Mcp Knowledge Gaps — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited Mcp Knowledge Gaps (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
<!-- mcp-name: io.github.onetrueclaude-creator/mcp-knowledge-gaps -->
Find what your knowledge base mentions but doesn't actually explain.
Find concepts mentioned but never defined in your markdown knowledge base (Obsidian vault, Logseq graph, any folder of .md files). Uses fuzzy canonicalization to avoid false positives, ranks gaps by frequency × region-diversity × novelty, generates prioritized research questions, and samples from the long tail via sortition to break confirmation bias in your research queue.
pip install mcp-knowledge-gaps
# or
uvx mcp-knowledge-gapsclaude mcp add mcp-knowledge-gaps -- mcp-knowledge-gapsAdd to claude_desktop_config.json:
{
"mcpServers": {
"knowledge_gaps": {
"command": "uvx",
"args": ["mcp-knowledge-gaps"]
}
}
}| Tool | Tier | Description |
|---|---|---|
find_gaps | Free | Scan a markdown vault and return concepts mentioned in multiple notes but without their own dedicated note. Applies fuzzy canonicalization and noise filtering. |
list_gaps_by_priority | Free | Return gaps ranked by priority: frequency × diversity × novelty (higher = fill this gap first). |
generate_research_questions | Pro | Generate prioritized research questions for the top N gaps. Each question comes with a priority score and factor breakdown. |
surprise_research_topic | Pro | Sortition sampling — pick a random gap from the LOW-priority long tail. Breaks confirmation bias by surfacing topics you'd never pick yourself. |
export_review_queue | Pro | Export a CSV of top-priority gap concepts, suitable for Anki or other spaced-repetition tools. Writes to output_csv and returns the row count. |
Unlocks research question generation with RL-weighted ranking, sortition sampling of long-tail gaps, and CSV review queue export.
License activation — any one of these works:
# 1. Environment variable
export KNOWLEDGE_GAPS_LICENSE="eyJhbGc..."
# 2. CLI flag
mcp-knowledge-gaps --license-key "eyJhbGc..."
# 3. Config file
echo "eyJhbGc..." > ~/.mcp-knowledge-gaps/license.jwtLicenses are verified fully offline — no phone-home, no activation server. Get a license at https://github.com/onetrueclaude-creator/mcp-knowledge-gaps#pro-tier.
MIT
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.