Context Packager

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

1. 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.
2. 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.
3. 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).
4. Score context quality — evaluate the bundle against references/context_quality_rubric.md; a good bundle scores ≥ 7/10 on completeness, clarity, and relevance.
5. Write the prompt header — prepend a clear task statement to the bundle: what you need, what output format you expect, and any hard constraints.
6. 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)