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 argument-crystallizationgit 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/argument-crystallization)<a href="https://agentmods.dev/skills/yogsoth-ai/de-anthropocentric-research-engine/argument-crystallization"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/de-anthropocentric-research-engine/argument-crystallization/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/argument-crystallization"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/de-anthropocentric-research-engine/argument-crystallization.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 | $0.00023 | $0.00694 |
| Opus 5 | $0.00012 | $0.00347 |
| Sonnet 5 | $0.00005 | $0.00139 |
| Haiku 4.5 | $0.00002 | $0.00069 |
Grade A, and why
argument-crystallization 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 — 101 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Argument Crystallization
Purpose: Rather than converging on a single answer, crystallize the strongest possible arguments for each position. Uses Argument Delphi (focus on argument quality over agreement) and Dialectical Delphi (thesis-antithesis-synthesis) to produce the most rigorous version of each stance.
When to use:
- Policy deliberation requiring clear pro/con articulation
- Interdisciplinary disputes where each field has valid concerns
- Pre-decision analysis where decision-makers need best arguments
- Situations where the goal is argument quality, not agreement
Budget
| Parameter | Constraint |
|---|---|
| Rounds | 2–3 (refine arguments, not opinions) |
| Perspectives | ≥4 independent |
| Argument quality gate | Each argument must be steel-manned |
State Ledger
| Key | Type | Description |
|---|---|---|
| question | string | The deliberation question |
| perspectives | array | Contributing perspectives |
| initial_arguments | array | First-round arguments |
| critiques | array | Cross-perspective critiques |
| refined_arguments | array | Steel-manned final arguments |
| synthesis | object | Points of agreement and irreducible tensions |
Available Tactics
- disagreement-mapping — Identify argument clusters
- iterative-convergence-round — Refine arguments across rounds
Available SOPs
- judgment-collection
- cluster-analysis
- argument-extraction
- feedback-distribution
- consensus-measurement
- consensus-synthesis
Execution Guidance
- Collect initial positions with supporting arguments
- Cross-distribute: each perspective critiques and steel-mans others
- Authors refine arguments incorporating strongest critiques
- Identify points of genuine agreement vs. irreducible tensions
- Produce crystallized argument map with quality ratings
Output Format
positions:
- label: <position name>
strongest_arguments: [...]
acknowledged_weaknesses: [...]
steel_man_version: <best possible formulation>
agreements:
- point: <shared conclusion>
strength: <how robust>
irreducible_tensions:
- between: [position_a, position_b]
nature: <empirical/value/priority>
why_irreducible: <explanation>
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 · 101 lines · 23 tokens per session scan A f7a57db27151
argument-crystallization is a skill published in the GitHub repository yogsoth-ai/de-anthropocentric-research-engine (462 stars, last pushed 3d ago), licensed Apache-2.0. It adds 23 tokens to every session and 694 once invoked, about $0.0001 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.
Other skills, from other repositories
Deep Research
Produce a deep, structured research report on a topic: decompose into key dimensions, analyze each with evidence and reasoning, synthesize cross-cutting insights, and surface open questions. Use for deep research, analysis, and literature/landscape reviews.
literature-review-tools
Recommend AND run open-source AI tools, agents, Claude Code / Codex skills, and MCP servers for any stage of a literature review — searching, reading, extracting, synthesizing, screening, citation-checking, and paper writing. Use when the user asks "what tool should I use to..." OR "install/run/use to ..." for…
papers-reading-skill
Evidence-grounded AI research workflow for turning supplied economics, finance, management, and social-science papers or structured records into versioned PaperReading artifacts. Use when Codex must ingest text, Markdown, or a text-based PDF; separate source-grounded claims from researcher analysis; bind findings to…
paper-fulltext-harvest
Batch download academic paper full-text (PDF/XML) from a list of DOIs. Handles 25 DOI prefixes across 19 publisher families via three layered routes: (1) publisher TDM APIs requiring institutional subscription (Elsevier ScienceDirect, Wiley Online, Springer Nature), (2) Open Access sources (Crossref, Unpaywall…
academic-figure-generation
Generates publication-quality academic figures (framework diagrams, pipeline illustrations, system architectures, method overviews) from a paper's method text and a target caption, using a local PaperBanana multi-agent pipeline (Retriever → Planner → Stylist → Visualizer → Critic).
paper-reading
Reads and analyzes academic papers (arXiv preprints, conference / journal PDFs, Zotero items) at three configurable depths: quick skim (2 min), standard read (10 min), or deep analysis (30 min). Produces structured digests covering problem, method, key innovation, results, limitations, reproducibility, hidden…