ladder-quality-order

ladder-quality-order is a skill for Claude Code, Codex from yogsoth-ai/de-anthropocentric-research-engine. It costs 66 tokens per session (731 once invoked), scanned A, original, Apache-2.0.

A judging task that compares six shuffled research-and-design samples about one topic and ranks them by quality using five defined criteria.

In plain words
What is it for?
Use it to assess whether research is meaningful, useful for skill design, usable by the DARE engine, consistent with its four-layer structure, and based on sound prerequisites.
Why use it?
It provides consistent pair-by-pair judgments without requiring the evaluator to guess hidden scores or compare the samples with academic publishing standards.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: mentions Codex.

Good fit Use it to assess whether research is meaningful, useful for skill design, usable by the DARE engine, consistent with its four-layer structure, and based on sound prerequisites.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/yogsoth-ai/de-anthropocentric-research-engine/ladder-quality-order
Install

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.

Any agent
npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill ladder-quality-order
Clone the repo
git clone --depth 1 https://github.com/yogsoth-ai/de-anthropocentric-research-engine

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for ladder-quality-order

README.md
[![agentmods](https://agentmods.dev/badge/skills/yogsoth-ai/de-anthropocentric-research-engine/ladder-quality-order/github.svg)](https://agentmods.dev/skills/yogsoth-ai/de-anthropocentric-research-engine/ladder-quality-order)
Your own site
<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.

agentmods 80×15 button for ladder-quality-order

Your own site · 80×15
<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>
Per session 66 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 731 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 11d ago against content hash 189de186f0b8, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

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.

ladder-foundry/skills/ladder-quality-order/SKILL.md · 59 lines

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.

Read the full file on GitHub · 59 lines

Files

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.

Changes

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.

  1. 11d ago First seen · 59 lines · 66 tokens per session scan A 189de186f0b8

Subscribe to this mod's changes

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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