memory-informed-design — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited memory-informed-design (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.
Design flow with memory baked in as a non-optional reflex.
Identify the relevant entity/entities (service, module, subsystem under design) per skills/shared/entity-detection.md.
Call pensyve_recall:
query: a short description of the design question, including secondary entity names where known (e.g., "auth-service jwt signing rs256 hs256")entity: primary detected entitytypes: ["semantic", "episodic"] (design benefits most from durable decisions and prior decision-contexts)limit: 5Secondary entities are folded into the query string since the MCP server scopes results by single primary entity only.
Surface one line: Recalled N prior decisions on <entity>. (Skip if N=0.)
Shape your recommendation using the recalled decisions. If the user's current question directly contradicts a prior decision, flag it:
Prior decision on <entity> (confidence 0.9): [decision]. Are we revisiting this, or does the current question differ?When the user accepts a design or states a decision ("let's go with X", "we'll use Y"), call the memory reflex:
pensyve_remember with entity: <primary_entity>, fact: "[proactive/in-flight/tier-1] <decision text>", confidence 0.9.pensyve_observe with episode_id: <session episode_id>, source_entity: "claude-code", about_entity: <primary_entity>, content: "[proactive/in-flight/tier-1] <decision context: alternatives considered, what tipped the balance>", content_type: "text". This enables future "why did we decide X?" queries.Surface one line: ↳ captured decision on <entity>: <short>.
If the design process revealed a reusable way to evaluate similar decisions (e.g., "run spike vs. prototype", "check latency first"), capture it as procedural: pensyve_observe with episode_id: <session episode_id>, source_entity: "claude-code", about_entity: <primary_entity>, content: "[procedural] [proactive/in-flight/tier-1] trigger=..., action=..., outcome=...", content_type: "text".
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.