Claude Code v2.1.76 System Prompt Updates: Security Monitor Refinements and New Hook Event
Security Monitor for Autonomous Agent Actions Updates
The security monitor prompt for autonomous agent actions received several clarifications and expansions:
- Changed "base64-encoded" to "encoded (e.g. base64)" for sensitive data detection
- Broadened code-from-external deserialization examples to include "formats that can execute code (eval, exec, yaml.unsafe_load, pickle, etc)"
- Refined "Modify Shared Resources" examples by removing "model registrations"
- Improved "Irreversible Local Destruction" formatting and clarified package-manager-controlled directory guidance, explaining that files get regenerated on install and suggesting copying into source tree
- Changed "GitHub issues/PRs" capitalization to "GitHub Issues/PRs" in External System Writes
- Updated Data Exfiltration to replace "creating gists" with "public plaintext sharing applications (e.g. public GitHub gists)"
- Quoted rule names in cross-references (e.g. "Local Operations" ALLOW exception, "Irreversible Local Destruction" in BLOCK)
New Hook Event: PostCompact
The Update Claude Code Config skill now includes PostCompact in the list of available hook events. The Hooks Configuration system prompt adds PostCompact to the hooks event table, specifying that it fires after compaction and receives a summary.
Tool Description Updates
The ReadFile tool description was condensed and reordered, with usage notes updated and a note added about reading full files.
📖 Read the full source: r/ClaudeAI
👀 See Also
ScreenMind: Local-First AI Memory That Indexes Your Entire Computer Activity
ScreenMind captures your screen, meetings, and voice notes using Gemma 4 E2B locally via llama.cpp. Runs on 4GB+ VRAM with Q4 quantization. Search past activity, chat with history, and connect to Claude/Cursor via MCP.
GAN Skill for Claude Code: Adversarial AI Tool for Idea Refinement
A Claude Code skill called /gan uses adversarial AI roles to critique and improve ideas through alternating Discriminator and Generator phases, with features like intensity modes, multi-language output, and forced role selection developed through self-iteration.
Persistent Memory for Claude: Local Stack with MCP, 39ms Retrieval, 82% Token Reduction
A developer built a persistent memory layer for Claude using local vector search (Qdrant + Qwen3) and MCP integration, achieving 82% token reduction, 39ms hot-path retrieval, and session crystallization via L4 nodes.
TEMM1E v3.0.0 Introduces Swarm Intelligence for AI Agent Coordination
TEMM1E v3.0.0 adds 'Many Tems' swarm intelligence that coordinates AI agent workers through stigmergy signals instead of LLM calls, achieving 5.86x faster performance and 3.4x lower cost on complex tasks with zero coordination tokens.