195 skills · sorted by relevance
Analyze records of an AI agent’s decisions and tool use to find repeated failures, lost goals, and misunderstood instructions. Use the findings to refine prompts, rerun tasks, and turn debugging lessons into reusable skills.
Design systems where multiple agents work with separate task information. Choose coordination structures, define handoffs and shared state, and plan parallel work while checking whether communication costs and failure risks justify using more than one agent.
Design structured summaries for long agent sessions that preserve goals, file changes, decisions, and next actions. Choose compression methods and timing, then test whether the agent can continue accurately without repeatedly retrieving information it already processed.
Diagnose why an AI agent becomes less reliable as conversations and documents accumulate. Distinguish missed information, persistent false claims, irrelevant material, mixed tasks, and conflicting sources, then choose ways to clean, reorganize, or separate the context.
Plan how an AI agent uses limited input space through reusable prompt prefixes, shorter tool outputs, summaries, and task splitting. Measure token use, response quality, and latency to choose optimizations that provide a net benefit.
Design how an AI agent edits its own working conversation history, choosing what to retain, rewrite, or remove. Covers safeguards, capacity monitoring, recovery after overflow, and the computing cost of changing earlier context.
Design file-based storage for agent context, including large tool results, plans, logs, and handoff notes. Keep information available outside the conversation and retrieve relevant sections when needed, instead of loading everything at once.
Design systems that use language models to judge AI responses. Covers scoring criteria, comparisons between two answers, bias reduction, confidence calibration, and checks against human judgments to make automated quality assessments more consistent and traceable.
Organize content, contacts, knowledge, and goals in structured files. Use recorded writing preferences and past posts to draft content, prepare meeting briefs from relationship history, and review weekly progress.
Use a structured starting point to write new context engineering skills. Organize activation conditions, responsibilities, practical steps, examples, and common mistakes, while keeping detailed references separate and clarifying boundaries with related skills.
Guide multi-source research through source checks, comparison of key claims, and recovery from failed tool calls. Produce reports that distinguish retrieved evidence from unresolved questions and clearly document remaining gaps.
Design tools that agents can select and call correctly. Clarify descriptions, input rules, response formats, and recovery messages, then review overlapping tools and naming conventions using examples of successful and failed calls.
Design persistent agent memory that retains knowledge across sessions and retrieves relevant facts when needed. Compare storage approaches, track people and changing information, and plan memory consolidation and evaluation based on the kinds of questions the agent must answer.
Write and review launch prompts for agents working autonomously for hours or days. Define successful deliverables, excluded partial results, stopping rules, worker handoffs, and evidence-based review requirements so completion can be assessed against explicit criteria.
Review how an AI agent loads, retains, summarizes, and shares working information. This historical guide helps examine forgotten constraints, repeated actions, tool selection, and handoffs using context engineering practices published in 2024–2025.
Diagnose why an AI workflow becomes inconsistent despite receiving extensive information. Through guided questions, define what to retain, retrieve when needed, or exclude, and develop a memory structure and a research-to-implementation process with focused context.
Design workflows where specialized AI agents share recurring product management tasks. Define each agent’s role, required information, handoffs, and human review points, then create monitoring and evaluation plans for the overall workflow.
Master the four operations of context engineering — Write, Select, Compress, Isolate. Manage token budgets, compaction strategies, and context partitioning to keep AI sessions sharp and efficient.
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Master context engineering for AI agent systems. Use when designing agent architectures, debugging context failures, optimizing token usage, implementing memory systems, building multi-agent coordination, evaluating agent performance, or developing LLM-powered pipelines. Covers context fundamentals, degradation patterns, optimization techniques (compaction, masking, caching), compression strategies, memory architectures, multi-agent patterns, LLM-as-Judge evaluation, tool design, and project development.
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Context window monitoring and budget management. Keeps orchestrator at 15-30% context usage while subagents get full 200k tokens. Provides warnings at thresholds, context-aware summarization triggers, and wave-level budget planning.
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Dynamic context injection, mode switching (dev/review/research), selective loading, and strategic compaction for token optimization.
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Use when creating or editing skills, before deployment, to verify they work under pressure and resist rationalization - applies RED-GREEN-REFACTOR cycle to process documentation by running baseline without skill, writing to address failures, iterating to close loopholes
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Use when creating or editing any prompt (commands, hooks, skills, subagent instructions) to verify it produces desired behavior - applies RED-GREEN-REFACTOR cycle to prompt engineering using subagents for isolated testing
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Add missing test coverage for your local code changes by generating new test files (covers uncommitted and untracked changes, or the latest commit if everything is committed). Use when you want write tests for new logic or increase test coverage.
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