Skip to content
Automation
Skill

/identify-variables

Identify relevant variables/factors and annotate functional roles.

From plugin
de-anthropocentric-research-engine
499200 skills
Install
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill identify-variables --agent claude-code

How 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/identify-variables

Context preview

The summary Claude sees to decide when to auto-load this skill.

Identify relevant variables/factors and annotate functional roles.

SKILL.md

identify-variables.SKILL.md
name: identify-variables
description: "Identify relevant variables/factors and annotate functional roles."

identify-variables

Purpose

Identify relevant variables, factors, conditions, and assumptions and annotate their functional roles.

Input contract

required: [artifact_or_system, target_claim_or_outcome]
optional: [causal_claims, domain_schema, candidate_factors]
constraints: [include explicit and implicit factors; each variable has a role, provenance, and suspected importance]

Procedure

1. Parse the target claim or outcome and list quantities that could support, alter, or confound it. 2. Enumerate explicit and implicit variables, factors, conditions, and assumptions. 3. Classify functional roles such as causal variable, factor, design dimension, mediator, moderator, or confounder. 4. Attach provenance and suspected importance and return the structured variable list.

If variables and their roles are defined well enough to assert directional relations, consider `represent-mechanism-edge` as the next tactic.

Output contract

produces: [variable_register, role_annotations, provenance_links, importance_notes]
delta_fields: [findings, evidence_updates, uncertainties, open_questions]

Quality gates

  • The register is exhaustive relative to the supplied artifact and causal claims; explicit and implicit entries are distinguished.
  • Every factor has a functional role and a suspected-importance annotation; do not collapse variables with different causal roles.

Parameterization

The caller must provide the artifact/system description, target claim or outcome, optional causal claims, role ontology, domain vocabulary, and any candidate-factor list to reconcile.

Failure and counterexamples

Reject when a variable has no relation to the target outcome, a role is assigned without evidence, or a confounder is silently treated as a cause.

Provenance map

  • concept: hypothesis-formation/variable-identification
  • resolved: stress-test/factor-enumeration
  • resolved: creative-ideation/parameter-identification

Preserved source criteria ledger

| source | physical line | kind | source criterion | |---|---:|---|---| | stress-test/factor-enumeration | 18 | structural | List all factors, conditions, and assumptions supporting the artifact conclusion, including explicit and implicit entries and suspected importance. |

Read more
Ships withde-anthropocentric-research-engine

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.

Get the whole plugin
Stats
499
Stars
41
Forks
Active
Maintenance
Python
Language
Apache-2.0
License
1h ago
Last commit
7mo ago
Created

Repo: yogsoth-ai/de-anthropocentric-research-engine