cass-memory — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited cass-memory (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.
Core Capability: Transforms scattered agent sessions into persistent, cross-agent procedural memory. A pattern discovered in Cursor automatically helps Claude Code on the next session.
cm is an upstream (Dicklesworthstone) tool and is self-describing — discover its command surface from cm --help (and per-subcommand --help), not from this skill. Full catalog snapshot: COMMANDS.md. This skill carries only the AgentOps operating doctrine: the session protocol, feedback discipline, and boundaries.
Architecture in one line: episodic memory (cass session logs) → working memory (diary summaries) → procedural memory (playbook rules with confidence tracking and decay). Full model: ARCHITECTURE.md.
Before starting this task, run:
cm context "<task description>" --json
Read the output carefully:
- relevantBullets: Rules from playbook scored by relevance
- antiPatterns: Things that have caused problems before
- historySnippets: Past sessions (yours and other agents')
- suggestedCassQueries: Deeper investigation if needed
Reference rule IDs when following them (e.g., "Following b-8f3a2c...")cm context "<task>" --json is THE ONE COMMAND — everything else is optional. Budget flags (--limit, --min-score, --no-history) exist when context is tight; see cm context --help.
1. START: cm context "<task>" --json
2. WORK: Reference rule IDs when following them
3. FEEDBACK: Leave inline comments when rules help/hurt
4. END: Just finish. Learning happens automatically.You do NOT need to:
cm reflect (automation handles this)cm mark manually (use inline comments)# When a rule helped / caused problems
cm mark b-8f3a2c --helpful
cm mark b-xyz789 --harmful --reason "Caused regression"
# Or leave inline comments (parsed during reflection)
// [cass: helpful b-8f3a2c] - this saved me from a rabbit hole
// [cass: harmful b-x7k9p1] - wrong for our use caseWhy feedback matters: rules aren't immortal. Confidence halves every 90 days without revalidation, one harmful mark counts 4x a helpful one, and repeatedly-harmful rules are inverted into explicit anti-pattern warnings rather than deleted. Skipping feedback starves the decay model.
cm guard --install / --git / --status installs hooks that block known-dangerous commands; cm trauma add / cm trauma scan manage the pattern set. Doctrine, scope, and pattern design: TRAUMA-GUARD.md.
cm reflection to shell out to claude -p (e.g. provider: cli) — that path is forbidden on this fleet. Use a compliant provider, or rely on deterministic reflection (cm degrades gracefully without an LLM).cm doctor --json first when anything misbehaves; cm doctor --fix for a corrupt playbook. If cass is missing, cm still works playbook-only (no history).| Topic | Reference |
|---|---|
| Full command reference | COMMANDS.md |
| Cognitive architecture | ARCHITECTURE.md |
| Trauma guard system | TRAUMA-GUARD.md |
| MCP server integration | MCP-SERVER.md |
| Onboarding workflow | ONBOARDING.md |
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.