177 skills · sorted by relevance
Verify that infrastructure code is connected to the system and actually runs. Trace execution paths, check hook registration and database configuration, and test the full workflow to identify unused code or duplicate implementations before declaring completion.
Use this skill for end-to-end testing of a persistence pipeline, the sequence of steps that saves data. The supplied material states this purpose but provides only placeholder instructions, without concrete checks.
Review session changes, group related files into focused Git commits, and draft messages explaining their purpose. Presents the plan for approval before committing, omits Claude attribution, and records development reasoning through a project script.
Look up Claude Code commands and options for interactive work or automation. Covers non-interactive execution, session management, output formats, tool permissions, and agent definitions, with command examples for scripts and CI/CD workflows.
Inspect Claude Code execution traces in Braintrust, including tool calls and token usage. The guide explains event hooks, troubleshooting missing records, and matching separate parent and sub-agent traces using timestamps and metadata.
Review Claude Code sessions using Braintrust traces, records of agent activity. Summarize tool, agent, and skill usage, inspect repeated actions, replay sessions, and track token consumption to understand failures and recurring work patterns.
Organize implementation work across research, planning, validation, implementation, and review agents. Each stage passes files to the next, while implementation starts with failing tests and the main conversation coordinates the workflow.
Coordinate background agents using system progress reminders while continuing other work. Read their output files after completion signals, avoiding repeated status checks and full transcripts that consume tokens and crowd the main conversation.
Find and replace code using syntax patterns with ast-grep. Search for function calls, imports, and definitions while excluding matches inside strings or comments, then preview proposed replacements before applying changes across selected files or directories.
Set up Agentica patterns that coordinate multiple AI agents. Choose different perspectives for research, a coordinator with specialists for implementation, repeated generation and critique for refinement, or a panel of agents for majority-based validation.
Set up Python agents using Agentica SDK with a local proxy that forwards requests to Claude CLI. Follow startup steps, configure connection endpoints, check service health, and troubleshoot connection failures or missing tool access.
Build Python agents with Agentica SDK using function decorators or agent instances. Examples cover conversation memory, external tools, MCP connections for tool access, multi-agent coordination, typed results, and logging to help implement and debug agent behavior.
Write clearer instructions for Agentica and REPL agents by specifying roles, file operations, and output formats. Includes templates for dividing research, coordinating specialists, reviewing solutions, and passing results between agents through files.
Consult Agentica APIs when building workflows where multiple AI agents cooperate. This reference covers eleven coordination patterns, agent tracking, shared state, handoffs, and ways to combine results, with pointers to detailed API specifications.
Guide framework, language, database, and infrastructure migrations through research, impact analysis, phased planning, implementation, and review. Specialized agents produce reports and code changes, with testing steps and a rollback strategy included in the workflow.
Standardize Python scripts that call MCP tools, which connect an agent to external capabilities. The rules cover command-line parameters, usage documentation, tool names, error messages, and output that Claude can process.
Connect Agentica SDK agents to Claude Code CLI through a proxy, an intermediary server. Use setup and debugging guidance to address file permissions, streamed responses, result formatting, and agents reporting success without performing the requested operations.
Connect five MCP tools, agent-accessible external capabilities, into a development workflow spanning documentation search, code search, quality checks, and Git status. Learn how to share results between steps and continue when individual tools are unavailable or fail.
Handle equations, calculus, matrices, unit conversions, and mathematical explanations through one entry point. The skill selects a suitable calculation tool or topic guide, including symbolic math tools that work directly with expressions.
Turn math requests into specific command-line instructions using pattern-based routing. It covers calculus, equations, unit conversion, geometry, logical proofs, and plotting, returning a command for the agent to execute with the corresponding math script.
Find the right tool for equations, calculus, numerical calculations, proof checks, or math practice. The guide connects tasks to commands for computing results, verifying steps, requesting hints, and plotting functions.
Find lemmas, reusable mathematical results, in Mathlib by matching the type signature you need for a Lean proof. Search with type variables, related terms, or exact names to identify candidates you can apply.
Choose LLM settings for theorem proving, code generation, and creative exploration. It provides examples for output limits and response variety, plus proof planning prompts and a method for generating multiple proof attempts and selecting the best candidate.
Guide agents to index files as soon as they are created, making them searchable for subsequent tasks. The skill describes write-triggered indexing and single-file updates instead of waiting for session-end batches or scheduled jobs.