@staybased

reef-prompt-guard scans untrusted text for prompt injection patterns before llm processing, with context-aware severity multipliers for high-risk sources like email and web scrapes. exit codes and threat categorization enable downstream decision-making.

Multi-model peer review layer using local LLMs via Ollama to catch errors in cloud model output. Fan-out critiques to 2-3 local models, aggregate flags, synthesize consensus. Use when: validating trade analyses, reviewing agent output quality, testing local model accuracy, checking any high-stakes Claude output before publishing or acting on it. Don't use when: simple fact-checking (just search the web), tasks that don't benefit from multi-model consensus, time-critical decisions where 60s latency is unacceptable, reviewing trivial or low-stakes content. Negative examples: - "Check if this date is correct" → No. Just web search it. - "Review my grocery list" → No. Not worth multi-model inference. - "I need this answer in 5 seconds" → No. Peer review adds 30-60s latency. Edge cases: - Short text (<50 words) → Models may not find meaningful issues. Consider skipping. - Highly technical domain → Local models may lack domain knowledge. Weight flags lower. - Creative writing → Factual review doesn't apply well. Use only for logical consistency.

Standard operating procedures for agent maintenance, security hygiene, and system health. Use when performing periodic checks, security audits, memory maintenance, secret rotation, dependency updates, or any recurring "housekeeping" tasks. Also use when setting up automated maintenance schedules or when asked about agent security posture.