Agent skill · software engineering · SylphAI-Inc
codegraph
Turn any codebase into an explorable interactive graph so a developer can understand it fast. Deterministic scan (files, imports, symbols, call edges, PageRank importance, architectural layers, dependency cycles, entry points) plus optional AI enrichment (plain-English summaries and guided tours), delivered as ONE self-contained HTML file. Use when asked to explain, map, visualize, diagram, onboard onto, or understand a repository or its architecture.
Why this skill is useful
Adds a deterministic scanning and visualization process for codebases that the AI wouldn't reliably generate on its own.
What it needs
Requires python installed locally. About 9k tokens when loaded. Last updated 2026-08-06. 101 stars on the source repository.
What this skill does
codegraph Turn a codebase into a map a human can actually read. The goal is not a graph that shows off how complex the code is — it is a graph that quietly teaches someone how the pieces fit together. When to Use Trigger on requests like: "help me understand this codebase" / "I just joined this project" "map / visualize / diagram this repo or its architecture" "what are the entry points?" / "how does data flow through this?" "generate an onboarding guide" / "where do I start reading?" "show me the dependency graph" / "find circular dependencies" "which files are most important here?" Do not use this for a single-file explanation — just read the file. Core Design (read this before running anything) Three stages. Two are deterministic; only the middle one uses your intelligence. Why this split matters — do not violate it: Stage Owner Guarantee --- --- --- Structure (files, imports, calls, metrics, cycles) scan.py Reproducible. Same commit always yields the same edges. Meaning (what a file is for, tours) You Only a model can write this. Delivery (layout, validation, HTML) render.py Validated before write; a broken artifact never replaces a good one. Never hand-write graph.json. It is machine output. You only ever write enrich.json. Never invent files, symbols, or relationships. Every id you emit must already exist in the digest. Instructions Step 1 — Scan (always start here) Nothing to install: Python 3.8+ stdlib only. ~2s for 230k LOC. Useful flags: --max-files N (default 4000) — raise it if output warns about truncation --symbols-for N (default 250) — extract function/class nodes for the N most important files --no-calls — skip heuristic cross-file call edges (faster, fewer false positives) Then read digest.md, not graph.json. The digest is a compact briefing (entry points, directory map, ranked-importance table, cycles, dependencies, layer counts). graph.json can be megabytes and will flood your context for no benefit. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills SylphAI-Inc/codegraph