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Automation
Skill

/detect-coverage-gap

Given an explicit coverage representation (matrix, graph, taxonomy, IP feature space), identify absent, thin, disconnected, or weakly covered regions and characterize why they matter.

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de-anthropocentric-research-engine
499200 skills
Install
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill detect-coverage-gap --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/detect-coverage-gap

Context preview

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

Given an explicit coverage representation (matrix, graph, taxonomy, IP feature space), identify absent, thin, disconnected, or weakly covered regions and characterize why they matter.

SKILL.md

detect-coverage-gap.SKILL.md
name: detect-coverage-gap
description: "Given an explicit coverage representation (matrix, graph, taxonomy, IP feature space), identify absent, thin, disconnected, or weakly covered regions and characterize why they matter."

detect-coverage-gap

Purpose

Given an explicit matrix, graph, taxonomy, or IP feature space, identify absent, thin, disconnected, or weakly covered regions and explain why they matter.

Input contract

required: [coverage_representation, universe_definition, coverage_evidence]
optional: [gap_priority_rule, taxonomy, graph_statistics, domain_constraints]
constraints: [the representation and eligible universe are explicit; every reported gap points to an absent, thin, disconnected, or weakly covered region]

Procedure

1. Validate the representation and enumerate its eligible regions or nodes. 2. Mark observed coverage and classify absent, thin, disconnected, and weak-link regions. 3. Characterize the consequence and evidence for each gap. 4. Prioritize gaps using the caller-supplied rule and return an actionable gap list.

Output contract

produces: [coverage_gap_list, coverage_map, priority_rationale, evidence_register]
delta_fields: [findings, evidence_updates, decisions, open_questions]

Quality gates

  • Declare the eligible universe, numerator, denominator, batch increment, stopping reason, source references, direction, and threshold rationale for relative coverage claims.
  • Report coverage ratio whenever a relative coverage claim is made.
  • When coverage is evaluated over evidence sources or batches, also report independent-source ratio, marginal information gain, and saturation state.
  • The complete orphan list is analyzed where graph mode applies; weak links below `<0.3` are retained as source-defined evidence and not silently normalized.
  • Matrix white-space detection and graph orphan detection remain distinct representations.

Parameterization

The caller must provide the coverage representation type, eligible universe, observed evidence, region/node schema, thinness or connectivity rule, priority rule, and source references. For graph mode provide node/edge statistics; for IP mode provide the feature taxonomy and claim map.

Failure and counterexamples

Reject when the universe is undefined, a gap is inferred from missing data rather than represented absence, or an orphan/weak-link result lacks a repair rationale.

Provenance map

  • resolved: creative-ideation/coverage-gap-detection
  • resolved: creative-ideation/white-space-detection
  • resolved: knowledge-acquisition/white-space-mapping
  • resolved: knowledge-structuring/gap-detection
  • resolved: knowledge-structuring/model-gap-detection
  • intermediate: Pass3/detect-white-space
  • intermediate: Pass3/map-ip-white-space
  • intermediate: Pass3/detect-structural-gap

Verbatim source criteria excerpts

  • `gap-detection` line 27: Must analyze the full orphan list and report actionable gap descriptions.
  • `model-gap-detection` line 21: Check for edges with weight < 0.3 - these are weak links needing more evidence.
  • `model-gap-detection` line 27: Must check both orphans and weak links and include actionable suggestions.

Preserved source criteria ledger

| source | physical line | kind | source criterion | |---|---:|---|---| | knowledge-structuring/gap-detection | 14 | gate | Analyze the complete orphan list and provide executable repair suggestions. | | knowledge-structuring/model-gap-detection | 14 | numeric/gate | Query orphan nodes with degree 0 and weak links `<0.3`; provide executable gaps and suggestions. |

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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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