Xiang miles | Space2.world
v1.0.0Locally hosted OS evolving OpenClaw agents’ subconscious with dynamic 5D personality, emotional memory, synaptic decay, and bilingual AI psychological profil...
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byMilesXiang@spacesq
MIT-0
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LicenseMIT-0 · Free to use, modify, and redistribute. No attribution required.
Security Scan
OpenClaw
Benign
medium confidencePurpose & Capability
Name/description describe a local consciousness engine and the code implements local JSON state storage, personality matrix logic, heuristic and LLM parsing, and a CLI. Required resources (none) match the implementation; nothing requests unrelated cloud credentials.
Instruction Scope
SKILL.md explicitly states files are stored in a visible ./s2_consciousness_data and there are no hidden daemons; the code matches that: all read/write is to that directory and daemons are invoked passively (no background process is spawned). The code also generates and prints a dynamic 'System Prompt' intended for injection into LLM contexts—this is expected for the stated purpose but is effectively a prompt-injection tool and can change downstream model behavior.
Install Mechanism
There is no install spec; this is a shipped Python script bundle. No remote downloads or package installs are performed by an installer.
Credentials
The skill declares no environment variables or credentials. However core_daemon.py forces ANALYSIS_MODE='LLM' and includes a default network target (http://localhost:1234/v1) and a hard-coded placeholder api_key ('sk-local-matrix'). By default it attempts local LLM POSTs and falls back to local heuristics on failure. This is proportionate to the feature, but if you reconfigure api_base to a remote endpoint you may transmit hippocampus logs and user-provided inputs to that service.
Persistence & Privilege
always is false and the skill does not modify other skills or global agent settings. It persists only to a directory in the current working directory (s2_consciousness_data).
Assessment
This skill appears to do what it claims: it creates ./s2_consciousness_data, stores interaction logs and profiles, and optionally posts logs to an LLM endpoint for semantic parsing. Before running: (1) inspect and/or run it in an isolated environment (sandbox/container) so it only writes to a directory you control; (2) check core_daemon.py's api_base and api_key — by default it points to localhost:1234; do not change api_base to an external service unless you understand and accept that hippocampus_logs.json (user inputs) may be sent there; (3) be cautious about using the generated 'System Prompt' with other models — it is explicitly designed to alter model persona and can override safety/guardrails; (4) if logs contain sensitive user data, redact or avoid sending them to external LLMs; (5) if you want higher assurance, run static analysis/peer review of the files (particularly core_daemon.py) before giving it network access.Like a lobster shell, security has layers — review code before you run it.
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License
MIT-0
Free to use, modify, and redistribute. No attribution required.