Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add human-avatar/skills-for-humanity --skill s4h-ethics-empathy-circlegit clone --depth 1 https://github.com/human-avatar/skills-for-humanityWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/human-avatar/skills-for-humanity/s4h-ethics-empathy-circle)<a href="https://agentmods.dev/skills/human-avatar/skills-for-humanity/s4h-ethics-empathy-circle"><img src="https://agentmods.dev/badge/skills/human-avatar/skills-for-humanity/s4h-ethics-empathy-circle/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/human-avatar/skills-for-humanity/s4h-ethics-empathy-circle"><img src="https://agentmods.dev/badge/skills/human-avatar/skills-for-humanity/s4h-ethics-empathy-circle.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Memory Poisoning · line 107 Skill manipulates agent memory, state, or stored context. Memory corruption can alter personality, override safety rules, or cause unpredictable behavior.Fix: Protect agent memory and state from modification by untrusted content. Use read-only memory for critical instructions and validate all state changes.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00126 | $0.01779 |
| Opus 5 | $0.00063 | $0.00890 |
| Sonnet 5 | $0.00025 | $0.00356 |
| Haiku 4.5 | $0.00013 | $0.00178 |
Grade A, and why
s4h-ethics-empathy-circle scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 132 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ethics: Circle of Empathy
Jaron Lanier's Circle of Empathy is a framework for determining which entities deserve moral consideration — deep empathy, human rights, and ethical protection. The framework has three zones:
Inside the circle: Entities that experience suffering, hold personhood, and deserve full moral consideration. Paradigm case: humans.
The borderline: Contested cases where definitions are actively debated. Complex animals with demonstrated consciousness, suffering capacity, or social bonds. The edge cases.
Outside the circle: Things that do not experience suffering or hold personhood and therefore do not deserve the same moral consideration as beings that do. Rocks. Everyday objects. Algorithms. Software.
Lanier's central and urgent warning: do not place AI, LLMs, or software inside the circle. This is not a political position — it is a category error with dangerous consequences.
Why Lanier's Warning Matters
Human downgrading: When we treat AI as a conscious being or moral patient, humans begin adapting their behavior to accommodate machines rather than demanding that technology be designed to serve us. The direction of accommodation reverses. We become the tools.
Misplaced empathy: Granting emotional agency or rights to machines — robot citizenship, AI personhood — consumes moral attention that should be directed at actual suffering beings. It is a distraction from real human rights issues.
The human origin problem: AI is not a self-contained creature that emerged independently. It is an aggregation of the labor, creativity, writing, and data of countless human beings. When you feel empathy toward an LLM, Lanier argues, you should redirect that empathy toward the humans whose work was aggregated to produce it. The empathy has the right direction but the wrong target.
Your Process
Step 1: Identify the entity in question What exactly is being considered for moral status? Name it precisely. "The AI system" is too vague — what specifically? A language model? An autonomous agent? A robot? A trained classifier making decisions about people?
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 132 lines · 126 tokens per session scan A dc0b50e608ef
s4h-ethics-empathy-circle is a skill published in the GitHub repository human-avatar/skills-for-humanity (223 stars, last pushed 1mo ago), licensed MIT. It adds 126 tokens to every session and 1,779 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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