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Improve CircleCI build and test pipelines by identifying slow or unreliable jobs, then adjusting caching, data sharing between jobs, triggers, and parallel execution. Receive incremental configuration changes with expected effects, validation criteria, and rollback guidance.
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Build and debug Python agents with Swarms, a framework for coordinating multiple AI agents. Covers tool connections, saved memory, autonomous execution, and workflow choices such as sequential steps, parallel work, specialist delegation, and combined answers.
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Build workflows on OpenServ where multiple AI agents share tasks and pass results between steps. Examples cover research, writing, and image creation, with guidance on finding agents, setting dependencies, and connecting sequential or parallel work.
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Automate browser work with a command-line tool for AI agents. Extract JavaScript-rendered content, fill forms, upload files, capture screenshots and network responses, and manage separate browser sessions for tasks involving multiple accounts or manual assistance.
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Write and configure unit tests, which check individual pieces of code, using Vitest. Replace dependencies with test substitutes, measure code coverage, compare saved outputs, and organize filtered or parallel test runs.
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Supports development with code quality and security checks, change impact analysis, documentation templates, and coordination across multiple agents. It defines checkpoints for new modules and refactoring, plus roles and file ownership rules for parallel work.
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Organize development work across multiple AI agents by defining roles, splitting tasks, sharing status, and resolving conflicts. Use file ownership rules and Codex agent tools to coordinate parallel changes without assigning simultaneous writers to one file.
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Convert NUnit 3 or 4 tests to MSTest v4 while keeping the existing target framework and test runner. The workflow translates assertions, test data, setup, and concurrency settings, then compares test counts and results.
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Write and repair custom MSBuild targets, the steps that organize build tasks. Preserve existing build sequences, choose appropriate insertion points, distinguish returned results from rebuild checks, and register generated files for cleanup.
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Make specific C# behavior testable by adding a minimal way to replace time, randomness, or other external dependencies. Preserve public APIs and production behavior, then run repeatable tests without real file access or waiting.
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Analyze MSBuild binary logs and timing summaries to find why builds are slow. Identify costly reference resolution, code analysis, file copying, restore work, or serialized tasks, then prioritize improvements by their share of total build time.
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Establish repeatable performance measurements for MSBuild and .NET builds before optimizing. Compare clean-output, changed-source, and unchanged builds, record timing ranges, and assess output layouts, dependency references, and parallel build options against the measured baseline.
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Diagnose unnecessary rebuilds in MSBuild by examining build logs, file timestamps, and declared inputs and outputs. Identify why work repeats, guide fixes when requested, and verify that a second unchanged build skips the expected targets.
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Coordinate multiple AI agents by assigning tasks, tracking dependencies, exchanging messages, and monitoring progress. Separate Git working directories help organize parallel changes, while context checks and snapshots support handoffs and recovery.
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Search multiple academic databases, screen studies against documented criteria, and compare findings by theme. Produce literature reviews in Markdown and PDF with study quality assessments, verified citation details, and diagrams explaining the review process or findings.
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Review code changes with five parallel agents before committing. Each checks a separate area: code quality, simplicity, consistency, robustness, or task scope. Results identify issues, and every check must pass before the changes are considered ready.
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Carry a research task from brainstorming and planning through code implementation, review, and documentation. Multiple agents handle independent implementation tasks in parallel, then report the resulting files, changes, and findings.
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Use this skill whenever the user wants to conduct an event study, create event study plots, test for parallel trends, implement difference-in-differences designs, or work with any panel data estimation that involves pre/post treatment comparisons. Trigger on phrases like "event study", "parallel trends", "pre-trends", "dynamic treatment effects", "leads and lags", "TWFE", "two-way fixed effects", "staggered adoption", "staggered treatment", "difference-in-differences", "DiD", "Sun and Abraham", "Callaway and Sant'Anna", "de Chaisemartin", "Borusyak", "did_multiplegt", "fixest", "did2s", "bacon decomposition", or any reference to plotting coefficients around a treatment event. Also trigger when the user uploads panel data and wants to estimate treatment effects with variation in treatment timing. All code is in R.
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Build causal inference analyses to estimate intervention effects using templates drawn from Causal Inference: The Mixtape. Adapt code across Python, R, and Stata, with checks for method assumptions and guidance on language-specific packages and limitations.
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Decompose large tasks across parallel agents — use for migrations, multi-file refactors, or batch work
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Decompose and execute large changes, migrations, or multi-issue fixes in parallel with quality gates
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Parallel read-only multi-agent review of a current git diff or explicit file scope to find behavioral regressions, security or privacy risks, performance or reliability issues, and contract or test coverage gaps. Use when the user asks for a review swarm, parallel review, diff review, regression review, security review, or wants high-signal issues plus a prioritized fix path without editing files.
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Parallel Agent Orchestration
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Parallel Agent Type Contracts
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