/causal-chain-query
SOP for tracing causal chains — follow edges from cause to effect through
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill causal-chain-query --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
/causal-chain-query
Context preview
The summary Claude sees to decide when to auto-load this skill.
SOP for tracing causal chains — follow edges from cause to effect through
SKILL.md
causal-chain-query.SKILL.mdname: causal-chain-query description: SOP for tracing causal chains — follow edges from cause to effect through intermediate variables. execution: sop
Causal Chain Query
Trace a causal chain from a starting variable through intermediate variables to downstream effects.
Tool
`vault_query_graph`
Protocol
1. Call vault_query_graph with starting node, direction=out, depth=3 2. Filter results to only causal edges (derived_from, component_of) 3. Reconstruct the chain as an ordered sequence 4. Identify branching points (one cause → multiple effects) 5. Check for cycles (indicates feedback loop)
HARD-GATE
<HARD-GATE> Must report the full chain, not just endpoints. Include intermediate variables. </HARD-GATE>
Yield
Returns: `{ chain: string[], branches: number, has_cycle: boolean }`
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