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 yogsoth-ai/de-anthropocentric-research-engine --skill domain-level-judgmentgit clone --depth 1 https://github.com/yogsoth-ai/de-anthropocentric-research-engineWrote 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/yogsoth-ai/de-anthropocentric-research-engine/domain-level-judgment)<a href="https://agentmods.dev/skills/yogsoth-ai/de-anthropocentric-research-engine/domain-level-judgment"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/de-anthropocentric-research-engine/domain-level-judgment/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/yogsoth-ai/de-anthropocentric-research-engine/domain-level-judgment"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/de-anthropocentric-research-engine/domain-level-judgment.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00114 | $0.00455 |
| Opus 5 | $0.00057 | $0.00228 |
| Sonnet 5 | $0.00023 | $0.00091 |
| Haiku 4.5 | $0.00011 | $0.00046 |
Grade A, and why
domain-level-judgment 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 11d 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.
What it actually says
Domain Level Judgment
Signalling answers → domain-level judgments via each tool's own lookup rules. QUADAS-2's dual-axis output (risk-of-bias + applicability-concern per domain) terminates here — no further rollup exists for it. RoB2/ROBINS-I continue to worst-case-lookup.
Execution
Subagent — spawned via spawn-agent skill.
Why This SOP Exists (coverage-audit S4)
The original graph connected signalling-question-answering directly to an overall-judgment node, with no place for the first-level domain rollup RoB2/ROBINS-I/QUADAS-2 all define algorithmically before any overall verdict — and no place at all for QUADAS-2's terminal dual-axis output, since QUADAS-2 never reaches a "worst case across domains" step the way RoB2/ROBINS-I do.
Available SOPs
| SOP | When to use |
|---|---|
| spawn-agent | Spawn a customized CC subagent with full MCP tool access. |
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 11d ago First seen · 37 lines · 114 tokens per session scan A d8320bcadf3a
domain-level-judgment is a skill published in the GitHub repository yogsoth-ai/de-anthropocentric-research-engine (456 stars, last pushed yesterday), licensed Apache-2.0. It adds 114 tokens to every session and 455 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 2026-08-30.
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