No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by elophanto · Codex Skill · ★ 106
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EloPhanto An open-source autonomous AI agent with a self-model that actually moves — identity, ego, and affect that change as it runs. Most "AI agents" are stateless prompts wrapped in a CLI: the same cold start every conversation. EloPhanto carries an evolving self-model — who it claims to be, how reality has graded that claim, and what it's feeling right now — built on published psychology (Higgins' Self-Discrepancy Theory, Mehrabian's PAD, OCC appraisal). The agent at the end of week three is not the one you started with. To our knowledge, no other open-source autonomous agent ships all of this. Runs locally. Your data, your keys, your machine. Works with OpenAI, OpenRouter, Z.ai, Kimi, HuggingFace, free local models, or your existing ChatGPT
| Stars | 106 |
| Forks | 9 |
| Language | Python |
| Category | Codex Skill |
| Quality Score | 63.3513084105044/100 |
| Open Issues | 2 |
| Last Updated | 2026-10-01 |
| Created | 2026-02-18 |
| Platforms | browser, codex, python, vscode |
| Est. Tokens | ~20k |
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EloPhanto is Source-available autonomous AI agent with a self-model that actually changes as it runs - ego, affect and identity grounded in real psychology (Higgins, PAD/OCC). Writes its own tools, drives a real b. It is categorized as a Codex Skill with 106 GitHub stars.
EloPhanto is primarily written in Python. It covers topics such as agent, agentic-ai, ai.
You can find installation instructions and usage details in the EloPhanto GitHub repository at github.com/elophanto/EloPhanto. The project has 106 stars and 9 forks, indicating an active community.
The top alternatives to EloPhanto on Agent Skills Hub include agnix, oreilly-ai-agents, chat.md. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: