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.
npx agentmods add skills/agenticnotetaking/arscontexta/graphnpx skills add agenticnotetaking/arscontexta --skill graphgit clone --depth 1 https://github.com/agenticnotetaking/arscontextaWhat 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.
| Model | Per session | Once 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 |
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.
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:
-
ops/derivation-manifest.md— vocabulary mapping, platform hints- Use
vocabulary.notesfor the notes folder name - Use
vocabulary.note/vocabulary.note_pluralfor note type references - Use
vocabulary.topic_map/vocabulary.topic_map_pluralfor MOC references - Use
vocabulary.cmd_reflectfor connection-finding command name - Use
vocabulary.cmd_reweavefor backward-pass command name
- Use
-
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"
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.
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.
- 2d ago First seen · 568 lines · 50 tokens per session scan A d966701e600c
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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