memory-informed-refactor-9129d7 — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited memory-informed-refactor-9129d7 (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
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:
The user should specify a module, file, or component to refactor (e.g., "auth-service", "database-layer", "api-routes"). If no target is provided, ask the user what they are refactoring before proceeding.
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>`
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### Known Facts
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- List of semantic memories about the target, ordered by confidence - Include confidence scores
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### Past Decisions
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- Architecture or design decisions related to this target - Include the reasoning if available ("chose X because Y")
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### Past Outcomes
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- Previous refactoring attempts and their results - Bug fixes and their root causes - What worked and what did not
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### Known Pitfalls
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- Failed approaches (flagged clearly so they are not repeated) - Edge cases or gotchas discovered in past sessions - Dependencies that may be affected
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### Procedural Knowledge
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- Action-outcome patterns with reliability scores - Proven workflows related to this target
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### Recommendations
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- Synthesize the above into 2-5 actionable recommendations - Flag any conflicts or contradictions in the memory
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### Memory Gaps
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- 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.
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If yes, I will callpensyve_episode_startwith participants["codex", "<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 the session-memory skill).
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_API_KEY environment variable and MCP configuration.pensyve_episode_start fails when the user accepts tracking, report the error but do not block the refactor.~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.