.claude/skills/context-compressionRuntime, accounts, dependencies, permissions, network behavior and task quality remain untested.
This skill should be used when the user asks to "compress context", "summarize conversation history", "implement compaction", "reduce token usage", or mentions context compression, structured summarization, tokens-per-task optimization, or long-running agent sessions exceeding context limits.
These states come from the source or distribution context. None of the entries below are SkillVetAI compatibility test results.
These checks parse the fixed package against dated platform rules. They do not execute the Skill or verify task behavior.
.claude/skills/context-compressionRuntime, accounts, dependencies, permissions, network behavior and task quality remain untested.
.agents/skills/context-compressionRuntime, accounts, dependencies, permissions, network behavior and task quality remain untested.
skills/context-compressionRuntime, accounts, dependencies, permissions, network behavior and task quality remain untested.
This command is recorded from the source ecosystem and resolves the registry's latest release. The fixed release shown on this page should be inspected before adoption.
clawhub install @leoyessi10-tech/context-engineeringclawhub inspect @leoyessi10-tech/context-engineering --version 1.0.0This automated, non-executing scan is bound to this release hash. It is not a safety certification and may contain false positives or false negatives.
This is registry-supplied evidence for the recorded release, not an independent SkillVetAI scan. Check the canonical source for the full report, scanner versions, scope, and current moderation state.
The catalog stores hashes and an inventory summary for change detection. It does not republish the package contents.
sha256:c1a0f33421dcec74c0a874d367fa9a7c3187aec640cf049f5aed207abdfad848references/evaluation-framework.mdscripts/compression_evaluator.pyskill-card.mdSKILL.md- Initial release of the context-engineering-collection skill. - Provides comprehensive guidance for building, optimizing, and debugging AI agent systems focused on effective context management. - Covers foundational concepts, architectural patterns (multi-agent, memory systems, tool design), operational techniques (compression, optimization, evaluation), and project development methodology. - Includes skill references for modular use and integration within any platform or agent framework. - Offers practical, platform-agnostic advice and links to further internal and external resources.