Efficiently package context for AI-assisted analysis. Use when preparing to work with Claude on analysis, organizing context documents, or structuring prompts for complex analytical tasks.
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
Before starting an AI-assisted analysis session when the task requires more than a single prompt — complex investigations, multi-step analyses, or work that depends on project-specific knowledge. A well-packaged context bundle reduces back-and-forth and produces better first responses.
references/context_layering_guide.md to decide which layers are needed: task definition, business context, data schema, prior findings, constraints, and output format.scripts/context_bundler.py to merge multiple context files into a single structured bundle; it deduplicates and applies the layering order.scripts/token_counter.py on the bundle to estimate token count; trim lower-priority layers if over budget (see references/context_layering_guide.md for trimming priority).references/context_quality_rubric.md; a good bundle scores ≥ 7/10 on completeness, clarity, and relevance.assets/context_package_template.md so it can be reused or updated for follow-up sessions.context_package_template.md)还没有评价。欢迎分享你的使用感受。
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Rigorous A/B test statistical analysis. Use when analyzing experiment results, calculating statistical significance, checking for sample ratio mismatch, or validating test design before launch.
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Track and document analytical assumptions and decisions. Use when making analytical choices, documenting trade-offs, ensuring transparency, or creating audit trails for analytical work.
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Structured, reproducible analysis documentation. Use when documenting analysis findings, creating analysis notebooks, ensuring reproducibility, or building analysis archives for future reference.
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Structure analysis approach before starting work. Use when receiving new analysis requests, breaking down complex questions into steps, or planning iterative analysis workflows.
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Pre-delivery quality assurance for analysis work. Use when reviewing analysis before sharing with stakeholders, checking for completeness, validating assumptions, or ensuring clarity of recommendations.
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Post-analysis learning and process improvement. Use when completing major analysis projects, documenting lessons learned, or improving team analytical practices.
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