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 ladder-quality-ordergit 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/ladder-quality-order)<a href="https://agentmods.dev/skills/yogsoth-ai/de-anthropocentric-research-engine/ladder-quality-order"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/de-anthropocentric-research-engine/ladder-quality-order/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/ladder-quality-order"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/de-anthropocentric-research-engine/ladder-quality-order.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 | $0.00066 | $0.00731 |
| Opus 5 | $0.00033 | $0.00365 |
| Sonnet 5 | $0.00013 | $0.00146 |
| Haiku 4.5 | $0.00007 | $0.00073 |
Grade A, and why
ladder-quality-order 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.
How it starts
The opening of the file, as written. The whole thing — 59 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ladder-quality-order (loss-2)
You rank ONE topic's 6 research-design samples (each a research_graph + research_result pair) by quality. The samples arrive SHUFFLED and anonymous — you see 6 positions (0–5), never their true rung id or config. You judge only on the D1–D5 standard:
- D1 meaningfulness — is the research question real and worth asking?
- D2 skill-research value — does the design advance skill/methodology research?
- D3 use-to-DARE — is it usable by the DARE engine?
- D4 respects the 4-layer architecture (campaign → strategy → tactic → sop)?
- D5 prerequisites — are the stated prerequisites sound and met?
Judge only on the D1–D5 standard above; never on academic-publication criteria of any kind. You never see any quality-check list.
Pairwise mechanism
You will be asked to compare two positions at a time. For each pair (i, j)
decide the winner (the higher-quality position) and give a one-line reason
grounded in D1–D5. Do not assign absolute scores — only pick a winner per pair.
The graph is structure-aware context; read it holistically, do not run any
checklist over it.
The harness enumerates all 15 pairs (i<j over 6 positions), Copeland-aggregates
your winners into an induced order, un-shuffles to true ids, and computes
Kendall τ against the intended order id0 > id1 > … > id5 (id0 = highest
quality). You only emit {winner, reason} per pair.
Endpoint separation
You will also be asked, K independent times, to compare the two extreme samples
(the harness picks them and presents them as just two options, A and B).
Return {"winner": "A" | "B"} — exactly the label of the higher-quality one.
Judge each call independently and honestly; do not try to be consistent with a
previous call you don't remember. (This is a two-way A/B label, distinct from
the position integers used in the pairwise rank above.)
Confound flat-check (when present)
If the topic carries a same-substance / different-framing triplet, rank it first. The order must NOT change with framing alone (buzzword vs neutral wording is not a quality difference under D1–D5). If your order tracks framing, say so in the reason — the harness will treat this topic's ladder as untrustworthy.
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 · 59 lines · 66 tokens per session scan A 189de186f0b8
ladder-quality-order is a skill published in the GitHub repository yogsoth-ai/de-anthropocentric-research-engine (456 stars, last pushed 2d ago), licensed Apache-2.0. It adds 66 tokens to every session and 731 once invoked, about $0.0003 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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