detect-coverage-gap
SkillDev toolsLets your agent analyze a matrix, graph, or taxonomy to find missing or weakly covered areas and explain why they matter.
Available today. Use it from your connected AI after setup.
No other account needed.
Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.
Then ask your AI: use the detect-coverage-gap skill
About 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.
What this skill tells your AI
The instructions your AI receives, as published by yogsoth-ai/de-anthropocentric-research-engine in skills/detect-coverage-gap/SKILL.md and read by ahel’s review.
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
- Validate the representation and enumerate its eligible regions or nodes.
- Mark observed coverage and classify absent, thin, disconnected, and weak-link regions.
- Characterize the consequence and evidence for each gap.
- 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
<0.3are 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-detectionline 27: Must analyze the full orphan list and report actionable gap descriptions.model-gap-detectionline 21: Check for edges with weight < 0.3 - these are weak links needing more evidence.model-gap-detectionline 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. |
Signals
- GitHub stars
- 501
- Forks
- 41
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Key
detect-coverage-gap- Source
- github.com/yogsoth-ai/de-anthropocentric-research-engine