900+ pure-markdown skills for autonomous AI research, organized as 9 freely-composable packages over a 4-layer hierarchy (Campaign → Strategy → Tactic → SOP). Non-linear orchestration with backtracking, 6 MCP integrations. The AI is the researcher — you set the direction.
- ✓Open-source license (Apache-2.0)
- ✓Actively maintained (<30d)
- ✓Healthy fork ratio
- ✓Clear description
- ✓Topics declared
git clone https://github.com/yogsoth-ai/de-anthropocentric-research-engine && cp de-anthropocentric-research-engine/*.md ~/.claude/agents/24 items in this repository
Experiment-specific - summarize the DARE executor's research design into a clean research_result report, forced to write back into the spec file produced by formated-specs.
Experiment-specific - replaces writing-specs, emits DARE's 4-layer call plan as a clean research_graph schema. Last step forces load formated-result.
loss-1 judge - read a sample's full dialogue and decide whether the user simulator semantically enacted its Policy Card. check-blind.
loss-2 judge - pairwise quality comparison across the n rungs within one topic; decide monotonicity and endpoint separation. check-blind, D1-D5 only.
Strategy: Inference to the best explanation in the face of anomalies
Remove components one by one, observe system changes to reveal hidden
Map system architecture to ablatable units for ablation studies
Remove components one by one from a system, record the response/impact
Classify assumptions on 2 axes — load-bearing (how much conclusion depends
Extract abstract principles from concrete domain cases. Strips domain-specific
Move between concrete and abstract framings — 3 levels up (Why?) and
Abstract biological principle to design principle. Bridge from biology
Compute Risk Priority Number (RPN = S x O x D), classify failure modes
Iteratively select maximally informative pairs, execute comparisons,
Structured debate protocol that constructs an advocate, deploys critic
Strategy: Progressive pressure escalation — starts with surface-level
Strategy: Role-play attacks from hostile personas — competing lab researcher,
Tactic: Construct detailed hostile persona, attack artifact from that
Campaign: Logical extreme and boundary testing via reductio ad absurdum
Construct the strongest possible case for a rejected candidate or counter-position.
Subagents overview
What people ask about de-anthropocentric-research-engine
What is yogsoth-ai/de-anthropocentric-research-engine?
+
yogsoth-ai/de-anthropocentric-research-engine is subagents for the Claude AI ecosystem. 900+ pure-markdown skills for autonomous AI research, organized as 9 freely-composable packages over a 4-layer hierarchy (Campaign → Strategy → Tactic → SOP). Non-linear orchestration with backtracking, 6 MCP integrations. The AI is the researcher — you set the direction. It has 389 GitHub stars and was last updated 18d ago.
How do I install de-anthropocentric-research-engine?
+
You can install de-anthropocentric-research-engine by cloning the repository (https://github.com/yogsoth-ai/de-anthropocentric-research-engine) or following the README instructions on GitHub. ClaudeWave also provides quick install blocks on this page.
Is yogsoth-ai/de-anthropocentric-research-engine safe to use?
+
Our security agent has analyzed yogsoth-ai/de-anthropocentric-research-engine and assigned a Trust Score of 97/100 (tier: Verified). See the full breakdown of passed checks and flags on this page.
Who maintains yogsoth-ai/de-anthropocentric-research-engine?
+
yogsoth-ai/de-anthropocentric-research-engine is maintained by yogsoth-ai. The last recorded GitHub activity is from 18d ago, with 5 open issues.
Are there alternatives to de-anthropocentric-research-engine?
+
Yes. On ClaudeWave you can browse similar subagents at /categories/agents, sorted by popularity or recent activity.
Deploy de-anthropocentric-research-engine to your cloud
Ship this repo to production in minutes. Each platform spins up its own environment with editable env vars.
Maintain this repo? Add a badge to your README
Drop the badge into your GitHub README to show it's tracked on ClaudeWave. Each badge links back to this page and reflects the live Trust Score.
[](https://claudewave.com/repo/yogsoth-ai-de-anthropocentric-research-engine)<a href="https://claudewave.com/repo/yogsoth-ai-de-anthropocentric-research-engine"><img src="https://claudewave.com/api/badge/yogsoth-ai-de-anthropocentric-research-engine" alt="Featured on ClaudeWave: yogsoth-ai/de-anthropocentric-research-engine" width="320" height="64" /></a>More Subagents
The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.
The agent that grows with you
Java 面试 & 后端通用面试指南，覆盖计算机基础、数据库、分布式、高并发、系统设计与 AI 应用开发
Build Agentic workflows, RAG pipelines, with rich AI model and tool support on one collaborative workspace. Deploy on cloud, VPC, or self-hosted, so teams move from prototype to production without rebuilding the stack.
The agent engineering platform.
Turn any codebase, with its docs, SQL schemas, configs, and PDFs, into a queryable knowledge graph. A /graphify skill for Claude Code, Cursor, Codex, and Gemini CLI: local deterministic AST parsing, every edge explained, no vector store.