graphify

graphify is a skill for Claude Code from mycelium-hq/ai-brain-starter. It costs 111 tokens per session (12,389 once invoked), scanned A, original, MIT.

A tool that turns a folder of files into a knowledge graph, which maps relationships between related items. It can produce an interactive webpage, graph data for retrieval, and a plain-language report.

In plain words
What is it for?
Use it to map a codebase or notes, find connections across files, detect communities, create GraphRAG data, or update an existing graph.
Why use it?
Large collections of code, documents, papers, notes, and images can be hard to understand as a whole. The graph reveals connections and groups related material while recording how it was built.

Skill for Claude Code

Written for Claude Code: argument-hint in frontmatter. Also seen: mentions CLAUDE.md; mentions subagents; built for gbrain.

Part of the ai-brain-starter plugin — 38 skills, 17 commands, 3 agents, 1 hook shipped together

Good fit Use it to map a codebase or notes, find connections across files, detect communities, create GraphRAG data, or update an existing graph.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/mycelium-hq/ai-brain-starter/graphify
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.

Any agent
npx skills add mycelium-hq/ai-brain-starter --skill graphify
Clone the repo
git clone --depth 1 https://github.com/mycelium-hq/ai-brain-starter

Made for: Claude Code.

Or install ai-brain-starter, the plugin that ships this one along with the rest of its 38 skills, 17 commands, 3 agents, 1 hook.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for graphify

README.md
[![agentmods](https://agentmods.dev/badge/skills/mycelium-hq/ai-brain-starter/graphify/github.svg)](https://agentmods.dev/skills/mycelium-hq/ai-brain-starter/graphify)
Your own site
<a href="https://agentmods.dev/skills/mycelium-hq/ai-brain-starter/graphify"><img src="https://agentmods.dev/badge/skills/mycelium-hq/ai-brain-starter/graphify/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for graphify

Your own site · 80×15
<a href="https://agentmods.dev/skills/mycelium-hq/ai-brain-starter/graphify"><img src="https://agentmods.dev/badge/skills/mycelium-hq/ai-brain-starter/graphify.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 111 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 12,389 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 5 findings, up to high

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

  • high Tool Misuse · line 452
    Tool parameters are crafted to achieve unintended or unsafe behavior. Parameter abuse can bypass intended safety checks (e.g. shell=True, --force, dangerous glob patterns).
    Fix: Validate all tool parameters against an allowlist. Reject dangerous parameter values (shell=True, --force, -rf /) and use safe defaults.
  • high Tool Misuse · line 907
    Tool parameters are crafted to achieve unintended or unsafe behavior. Parameter abuse can bypass intended safety checks (e.g. shell=True, --force, dangerous glob patterns).
    Fix: Validate all tool parameters against an allowlist. Reject dangerous parameter values (shell=True, --force, -rf /) and use safe defaults.
  • high Tool Misuse · line 908
    Tool parameters are crafted to achieve unintended or unsafe behavior. Parameter abuse can bypass intended safety checks (e.g. shell=True, --force, dangerous glob patterns).
    Fix: Validate all tool parameters against an allowlist. Reject dangerous parameter values (shell=True, --force, -rf /) and use safe defaults.
  • high Anti-Refusal · line 970
    Skill instructs the agent to omit warnings, disclaimers, or ethical commentary. Stripping safety caveats hides risk from the user and is a common jailbreak preamble.
    Fix: Remove instructions that suppress warnings, disclaimers, or ethical commentary. Let the agent surface safety-relevant caveats to the user.
  • medium Excessive Agency · line 75
    Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.
    Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
How audits are shown
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 5.1 $0.00111 $0.12389
Opus 5 $0.00056 $0.06195
Sonnet 5 $0.00022 $0.02478
Haiku 4.5 $0.00011 $0.01239

Measured yesterday against content hash f8b5a407fe42, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

graphify 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 yesterday.

The scan reads SKILL.md. This mod also ships 9 executable files (scripts/graphify_canonicalize.py, scripts/graphify_chunk.py, scripts/graphify_prep.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

skills/graphify/SKILL.md · 971 lines

How it starts

The opening of the file, as written. The whole thing — 971 lines — stays where its author put it; the contents beside it link to each section on GitHub.

/graphify

{SKILL_DIR} = this skill's own folder (locally: the directory this SKILL.md lives in; a served brain substitutes the real absolute path before you read this). If a path does not resolve, name the missing file and stop — never guess another location.

Turn any folder of files into a navigable knowledge graph with community detection, an honest audit trail, and three outputs: interactive HTML, GraphRAG-ready JSON, and a plain-language GRAPH_REPORT.md.

Before running on a corpus larger than ~50 files, READ OPTIMIZATIONS.md. The wrapper scripts in scripts/ (dedupe, regex preflight, word-balanced chunking, label canonicalization, cache integration) typically cut LLM token cost by 80–92% and produce a higher-quality graph. The single most important step is calling graphify_canonicalize.py --cache after merging — without it, the next --update run repays the entire cost. Skip these wrappers and a 1,500-file vault will burn ~10M LLM tokens for the same graph that costs ~1M with them.

Usage

/graphify                                             # full pipeline on current directory → Obsidian vault
/graphify <path>                                      # full pipeline on specific path
/graphify <path> --mode deep                          # thorough extraction, richer INFERRED edges
/graphify <path> --update                             # incremental - re-extract only new/changed files
/graphify <path> --directed                            # build directed graph (preserves edge direction: source→target)
/graphify <path> --whisper-model medium                # use a larger Whisper model for better transcription accuracy (base|small|medium|large)
/graphify <path> --cluster-only                       # rerun clustering on existing graph
/graphify <path> --no-viz                             # skip visualization, just report + JSON
/graphify <path> --html                               # (HTML is generated by default - this flag is a no-op)
/graphify <path> --svg                                # also export graph.svg (embeds in Notion, GitHub)
/graphify <path> --graphml                            # export graph.graphml (Gephi, yEd)
/graphify <path> --neo4j                              # generate graphify-out/cypher.txt for Neo4j
/graphify <path> --neo4j-push bolt://localhost:7687   # push directly to Neo4j
/graphify <path> --mcp                                # start MCP stdio server for agent access
/graphify <path> --watch                              # watch folder, auto-rebuild on code changes (no LLM needed)
/graphify <path> --wiki                               # build agent-crawlable wiki (index.md + one article per community)
/graphify <path> --obsidian --obsidian-dir ~/vaults/my-project  # write vault to custom path (e.g. existing vault)
/graphify add <url>                                   # fetch URL, save to ./raw, update graph
/graphify add <url> --author "Name"                   # tag who wrote it
/graphify add <url> --contributor "Name"              # tag who added it to the corpus
/graphify query "<question>"                          # BFS traversal - broad context
/graphify query "<question>" --dfs                    # DFS - trace a specific path
/graphify query "<question>" --budget 1500            # cap answer at N tokens
/graphify path "AuthModule" "Database"                # shortest path between two concepts
/graphify explain "SwinTransformer"                   # plain-language explanation of a node

Read the full file on GitHub · 971 lines

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. yesterday Changed · +17 lines f8b5a407fe42
  2. 8d ago First seen · 954 lines · 111 tokens per session scan A 0bb48a94cbe4

Subscribe to this mod's changes

graphify is a skill published in the GitHub repository mycelium-hq/ai-brain-starter (36 stars, last pushed yesterday), licensed MIT. It adds 111 tokens to every session and 12,389 once invoked, about $0.0006 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 2026-08-31.

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