graph

An interactive tool for exploring a knowledge graph, where notes are connected by shared topics or links. It translates natural-language questions into graph analyses and explains the results using the vault's configured terminology.

In plain words
What is it for?
Use it to check graph health, find triangles and synthesis opportunities, investigate relationships, or ask questions about the structure of a knowledge base.
Why use it?
It makes relationships in a note collection easier to inspect than reading notes one at a time. It can reveal weakly connected notes, clusters, and possible connections worth exploring.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/agenticnotetaking/arscontexta/graph
Any agent
npx skills add agenticnotetaking/arscontexta --skill graph
Clone the repo
git clone --depth 1 https://github.com/agenticnotetaking/arscontexta

Made for: Claude Code, Codex.

Per session 50 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 5,231 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00050 $0.05231
Opus 5 $0.00025 $0.02616
Sonnet 5 $0.00010 $0.01046
Haiku 4.5 $0.00005 $0.00523

Measured 2d ago against content hash d966701e600c, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

graph scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skill-sources/graph/SKILL.md · 568 lines

How it starts

The opening of the file, as written. The whole thing — 568 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Runtime Configuration (Step 0 — before any processing)

Read these files to configure domain-specific behavior:

  1. ops/derivation-manifest.md — vocabulary mapping, platform hints

    • Use vocabulary.notes for the notes folder name
    • Use vocabulary.note / vocabulary.note_plural for note type references
    • Use vocabulary.topic_map / vocabulary.topic_map_plural for MOC references
    • Use vocabulary.cmd_reflect for connection-finding command name
    • Use vocabulary.cmd_reweave for backward-pass command name
  2. ops/config.yaml — for graph thresholds (MOC size limits, orphan thresholds)

If no derivation file exists, use universal terms (notes, MOCs, etc.).


EXECUTE NOW

Target: $ARGUMENTS

Parse the operation from arguments:

  • If arguments match a known operation: route to that operation
  • If arguments are a natural language question: map to the closest operation (see Interactive Mode)
  • If no arguments: enter interactive mode

START NOW. Route to the appropriate operation.


Philosophy

The graph IS the knowledge. This skill makes it visible.

Individual {vocabulary.note_plural} are valuable, but their connections create compound value. /graph reveals the structural properties of those connections — where the graph is dense, where it is sparse, where it is fragile, and where synthesis opportunities hide.

Every operation produces two things: findings (what the analysis reveals) and actions (what to do about it). Never dump raw data. Always interpret results with {vocabulary.note} descriptions and domain context. Always suggest specific next steps.


Operations

/graph health

Full graph health report: density, orphans, dangling links, coverage.

Step 1: Collect raw metrics

# Count total notes (excluding MOCs)
NOTES_DIR="{vocabulary.notes}"
TOTAL=$(ls -1 "$NOTES_DIR"/*.md 2>/dev/null | wc -l | tr -d ' ')
MOC_COUNT=$(grep -rl '^type: moc' "$NOTES_DIR"/*.md 2>/dev/null | wc -l | tr -d ' ')
NOTE_COUNT=$((TOTAL - MOC_COUNT))

# Count all wiki links
LINK_COUNT=$(grep -ohP '\[\[[^\]]+\]\]' "$NOTES_DIR"/*.md 2>/dev/null | wc -l | tr -d ' ')

# Calculate link density
# Density = actual_links / possible_links
# possible_links = N * (N - 1) for directed graph
echo "Density: $LINK_COUNT / ($NOTE_COUNT * ($NOTE_COUNT - 1))"

# Find orphan notes (zero incoming links)
for f in "$NOTES_DIR"/*.md; do
  NAME=$(basename "$f" .md)
  INCOMING=$(grep -rl "\[\[$NAME\]\]" "$NOTES_DIR"/ 2>/dev/null | grep -v "$f" | wc -l | tr -d ' ')
  [[ "$INCOMING" -eq 0 ]] && echo "ORPHAN: $NAME"
done

# Find dangling links (links to non-existent files)
grep -ohP '\[\[([^\]]+)\]\]' "$NOTES_DIR"/*.md 2>/dev/null | sort -u | while read -r link; do
  NAME=$(echo "$link" | sed 's/\[\[//;s/\]\]//')
  [[ ! -f "$NOTES_DIR/$NAME.md" ]] && echo "DANGLING: $NAME"
done

# MOC coverage: % of notes appearing in at least one MOC's Core Ideas
COVERED=0
for f in "$NOTES_DIR"/*.md; do
  NAME=$(basename "$f" .md)
  # Skip MOCs themselves
  grep -q '^type: moc' "$f" 2>/dev/null && continue
  # Check if any MOC links to this note
  if grep -rl '^type: moc' "$NOTES_DIR"/*.md 2>/dev/null | xargs grep -l "\[\[$NAME\]\]" >/dev/null 2>&1; then
    COVERED=$((COVERED + 1))
  fi
done
echo "Coverage: $COVERED / $NOTE_COUNT"

Read the full file on GitHub · 568 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 568 lines · 50 tokens per session scan A d966701e600c

Subscribe to this mod's changes

graph is a skill published in the GitHub repository agenticnotetaking/arscontexta (3,486 stars, last pushed 6mo ago), licensed MIT. It adds 50 tokens to every session and 5,231 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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