tetra-scar

Scar memory, reflex arc, and decision traces for AI agents. Learn from failures permanently. Block repeated mistakes instantly — no LLM calls needed. Three-l...

This package implements a local scar/reflex memory and the code mostly matches that purpose — it stores JSONL scars, does pattern matching, and offers a 4-axis check. Before installing or enabling it, check/consider: - Provenance: files reference multiple authors/owners (b-button-corp, aibenyclaude-coder, and the anonymous owner ID). Verify the repository origin and prefer code from a trusted source. - Missing referenced components: examples and CI scripts call or import external scripts (tetra-scar-code-review/scar_code_review.py, scar_safety, tetra-scar-safety) that are not included. If you run those examples or wire the action as-is, you may end up executing external code—locate and inspect those referenced projects first. - Remote clone behavior: scar_audit.py can clone arbitrary GitHub repos when used with --repo. Cloning itself is expected for an audit tool, but avoid running the script on untrusted repos or in privileged CI runners without review. - Local persistence: scars are append-only JSONL files written to disk. Confirm the memory_dir path is acceptable and not pointing to sensitive locations. If you plan to use this skill: run the included tests locally (pytest), inspect the referenced external scripts before using the CI examples, and verify the action's owner/URL if you use it in CI. The inconsistencies suggest sloppy packaging rather than overt maliciousness, but manual review is recommended before trusting it in production.

Type: OpenClaw Skill Name: tetra-scar Version: 0.4.0 The tetra-scar skill bundle is a safety and memory framework for AI agents designed to prevent repetitive failures through 'scar memory' (immutable failure records) and a 'reflex arc' (keyword-based pattern matching). The core logic in tetra_scar.py and the auditing tool scar_audit.py focus on identifying and blocking dangerous operations (e.g., 'rm -rf', SQL injection patterns, hardcoded secrets) without external API calls. The instructions in SKILL.md and README.md are aligned with the stated purpose of improving agent reliability, and no evidence of data exfiltration, malicious execution, or prompt injection was found.