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From pensyve
Queries Pensyve memory for past decisions, failures, pitfalls, and patterns on a refactoring target, compiling a structured briefing. Use before refactors to avoid mistakes.
npx claudepluginhub major7apps/pensyve --plugin pensyveHow this skill is triggered — by the user, by Claude, or both
Slash command
/pensyve:memory-informed-refactorThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Load historical context from Pensyve memory before starting a refactor. Surfaces past decisions, failed approaches, known pitfalls, and relevant patterns to avoid repeating mistakes.
Leverages fireauto-mem DB to actively search and apply user's accumulated dev knowledge on files, topics, patterns during code mods, bug fixes, new features, or explicit history requests.
Checks memory before reading files when answering questions about code, architecture, or patterns. Reduces token usage and ensures consistency.
Manages persistent memory across Claude Code sessions via AutoMem. Recall project context, architectural decisions, bug fixes, user preferences, and patterns at session start or debugging.
Share bugs, ideas, or general feedback.
Load historical context from Pensyve memory before starting a refactor. Surfaces past decisions, failed approaches, known pitfalls, and relevant patterns to avoid repeating mistakes.
When this skill is invoked with a refactoring target, follow these steps:
Run multiple pensyve_recall queries to gather comprehensive context about the target:
pensyve_recall with query "<target>" (limit: 10)pensyve_recall with query "<target> refactor" (limit: 5)pensyve_recall with query "<target> failed" (limit: 5)pensyve_recall with query "<target> error" (limit: 5)pensyve_recall with query "<target> decided" (limit: 5)pensyve_recall with query "<target> depends" (limit: 5)If the target matches an entity name, also call pensyve_inspect with entity: "<target>" to get the full memory inventory for that entity.
Organize the findings into a structured briefing. Deduplicate results that appear across multiple queries.
Refactor Briefing:
<target>Known Facts
- List of semantic memories about the target, ordered by confidence
- Include confidence scores
Past Decisions
- Architecture or design decisions related to this target
- Include the reasoning if available ("chose X because Y")
Past Outcomes
- Previous refactoring attempts and their results
- Bug fixes and their root causes
- What worked and what did not
Known Pitfalls
- Failed approaches (flagged clearly so they are not repeated)
- Edge cases or gotchas discovered in past sessions
- Dependencies that may be affected
Procedural Knowledge
- Action-outcome patterns with reliability scores
- Proven workflows related to this target
Recommendations
- Synthesize the above into 2-5 actionable recommendations
- Flag any conflicts or contradictions in the memory
Memory Gaps
- Areas where no relevant memories exist
- Suggest what to watch for during the refactor
If no relevant memories are found for a section, omit that section entirely rather than showing an empty one. If no memories are found at all, say so clearly and proceed without historical context.
After presenting the briefing, offer to track the refactor as an episode:
Would you like me to track this refactor as an episode? This will let Pensyve capture the decisions and outcomes from this session for future reference.
If yes, I will call
pensyve_episode_startwith participants["claude-code", "<target>"].
If the user accepts, call pensyve_episode_start. Remind the user to close the episode at the end of the refactor (or suggest using /consolidate or the session-memory skill).
.claude/ memory files. All memory operations go through the Pensyve MCP tools exclusively.pensyve_recall returns errors on some queries, present results from the successful queries and note the failures.pensyve_inspect fails, skip the entity inspection and rely on recall results.pensyve_episode_start fails when the user accepts tracking, report the error but do not block the refactor.