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 skills add vinnie357/claude-skills --skill graphifygit clone --depth 1 https://github.com/vinnie357/claude-skillsWrote 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.
[](https://agentmods.dev/skills/vinnie357/claude-skills/graphify)<a href="https://agentmods.dev/skills/vinnie357/claude-skills/graphify"><img src="https://agentmods.dev/badge/skills/vinnie357/claude-skills/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.
<a href="https://agentmods.dev/skills/vinnie357/claude-skills/graphify"><img src="https://agentmods.dev/badge/skills/vinnie357/claude-skills/graphify.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
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 →
- medium MCP Rug Pull · line 19 uvx/uv tool run commands without ==version create a rug-pull risk.Fix: Pin the version: uvx package-name==1.2.3
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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00076 | $0.01515 |
| Opus 5 | $0.00038 | $0.00758 |
| Sonnet 5 | $0.00015 | $0.00303 |
| Haiku 4.5 | $0.00008 | $0.00152 |
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 5d 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 — 126 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Graphify
Graphify converts a folder of code, docs, PDFs, images, and videos into a queryable knowledge graph. It extracts symbols and relationships locally with tree-sitter (no code leaves the machine for AST extraction), writes the graph to graphify-out/graph.json plus an interactive graph.html, and answers questions by traversing the graph instead of re-reading files.
PyPI package: graphifyy (double-y). CLI command: graphify. License: MIT. Requires Python >= 3.10.
For using graphify to reduce agent token usage during research and decomposition, load graphify-agents.
Installation
Using mise (recommended for this project)
Copy templates/mise.toml from this skill into the project's mise.toml, then run mise install. graphify is a PyPI package, so mise installs it through its pipx backend; the template pins uv as the backend installer and enables uvx mode so one mise install resolves uv then graphifyy:
[settings.pipx]
uvx = true
[tools]
python = "3.12"
uv = "latest"
"pipx:graphifyy" = "0.8.36"
mise trust && mise install # installs python, uv, then graphifyy
graphify --version # → graphify 0.8.36
Verify the backend sees the package before pinning a different version:
mise ls-remote pipx:graphifyy | tail -5
Alternative installation (upstream)
The project's own documented install (outside mise):
pip install graphifyy && graphify install
pip install graphifyy also accepts extras, e.g. pip install "graphifyy[all]". Available extras: pdf, office, google, video, mcp, neo4j, svg, leiden, ollama, openai, gemini, anthropic, bedrock, azure, sql, postgres, dm, terraform, chinese, all.
How Graphify Works
- Scan + extract — walks the target path, classifies files (code, docs, papers, images), and runs tree-sitter AST extraction locally. AST extraction needs no LLM and no network.
- Infer relationships — semantic edge inference uses a configured LLM backend (
gemini|kimi|claude|openai|deepseek|ollama, auto-detected from available API keys). Skippable with--no-cluster/update. - Cluster + label — community detection groups related nodes; an LLM names the communities (skippable with
--no-label). - Write outputs —
graphify-out/graph.json(NetworkX node-link JSON),graph.html(interactive viz), and a markdown report. - Query —
query/path/explain/affectedtraversegraph.jsonwith no LLM call for the traversal itself.
What ships with it
2 files 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.
- 5d ago First seen · 126 lines · 76 tokens per session scan A 40591861e84d
graphify is a skill published in the GitHub repository vinnie357/claude-skills (25 stars, last pushed 3d ago), licensed MIT. It adds 76 tokens to every session and 1,515 once invoked, about $0.0004 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 2026-09-03.
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