“context-engineering”的搜索结果

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.

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• Investigating declining quality in long conversations
• Recovering from false claims that persist after correction
• Resolving conflicts between retrieved documents

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.

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• Summarize long coding sessions for continuation
• Preserve file changes and decision history
• Create durable summaries for task handoffs

Explore how coordinating agents can pass relevant working state to workers through KV cache, a model's internal attention memory. Covers task-specific selection, runtime requirements, and comparisons of accuracy, token use, and processing overhead.

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• Reducing repeated history sent to worker agents
• Comparing internal memory sharing with text summaries
• Assessing runtime support for KV cache compaction

Plan projects that use large language models, from checking whether the task suits a model to designing processing stages. Estimate costs, choose single or multiple agents, and organize prototypes, structured outputs, and iteration.

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• Checking whether a task is suitable for an LLM
• Designing a pipeline for batch analysis
• Estimating processing costs before development

Design infrastructure for agents that run in isolated remote environments. Plan faster session startup, saved work states, shared sessions, authentication, and delivery of code changes and other results back to users.

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• Designing infrastructure for background coding agents
• Reducing delays when agent sessions start
• Saving and restoring session work

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.

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• Compare responses from different models
• Run A/B tests for prompt changes
• Create shared scoring rubrics

Design the controls around autonomous agents: what they can edit, how results are evaluated, how progress is logged, and when human approval is required. Build research and experiment loops with clear recovery and review rules.

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• Designing autonomous research and experiment loops
• Separating editable work from fixed evaluation rules
• Preserving progress and failed attempts across long runs

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.

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• Reviewing information loaded at startup versus on demand
• Investigating repeated actions or forgotten constraints
• Planning summaries and persistent task notes

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.

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• Diagnosing vague answers despite extensive input
• Defining information boundaries between agents
• Planning persistent memory and on-demand retrieval

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.

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• Designing parallel agents for weekly competitive research
• Defining agent roles and information handoffs
• Planning quality checks and cost monitoring

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 errors occur deep in execution and you need to trace back to find the original trigger - systematically traces bugs backward through call stack, adding instrumentation when needed, to identify source of invalid data or incorrect behavior

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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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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 after writing tests to assess coverage quality across structural, mutation, requirements, and API/integration dimensions; organized knowledge for choosing and interpreting coverage analyses.

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Guide for setup Serena MCP server for semantic code retrieval and editing capabilities

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Use when tackling complex reasoning tasks requiring step-by-step logic, multi-step arithmetic, commonsense reasoning, symbolic manipulation, or problems where simple prompting fails - provides comprehensive guide to Chain-of-Thought and related prompting techniques (Zero-shot CoT, Self-Consistency, Tree of Thoughts, Least-to-Most, ReAct, PAL, Reflexion) with templates, decision matrices, and research-backed patterns

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Use when implementing any feature or bugfix, before writing implementation code - write the test first, watch it fail, write minimal code to pass; ensures tests actually verify behavior by requiring failure first

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This skill should be used when need prioritize what changed code in repository human must review.

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Update and maintain project documentation for local code changes using multi-agent workflow with tech-writer agents. Covers docs/, READMEs, JSDoc, and API documentation.

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Display the current state of the FPF knowledge base

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