Search archived project context by keyword and load specific entries.
npx skills add https://github.com/lalulali/vespyr --skill memory-lalulali
Please help me install this Agent Skill. Skill: memory Source: https://github.com/lalulali/vespyr/tree/main/.agents/skills/memory Command: npx skills add https://github.com/lalulali/vespyr --skill memory-lalulali
This Skill helps you find historical project context that has been compacted out of active memory, so you can recover past decisions, lessons, and patterns without digging through old files manually.
Ask the memory skill to search for the relevant historical decision or lesson in the project archive.
High-intent search queries and answers about installing and using this skill.
To recover past architecture decisions, use keyword-based archive search to retrieve compacted historical project context. The system applies relevance ranking to match your query with archived entries, allowing you to locate specific architecture records without manual file digging.
The best way to find historical project context is performing a keyword-based archive search with relevance ranking. This retrieves compacted lessons and patterns from past engineering workflows, helping you identify and optionally load the specific archived entries you need.
Keyword-based archive search works by querying an index of compacted historical context and applying relevance ranking to the results. Once the search identifies matching historical records, you can optionally load the specific archived entry to review the full decision or lesson.
Yes, you can retrieve specific architecture records using just a few keywords. The archive search applies relevance ranking to match your keywords against compacted historical context, returning ranked results that point you to the exact archived entry for optional loading.
Yes, this skill supports retrieving lessons across both product and engineering workflows. It applies keyword-based archive search to recover historical project context, including past decisions, lessons, and patterns documented throughout the project lifecycle.
A limitation of relying on compacted historical context is that search results depend entirely on the keywords provided. If the archive index lacks the specific terms or if context was overly compacted, relevance ranking may fail to surface the correct historical record.
dtbuchholzSearch project memory, skills, and documentation using semantic and keyword queries.
raydocsSearch .flow/memory files for relevant pitfalls, conventions, and decisions.
nexus-a1Archive completed requirements into a git-backed knowledge base with a searchable index.
YehudaFrankelSearch read-only memory index for notes, decisions, and lessons by topic.
