How do I install the Second Brain Graph skill?

Run npx skillmds@latest add undefined-ui/second-brain-graph in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Second Brain Graph safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Second Brain Graph?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Second Brain Graph free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Second Brain Graph?

undefined-ui (@undefined-ui) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Second Brain Graph by undefined-ui — Agent Skill

Name: Second Brain Graph
Author: undefined-ui

Second Brain Graph

Analyse the vault's link graph and report on its shape: orphan rate, average degree, components, hubs, bridges and clusters, with what each number means for retrieval. Use this skill when the user asks about the health or structure of their graph, wants metrics, asks which pages are most connected or most isolated, or asks whether the vault is fragmented. Do NOT use for fixing what it finds, which is second-brain-lint, or for answering a content question.

undefined-ui 1a5d78d 2.1 KB Updated Sep 17, 2026

File contents

Analyse the graph

The graph is the search space every answer is drawn from. Its shape decides whether retrieval reaches the relevant material in a few hops or reaches nothing.

Core rule

Report shape and what it means, never fix anything. This skill is read-only.

Workflow

Run the scripts rather than reading files: scripts/vault_stats.py for counts and degree, scripts/link_check.py for orphans and breaks, scripts/graph_export.py when deeper analysis is wanted.
Compute the four metrics: orphan rate, average degree, component count, stale-page rate.
Identify hubs (pages that have swallowed the graph) and bridges (pages connecting otherwise separate clusters).
Name the clusters. These are what the vault is actually about, which is reliably different from what the user would say it is about.
Say what each number means for answering questions, not just the value.

Output format

Pages <n> | links <n> | avg degree <n>
Orphan rate <n>%      (healthy under 5)
Components <n>        (main holds <n>%)
Stale concepts <n>%   (untouched 90+ days)

Hubs: <pages with outsized degree, and whether to split them>
Bridges: <pages connecting clusters, and what breaks if they are wrong>
Clusters: <what the vault is actually about>

What this means: <two or three sentences>

Calibration

Never report page count or word count as a health signal. Both rise whether the vault is improving or not.

A single reading says little. If the user has recorded previous numbers, compare against them; if not, suggest recording these.

---name: second-brain-graphdescription: Analyse the vault's link graph and report on its shape: orphan rate, average degree, components, hubs, bridges and clusters, with what each number means for retrieval. Use this skill when the user asks about the health or structure of their graph, wants metrics, asks which pages are most connected or most isolated, or asks whether the vault is fragmented. Do NOT use for fixing what it finds, which is second-brain-lint, or for answering a content question.---# Analyse the graphThe graph is the search space every answer is drawn from. Its shape decideswhether retrieval reaches the relevant material in a few hops or reaches nothing.## Core ruleReport shape and what it means, never fix anything. This skill is read-only.## Workflow1. **Run the scripts** rather than reading files: `scripts/vault_stats.py` for counts and degree, `scripts/link_check.py` for orphans and breaks, `scripts/graph_export.py` when deeper analysis is wanted.2. **Compute the four metrics:** orphan rate, average degree, component count, stale-page rate.3. **Identify hubs** (pages that have swallowed the graph) and **bridges** (pages connecting otherwise separate clusters).4. **Name the clusters.** These are what the vault is actually about, which is reliably different from what the user would say it is about.5. **Say what each number means** for answering questions, not just the value.## Output format```Pages <n> | links <n> | avg degree <n>Orphan rate <n>% (healthy under 5)Components <n> (main holds <n>%)Stale concepts <n>% (untouched 90+ days)Hubs: <pages with outsized degree, and whether to split them>Bridges: <pages connecting clusters, and what breaks if they are wrong>Clusters: <what the vault is actually about>What this means: <two or three sentences>```## CalibrationNever report page count or word count as a health signal. Both rise whether thevault is improving or not.A single reading says little. If the user has recorded previous numbers, compareagainst them; if not, suggest recording these.

Frequently asked questions