| name | context-packager |
| description | 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. |
When to use
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.
Process
- Identify required context layers — use
references/context_layering_guide.md to decide which layers are needed: task definition, business context, data schema, prior findings, constraints, and output format.
- Collect and deduplicate sources — run
scripts/context_bundler.py to merge multiple context files into a single structured bundle; it deduplicates and applies the layering order.
- Check token budget — run
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).
- Score context quality — evaluate the bundle against
references/context_quality_rubric.md; a good bundle scores ≥ 7/10 on completeness, clarity, and relevance.
- Write the prompt header — prepend a clear task statement to the bundle: what you need, what output format you expect, and any hard constraints.
- Save the package — store the bundle using
assets/context_package_template.md so it can be reused or updated for follow-up sessions.
Inputs the skill needs
- Task description (what you want the AI to do)
- List of context source files or snippets (schema docs, prior reports, business definitions)
- Token budget (default: 100k tokens)
Output
- Merged context bundle (single text file)
- Token count estimate
- Context quality score
- Ready-to-use prompt with task header (
context_package_template.md)
