel (Telegram, WhatsApp, Email, Slack, Notion, Linear, Sentry, Vercel), builds the project registry, and saves user preferences. Run once after installing the plugin or any time to reconfigure.
The English summary is being prepared. Showing the original description.
[omh] Email, Slack, or Jira action to perform: external app actions - email, Slack, Discord, Notion, Linear, Jira, CRM, and similar providers, scoped with auth, payload, confirmation, and result-evidence gates. Use when the user says: connector-operator, connector operator, external app action, external connector action, saas action, api action, send email, email customer.
The English summary is being prepared. Showing the original description.
Map the full ecosystem of touchpoints, channels, and relationships across a service. Use when the experience spans more than one product. For one persona's linear journey use `journey-map` (design-research); for backstage operations use `service-blueprint`.
The English summary is being prepared. Showing the original description.
Configures CI/CD pipelines using AWS CodePipeline, CodeBuild, CodeDeploy, CodeConnections, and CodeArtifact. Covers CodePipeline V2 (triggers, variables, execution modes, cross-account), buildspec.yml (caching, VPC, Docker), CodeDeploy strategies (blue/green, canary, linear), CodeArtifact (private package registries, auth tokens, cross-account), and source connections (GitHub, GitLab, Bitbucket). Applies when CodePipeline, CodeBuild, CodeDeploy, CodeConnections, CodeArtifact, buildspec.yml, appspec.yml, or CI/CD pipeline orchestration is referenced. Does NOT cover: ECS Fargate services or task definitions (use aws-containers), CDK Pipelines or cdk deploy (use aws-cdk), sam deploy (use aws-serverless), Amplify deployments (use aws-amplify), or GitHub Actions/GitLab CI.
The English summary is being prepared. Showing the original description.