/blend-construction
Construct complete 4-space blends with emergent structure. Orchestrates
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill blend-construction --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/blend-construction
Context preview
The summary Claude sees to decide when to auto-load this skill.
Construct complete 4-space blends with emergent structure. Orchestrates
SKILL.md
blend-construction.SKILL.mdname: blend-construction
description: Construct complete 4-space blends with emergent structure. Orchestrates
input-space-construction → generic-space-extraction → blend-composition.
execution: tactic
Blend Construction
Construct complete 4-space blends with emergent structure following the Fauconnier-Turner conceptual integration network model.
Stages
Stage 1: Input Space Construction
Build rich input spaces for both source concepts using input-space-construction SOP. Each space must include elements, relations, attributes, and internal logic.
Stage 2: Generic Space Extraction
Extract the shared abstract structure from both input spaces using generic-space-extraction SOP. The generic space captures what the two inputs have in common at the most abstract level.
Stage 3: Blend Composition
Compose the blended space by selectively projecting structure from both inputs and creating new connections using blend-composition SOP. The blend must develop emergent structure not present in either input.
Minimum Yield
| Metric | Floor | |--------|-------| | Complete 4-space blends | ≥2 | | Emergent structures per blend | ≥1 | | Vital relations compressed | ≥3 per blend | | Novel connections in blend | ≥2 per blend |
Available SOPs
| SOP | Role | |-----|------| | input-space-construction | Stage 1 — build input spaces | | generic-space-extraction | Stage 2 — extract shared structure | | blend-composition | Stage 3 — compose blended space | | blend-completion | Post-Stage 3 — recruit background knowledge | | vital-relation-mapping | Pre-Stage 1 — map vital relations to guide projection |
Read more
name: blend-construction description: Construct complete 4-space blends with emergent structure. Orchestrates input-space-construction → generic-space-extraction → blend-composition. execution: tactic
Blend Construction
Construct complete 4-space blends with emergent structure following the Fauconnier-Turner conceptual integration network model.
Stages
Stage 1: Input Space Construction
Build rich input spaces for both source concepts using input-space-construction SOP. Each space must include elements, relations, attributes, and internal logic.
Stage 2: Generic Space Extraction
Extract the shared abstract structure from both input spaces using generic-space-extraction SOP. The generic space captures what the two inputs have in common at the most abstract level.
Stage 3: Blend Composition
Compose the blended space by selectively projecting structure from both inputs and creating new connections using blend-composition SOP. The blend must develop emergent structure not present in either input.
Minimum Yield
| Metric | Floor | |--------|-------| | Complete 4-space blends | ≥2 | | Emergent structures per blend | ≥1 | | Vital relations compressed | ≥3 per blend | | Novel connections in blend | ≥2 per blend |
Available SOPs
| SOP | Role | |-----|------| | input-space-construction | Stage 1 — build input spaces | | generic-space-extraction | Stage 2 — extract shared structure | | blend-composition | Stage 3 — compose blended space | | blend-completion | Post-Stage 3 — recruit background knowledge | | vital-relation-mapping | Pre-Stage 1 — map vital relations to guide projection |
The complete research orchestration system for AI-native science. What It Does Design Philosophy Architecture (v3.2.2) Quick Start Configuration Roadmap License DARE is not a tool that helps you do research. It is the researcher.
Repo: yogsoth-ai/de-anthropocentric-research-engine
Other skills on de-anthropocentric-research-engine.
- /formated-results
Closing skill for the research-executor, loaded as the last step of formated-specs. Summarize the design just produced into one research-result JSON fenced block in your reply. Do not execute the research.
Open skill - /formated-specs
Spec-slot skill for the research-executor. Emit the 4-layer DARE orchestration of the assigned topic as one research-graph JSON fenced block in your reply. Replaces the generic spec-writing step.
Open skill - /injection-fidelity
Loss-1 judge (codex role). Given one sample's de-identified dialogue and its PolicyCard, decide axis-by-axis whether the user-simulator enacted the card's per-axis pressure. Judge enactment of the card, never whether the research is good.
Open skill - /ladder-quality-order
Loss-2 judge (codex role). Over one topic's 6 shuffled research-design samples, pairwise-rank by quality using the D1–D5 standard. Emit the pairwise log; the harness computes the order and the ladder verdicts. Judge quality difference, never against academic standards.
Open skill - /optimization-loop
The optimizer brain for the ladder-foundry pretraining loop. Runs the two-level nested batch loop, delegates gating to gate_eval, attributes a failing batch to one weight (attribute-first), and recovers from disk after compaction. Control flow is fully scripted; only the
Open skill - /acu-nugget-recall
Tactic: Extract atomic units from one paper and score how much of a caller-supplied summary covers. Use for ACU-style binary or Nugget-style ternary recall checks; cannot run without a target summary.
Open skill