AI Context

Use this skill when the user needs AI context docs organized so important context can be captured, found, and updated without implying hidden memory or invisible indexed state.

Start By Clarifying

• What information deserves durable capture versus short-lived discussion.
• How readers should locate the right context later.
• Which taxonomy, tags, or summary conventions will keep retrieval manageable.
• What freshness signals or ownership rules prevent silent decay.
• Where the boundaries are between documented knowledge and inferred context.

Workflow

1. Define the knowledge objects, categories, and retrieval paths readers will use.
2. Choose a structure for summaries, tags, links, and source references.
3. Document what should be captured, what should be omitted, and how updates happen.
4. Add examples of good entries, summaries, or indexing conventions.
5. Review for taxonomy sprawl, stale knowledge, and unclear source-of-truth rules.

Good Output

• Knowledge structure or taxonomy recommendation.
• Rules for summaries, tags, references, and freshness.
• Ambiguities where context capture could become misleading or too broad.
• Maintenance guidance so the knowledge system stays usable.

Common Pitfalls

• Documenting everything and creating an unsearchable memory dump.
• Using vague labels that do not help future retrieval.
• Failing to separate verified facts from interpretation or working notes.
• Implying persistent memory or indexing capabilities that are not actually present.

Boundaries

• Do not imply undocumented retrieval or memory systems exist.
• Prefer explicit taxonomy and source references over invisible context magic.