comprehensive-research-agent
Comprehensive Research Agent Best Practices
This skill addresses common failures in multi-step research tasks: unhandled tool errors, missing validation, opaque reasoning, and premature conclusions. It provides structured protocols for source validation, error recovery, and thinking transparency that significantly improves research quality and reliability.
When to Activate
- Task involves web research with search, read_url, or fetch operations
- Task requires gathering information from multiple sources
- Task has explicit requirements for completeness or verification
- Task includes file operations that need validation (save, write, read)
- Any research or information-gathering workflow with 3+ tool interactions
Core Concepts
- Validation Checkpoints: Explicit verification steps at phase transitions to confirm tool outputs, source relevance, and information completeness before proceeding
- Error Recovery Protocols: Mandatory acknowledgment and handling of tool failures with fallback strategies rather than silent continuation
- Source Traceability: Maintaining clear tracking of which sources were actually retrieved vs. referenced from prior knowledge to prevent hallucination
- Substantive Thinking Blocks: Detailed reasoning traces that document insights, connections, gaps, and decision rationale at each step
- Cross-Source Validation: Verifying key claims against multiple sources and explicitly noting consensus, contradictions, and information gaps
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context-engineering-collection
A comprehensive collection of Agent Skills for context engineering, multi-agent architectures, and production agent systems. Use when building, optimizing, or debugging agent systems that require effective context management.
1.4Kcontext-optimization
This skill should be used when the user asks to "optimize context", "reduce token costs", "improve context efficiency", "implement KV-cache optimization", "partition context", or mentions context limits, observation masking, context budgeting, or extending effective context capacity.
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This skill should be used when the user asks to "compress context", "summarize conversation history", "implement compaction", "reduce token usage", or mentions context compression, structured summarization, tokens-per-task optimization, or long-running agent sessions exceeding context limits.
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19multi-agent-patterns
This skill should be used when the user asks to "design multi-agent system", "implement supervisor pattern", "create swarm architecture", "coordinate multiple agents", or mentions multi-agent patterns, context isolation, agent handoffs, sub-agents, or parallel agent execution.
19tool-design
This skill should be used when the user asks to "design agent tools", "create tool descriptions", "reduce tool complexity", "implement MCP tools", or mentions tool consolidation, architectural reduction, tool naming conventions, or agent-tool interfaces.
18