Graph
Open-source AI Operating System (AIOS) blueprint for Claude Code & Codex — turn any agent into your personal second brain + Chief of Staff: skills, self-verifying workflows, a second-brain wiki, and deterministic tools. Fork it for any role or domain.
npx -y skills add alirezarezvani/gaios --skill graphAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
One thing to look at
- no licenseNo license file was found in the repository. Code published without one is not open source by default, so using it at work is a question for whoever answers licensing questions where you are.
What its author says it does
Copied from the file, not written here
Use when the user wants to build, refresh, or visualize the repo's knowledge graph — code structure plus the committed wiki/ — via graphify. Produces an interactive graph + a GRAPH_REPORT.md of God Nodes, surprising connections, and suggested questions. Trigger on "/graph", "build the knowledge graph", "graph the repo", "map the codebase", "refresh the graph", "visualize the second brain".
SKILL.md
5.4 KB, as published. Nobody here has run it
Knowledge Graph (graphify)
Build a map of how this AIOS actually fits together — which code files and which wiki notes depend on, reference, and cluster with each other — so you can see the structure instead of guessing it. Powered by graphify (a knowledge-graph builder), scoped hard to what's safe to graph.
When to run
- The user asks to see how the repo / second brain hangs together ("map the codebase", "graph the repo").
- After meaningful structural change — new skills, tools, SOPs, or a
/wikiloop that added entries — to refresh the picture. - As a thinking aid before a refactor, an audit, or a
/weeklyreview: what's load-bearing, what's orphaned, what's surprisingly connected. - Before exploring with
/graph-query(this builds/refreshes the graph it queries).
The output (always this shape)
graphify writes everything to graphify-out/ (git-ignored — a derived artifact, never committed):
## Knowledge graph — <date>
**Built from** — code (local AST) + committed wiki/. Excluded: raw/, .tmp/, .env, secrets.
**Artifacts** — graphify-out/graph.html (interactive) · graph.json · GRAPH_REPORT.md
**God Nodes** — the most-connected hubs (what everything leans on):
1. <node> — <why it's central, in a phrase>
2. … 3. …
**Surprising Connections** — edges you wouldn't expect (cross-domain links worth a look):
- <A> ↔ <B> — <what the link is>
**Suggested Questions** — what the graph invites you to ask next:
- <question> → run `/graph-query "<question>"`
Every edge carries graphify's honesty label — EXTRACTED (read directly), INFERRED (reasoned), or AMBIGUOUS — so claims about the graph cite their basis (cite-don't-invent).
Process
- Ensure graphify is installed. Run
python tools/graphify_setup.py check. If missing, offer to install (uv tool install graphifyy, fallbackpipx install graphifyy/pip install graphifyy, thengraphify install --platform claude). No account or signup is needed. Gate: don't proceed untilgraphify --versionresolves. - Set the build scope — code + committed
wiki/ONLY. This is the hard line:- Include: source code (Python in
tools/, etc.) and the committedwiki/. - EXCLUDE always:
raw/(may hold raw PHI/PII/financials before de-identification),.tmp/,.env,graphify-out/, and any sensitive/secret path. Never point graphify's document extraction atraw/. - Why this is safe: code is parsed locally by tree-sitter AST (no LLM, nothing leaves the machine); document extraction is limited to
wiki/, which is already de-identified and non-confidential per the wiki admission policy. graphify also auto-skips sensitive files during detection — but scope, not that fallback, is your guardrail.
- Include: source code (Python in
- Build the graph.
- Full build (code + wiki): the heavy document pass runs through graphify's own pipeline using the host IDE session — invoke
/graphify .(graphify's global skill), having confirmed scope. Non-code text inwiki/is extracted by this session; graphify does not call any third party or readANTHROPIC_API_KEYin-IDE. - Code-only refresh (no LLM, fast): run
graphify update <path>— re-extracts the code AST deterministically, no model call. Prefer this when only code changed. - Gate: confirm
graphify-out/graph.jsonandGRAPH_REPORT.mdwere written before reporting.
- Full build (code + wiki): the heavy document pass runs through graphify's own pipeline using the host IDE session — invoke
- Surface the highlights. Read
graphify-out/GRAPH_REPORT.mdand summarize into the output shape above — God Nodes, Surprising Connections, Suggested Questions — keeping the EXTRACTED/INFERRED/AMBIGUOUS labels. Point the user atgraphify-out/graph.htmlfor the interactive view. - Offer the next move. Propose
/graph-query "<question>"to explore (shortest paths, neighbors, communities), and — if the user wants it standing —graphify hook installfor auto-rebuild on commit (a Cadence touch) or--watchfor continuous rebuild while working.
Autonomy
L2 — drafts/refreshes the graph; the human reads it. Building is reversible and lands only in git-ignored graphify-out/, so it's safe to run on request. A code-only graphify update (deterministic, no LLM, no document pass) can run at L3 — e.g. via the commit hook — once trusted. Never commit graphify-out/; never widen scope beyond code + wiki/ without explicit approval.
Guardrails (from CLAUDE.md)
- Never extract
raw/(or.env,.tmp/, secrets) —raw/can hold pre-de-identified PHI/PII/financials. Document extraction is limited to the committed, de-identifiedwiki/. (Guardrails #1, #2, #7) graphify-out/is git-ignored — a derived artifact. No secrets, confidential figures, or sensitive data enter git via the graph.- Cite, don't invent. Carry graphify's honesty audit trail (EXTRACTED / INFERRED / AMBIGUOUS) into any claim about the graph. (Guardrail #6)
- Code AST is parsed locally; the wiki document pass uses this host session only — no third-party call, no
ANTHROPIC_API_KEYread.