/causal-modeling
Campaign for building causal models — identify variables, map mechanisms,
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill causal-modeling --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-modeling
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Campaign for building causal models — identify variables, map mechanisms,
SKILL.md
causal-modeling.SKILL.mdname: causal-modeling
description: Campaign for building causal models — identify variables, map mechanisms,
collect evidence, analyze interventions, validate models. Produces causal graphs
in the wiki vault.
execution: campaign
dependencies:
strategies:
- evidence-collection
- intervention-analysis
- knowledge-structuring-variable-identification
- mechanism-mapping
- model-validation
tactics:
- counterfactual-reasoning
- evidence-weighing
- feedback-loop-detection
- knowledge-compilation
sops:
- context-checkpoint
- context-init
Causal Modeling
Build causal models for research domains. Identifies variables, maps causal mechanisms, collects supporting evidence, analyzes potential interventions, and validates the resulting causal graph.
Manifest
| Level | Count | Skills | |-------|-------|--------| | Strategy | 5 | variable-identification, mechanism-mapping, evidence-collection, intervention-analysis, model-validation | | Tactic | 3 | counterfactual-reasoning, evidence-weighing, feedback-loop-detection | | SOP | 10 | variable-page-creation, mechanism-edge-creation, evidence-linking, contradiction-flagging, confidence-scoring, intervention-page-creation, loop-documentation, model-gap-detection, causal-chain-query, validation-report |
Budget Table
| Metric | Small | Medium | Large | |--------|-------|--------|-------| | Variables identified | 8 | 20 | 40 | | Causal edges created | 15 | 40 | 80 | | Evidence pages linked | 10 | 30 | 60 | | Interventions analyzed | 2 | 5 | 10 | | Feedback loops documented | 1 | 3 | 6 |
Strategy Sequence (Reference, Not Prescription)
1. **variable-identification** — identify key variables in the causal system 2. **mechanism-mapping** — map causal mechanisms between variables 3. **evidence-collection** — gather evidence supporting/refuting causal claims 4. **intervention-analysis** — analyze what happens when variables are manipulated 5. **model-validation** — validate the causal model for consistency and completeness
MCP Tools Used
- `vault_search` — find existing variables and mechanisms
- `vault_add_edge` — create causal edges (derived_from, supported_by, contradicts)
- `vault_query_graph` — trace causal chains
- `vault_graph_stats` — assess model coverage
- `vault_lint` — validate structural integrity
Context-Management
<HARD-GATE>
- Call `context-init` at campaign start
- Call `context-checkpoint` after each strategy completes
- Call `knowledge-compilation` after each strategy
</HARD-GATE>
Guiding Principles
- **Correlation is not causation.** Every causal edge must have mechanistic justification, not just statistical association.
- **Confounders are everywhere.** Actively search for confounding variables that could explain observed relationships.
- **Interventions reveal truth.** The strongest evidence for causation comes from intervention studies.
- **Feedback loops are the norm.** Most real systems have circular causation. Document loops explicitly.
- **Confidence is calibrated.** Strong mechanism + strong evidence = high confidence. Weak either = low confidence.
<!-- BEGIN available-tables (generated) -->
Available Strategies
Optional, no fixed order; the final leaf is always a sop.
| Strategy | When to use | | --- | --- | | evidence-collection | Gather evidence for causal claims | | intervention-analysis | Analyze interventions and manipulations on the causal system | | knowledge-structuring-variable-identification | Identify key variables in the causal system | | mechanism-mapping | Map causal mechanisms between variables | | model-validation | Validate causal model consistency |
Available Tactics
Optional, no fixed order; the final leaf is always a sop.
| Tactic | When to use | | --- | --- | | counterfactual-reasoning | Tactic for reasoning about what would happen if variables were different — supports causal identification and intervention analysis. | | evidence-weighing | Tactic for assessing the strength and relevance of evidence for causal claims — distinguishes correlation from causation. | | feedback-loop-detection | Tactic for identifying circular causation — detect feedback loops, classify as reinforcing or balancing, document loop structure. | | knowledge-compilation | Tactic for compiling research findings into vault pages — orchestrates page creation, updates, edge linking, and index maintenance. Minimum yield ≥3 page operations per invocation. |
Available SOPs
Optional, no fixed order; the final leaf is always a sop.
| SOP | When to use | | --- | --- | | context-checkpoint | Append research process and results to the current Phase's context file. Covers both process and results with genuine substance. Use this skill at plan-designated checkpoint points — typically after each strategy completes or at key decision nodes within a research Phase. | | context-init | Create a new context file for a research Phase. Called once at Phase start to initialize the file that subsequent context-checkpoint calls will append to. Use this skill whenever a new research Phase begins and a fresh context file is needed. |
<!-- END available-tables (generated) -->
Read more
name: causal-modeling description: Campaign for building causal models — identify variables, map mechanisms, collect evidence, analyze interventions, validate models. Produces causal graphs in the wiki vault. execution: campaign dependencies: strategies: - evidence-collection - intervention-analysis - knowledge-structuring-variable-identification - mechanism-mapping - model-validation tactics: - counterfactual-reasoning - evidence-weighing - feedback-loop-detection - knowledge-compilation sops: - context-checkpoint - context-init
Causal Modeling
Build causal models for research domains. Identifies variables, maps causal mechanisms, collects supporting evidence, analyzes potential interventions, and validates the resulting causal graph.
Manifest
| Level | Count | Skills | |-------|-------|--------| | Strategy | 5 | variable-identification, mechanism-mapping, evidence-collection, intervention-analysis, model-validation | | Tactic | 3 | counterfactual-reasoning, evidence-weighing, feedback-loop-detection | | SOP | 10 | variable-page-creation, mechanism-edge-creation, evidence-linking, contradiction-flagging, confidence-scoring, intervention-page-creation, loop-documentation, model-gap-detection, causal-chain-query, validation-report |
Budget Table
| Metric | Small | Medium | Large | |--------|-------|--------|-------| | Variables identified | 8 | 20 | 40 | | Causal edges created | 15 | 40 | 80 | | Evidence pages linked | 10 | 30 | 60 | | Interventions analyzed | 2 | 5 | 10 | | Feedback loops documented | 1 | 3 | 6 |
Strategy Sequence (Reference, Not Prescription)
1. **variable-identification** — identify key variables in the causal system 2. **mechanism-mapping** — map causal mechanisms between variables 3. **evidence-collection** — gather evidence supporting/refuting causal claims 4. **intervention-analysis** — analyze what happens when variables are manipulated 5. **model-validation** — validate the causal model for consistency and completeness
MCP Tools Used
- `vault_search` — find existing variables and mechanisms
- `vault_add_edge` — create causal edges (derived_from, supported_by, contradicts)
- `vault_query_graph` — trace causal chains
- `vault_graph_stats` — assess model coverage
- `vault_lint` — validate structural integrity
Context-Management
<HARD-GATE>
- Call `context-init` at campaign start
- Call `context-checkpoint` after each strategy completes
- Call `knowledge-compilation` after each strategy
</HARD-GATE>
Guiding Principles
- **Correlation is not causation.** Every causal edge must have mechanistic justification, not just statistical association.
- **Confounders are everywhere.** Actively search for confounding variables that could explain observed relationships.
- **Interventions reveal truth.** The strongest evidence for causation comes from intervention studies.
- **Feedback loops are the norm.** Most real systems have circular causation. Document loops explicitly.
- **Confidence is calibrated.** Strong mechanism + strong evidence = high confidence. Weak either = low confidence.
<!-- BEGIN available-tables (generated) -->
Available Strategies
Optional, no fixed order; the final leaf is always a sop.
| Strategy | When to use | | --- | --- | | evidence-collection | Gather evidence for causal claims | | intervention-analysis | Analyze interventions and manipulations on the causal system | | knowledge-structuring-variable-identification | Identify key variables in the causal system | | mechanism-mapping | Map causal mechanisms between variables | | model-validation | Validate causal model consistency |
Available Tactics
Optional, no fixed order; the final leaf is always a sop.
| Tactic | When to use | | --- | --- | | counterfactual-reasoning | Tactic for reasoning about what would happen if variables were different — supports causal identification and intervention analysis. | | evidence-weighing | Tactic for assessing the strength and relevance of evidence for causal claims — distinguishes correlation from causation. | | feedback-loop-detection | Tactic for identifying circular causation — detect feedback loops, classify as reinforcing or balancing, document loop structure. | | knowledge-compilation | Tactic for compiling research findings into vault pages — orchestrates page creation, updates, edge linking, and index maintenance. Minimum yield ≥3 page operations per invocation. |
Available SOPs
Optional, no fixed order; the final leaf is always a sop.
| SOP | When to use | | --- | --- | | context-checkpoint | Append research process and results to the current Phase's context file. Covers both process and results with genuine substance. Use this skill at plan-designated checkpoint points — typically after each strategy completes or at key decision nodes within a research Phase. | | context-init | Create a new context file for a research Phase. Called once at Phase start to initialize the file that subsequent context-checkpoint calls will append to. Use this skill whenever a new research Phase begins and a fresh context file is needed. |
<!-- END available-tables (generated) -->
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

