Merge outputs from multiple sources, resolve conflicts, and reconcile constraints into a unified result. Use when combining parallel agent outputs, merging data from different systems, or...
Check correctness against tests, specs, or invariants; produce pass/fail evidence. Use when validating changes, testing hypotheses, checking invariants, or confirming behavior matches expectations.
Fetch known facts or data from specified sources with citations and evidence pointers. Use when you know what you need and where to find it. Emphasizes provenance and verifiable references.
Convert data between formats, schemas, or representations with explicit loss accounting and validation. Use when reformatting data, mapping between schemas, normalizing inputs, or translating structures.
Retrieve prior decisions, rationale, and learned patterns from memory to apply consistently. Use when needing context from previous interactions, looking up past decisions, or ensuring consistency...
Break a goal into subgoals, constraints, and acceptance criteria. Use when planning complex work, creating work breakdown structures, or defining requirements.
Combine heterogeneous data sources into a unified model with conflict resolution, schema alignment, and provenance tracking. Use when merging data from multiple systems, consolidating information,...
Ingest and parse incoming messages, events, or signals into structured form. Use when processing external inputs, handling API responses, parsing webhook payloads, or ingesting sensor data.
Produce a comprehensive audit trail of actions, tools used, changes made, and decision rationale. Use when recording compliance evidence, tracking changes, or documenting decision lineage.
Write stable learnings, decisions, and patterns to durable storage like CLAUDE.md or knowledge files. Use when saving project decisions, recording patterns, or updating long-term memory.
Compare multiple alternatives using explicit criteria, weighted scoring, and tradeoff analysis. Use when choosing between options, evaluating alternatives, or making decisions.
Find relevant items under uncertainty across repositories, databases, web sources, or any searchable corpus. Use when exploring unknown territory, finding related information, or discovering...
Produce clear reasoning with assumptions, causal chains, and evidence. Use when clarifying decisions, teaching concepts, justifying recommendations, or documenting rationale.
Beads Viewer - Graph-aware triage engine for Beads projects. Computes PageRank, betweenness, critical path, and cycles. Use --robot-* flags for AI agents.
Build AI applications using the Azure AI Projects Python SDK (azure-ai-projects). Use when working with Foundry project clients, creating versioned agents with PromptAgentDefinition, running...
Test PydanticAI agents using TestModel, FunctionModel, VCR cassettes, and inline snapshots. Use when writing unit tests, mocking LLM responses, or recording API interactions.
Destructive Command Guard - High-performance Rust hook for Claude Code that blocks dangerous commands before execution. SIMD-accelerated, modular pack system, whitelist-first architecture....
Avoid common mistakes and debug issues in PydanticAI agents. Use when encountering errors, unexpected behavior, or when reviewing agent implementations.
Debug LLM applications using the Phoenix CLI. Fetch traces, analyze errors, review experiments, and inspect datasets. Use when debugging AI/LLM applications, analyzing trace data, working with...
Configure LLM providers, use fallback models, handle streaming, and manage model settings in PydanticAI. Use when selecting models, implementing resilience, or optimizing API calls.