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/assess-readiness-dimension

Assess one readiness dimension against explicit anchors/evidence.

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de-anthropocentric-research-engine
499200 skills
Install
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill assess-readiness-dimension --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/assess-readiness-dimension

Context preview

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

Assess one readiness dimension against explicit anchors/evidence.

SKILL.md

assess-readiness-dimension.SKILL.md
name: assess-readiness-dimension
description: "Assess one readiness dimension against explicit anchors/evidence."

assess-readiness-dimension

Purpose

Assess one readiness dimension against explicit anchors, evidence, and known deficits.

Input contract

required: [subject_record, readiness_dimension, anchor_definition, evidence_register]
optional: [scoring_scale, prior_assessment]
constraints: [anchor levels are ordered and observable; every score cites evidence and a deficit statement]

Procedure

1. Define the dimension, scale, anchor descriptions, and decision relevance. 2. Map each supplied evidence item to the strongest supported anchor and record contrary evidence. 3. Assign the lowest defensible level whose requirements are met; record missing requirements separately. 4. Emit the dimension score, evidence basis, deficits, and reassessment trigger.

Output contract

produces: [readiness_level, evidence_basis, deficit_list, reassessment_trigger]
delta_fields: [findings, evidence_updates, uncertainties, decisions, open_questions]

Quality gates

  • A-class gate: declared universe = all requirements of the readiness dimension; numerator = requirements supported by at least two independent evidence items; batch increment = one requirement assessed; stopping reason = all requirements assessed or a blocking deficit is established; source references = evidence and anchor IDs; direction/threshold reason = level rises only when its anchor requirements are met and falls when blocking contrary evidence dominates.
  • A level without a deficit or uncertainty statement is invalid.

Failure and counterexamples

Do not average incomparable readiness dimensions. A high score on one dimension cannot erase an unmet hard requirement on another.

Provenance map

  • resolved: dimension-assessment
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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.

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