project-graph

name

project-graph

description

Generates an interactive HTML map of any coding project showing all files, their connections, and entry-point flows, enriched by a living AI knowledge file that grows as the project is explored. Use when asked to generate a project graph, visualize project structure, map file connections or flows, get a high-level overview of a codebase, or answer questions about a project that already has a generated graph (technologies, services, how a flow works).

Project Graph

Core Rules

• Run scripts/generate-graph.py with python3; it has no third-party dependencies. The CLI is a thin wrapper over the scripts/project_graph/ package (discovery, parsing, flows, knowledge, build, render).
• Privacy first: the script never opens or includes dotfiles or dot-folders (.env*, .git/, .github/, .vscode/, and so on); the only dotfile it reads is .gitignore, and only to learn which paths to ignore. Never weaken this rule.
• The script respects .gitignore automatically (via git ls-files, with a manual fallback), so node_modules, build output, and machine-local files never enter the graph.
• Output is a single self-contained file, project-graph.html, written to the project root by default. It does not embed absolute machine paths; Go to file resolves the project root from where each user opens the HTML (with an optional per-browser override in the Config tab).
• Flows are first-class: routes, API handlers, and entry files are auto-detected; important business flows (for example "send OTP") should be defined in a project-graph.flows.json manifest so users can click a flow and see every file it touches.
• The graph is a living artifact: project-graph.knowledge.json at the project root holds AI-authored understanding (overview, technologies, services, flow insights and diagrams, file notes) and is merged into every regeneration. Writing the JSON alone does not update the dashboard — you must always regenerate project-graph.html afterward and verify the merge (see Knowledge update loop below).
• Knowledge grows incrementally and context-cheaply: investigate only what the question needs (manifests first, then entry files), write short summaries, and always merge into the existing knowledge file - never rewrite it wholesale and never bulk-analyze the project.
• When regenerating the graph, if project-graph.knowledge.json contains flow diagrams, re-verify each against its entry point (and only files referenced in the diagram) before finishing; update any stale insight or diagram, leave accurate ones unchanged. See references/knowledge.md diagram refresh rules.
• Always verify the run via the exit code and the PROJECT_GRAPH_RESULT line; never read the generated HTML wholesale to check it.
• Works on any project (API, client app, monorepo, docs repo) on any machine with Python 3.8+; never assume a specific framework, repo layout, or skill install path. See references/portability.md.
• The skill install folder and the mapped project are independent — resolve generate-graph.py per references/portability.md; never hardcode one developer's paths.
• Do not commit project-graph.html unless the user asks for it.

Workflow

1. Identify the project root to map (default: the workspace root).
2. Check for important flows the auto-detection will miss; if the user names flows or the project has clear business flows, author project-graph.flows.json at the project root per references/flows.md.
3. Resolve and run the generator (see references/portability.md):

python3 "<path-to-skill>/scripts/generate-graph.py" [project_root]

Use generator_script from a previous PROJECT_GRAPH_RESULT when available; otherwise the skill path from this session, .cursor/skills/project-graph/ in the workspace, or ~/.cursor/skills/project-graph/. Pass an explicit [project_root] when cwd is not the codebase root.

4. Verify: exit code 0 and "ok": true in the final PROJECT_GRAPH_RESULT JSON line. Sanity-check the reported file/connection/flow counts against expectations.
5. If the knowledge file has flow diagrams (flow_diagrams > 0 in the result), spot-check each diagram against its entry point and referenced step files; merge updates for any that are stale, then re-run once more if you changed the knowledge file.
6. If the run failed, was suspicious, or reported problems, follow the recovery protocol in references/self-healing.md until a verified generation succeeds.
7. Tell the user to open project-graph.html in a browser and summarize what the graph shows, using references/dashboard-guide.md for tab semantics. On a first generation, offer to populate the project's overview, technologies, and services into the knowledge file.
8. From then on, treat the graph as a conversation: each time the user asks about a flow, file, or aspect of the project (often via a dashboard "Copy & explain" prompt), follow the Knowledge update loop below — never stop after writing JSON only.

Knowledge update loop (mandatory)

Use this whenever you add or change anything in project-graph.knowledge.json (including "Copy & explain" / "Copy & update" prompts from the dashboard). Do not mark the task complete until every step passes.

1. Answer — investigate only what the question needs (references/knowledge.md context budget).
2. Merge — read the existing project-graph.knowledge.json (create on first use), update only the keys you learned about, set "updated" to today. Never wipe other keys.
3. Regenerate — always run the generator immediately after a knowledge change (resolve path per references/portability.md):

python3 "<path-to-skill>/scripts/generate-graph.py" [project_root]

4. Verify merge — check exit code 0 and parse PROJECT_GRAPH_RESULT:
   • "ok": true
   • knowledge.present is true when the knowledge file exists
   • knowledge.total_entries increased (or matches expectations if you only updated an existing entry)
   • the specific count for your entry type increased when adding something new (file_notes, flow_insights, technologies, etc.)
   • no problems mentioning the knowledge file
5. Tell the user — say what was added and ask them to reload project-graph.html in their browser (the open tab does not auto-refresh).

If verification fails: fix the knowledge file (invalid JSON, wrong flow key, wrong file path) per references/knowledge.md and references/self-healing.md, then regenerate again. Never assume the dashboard updated without a successful generator run.

Package layout

scripts/project_graph/ — stdlib-only modules the skill and agents can import individually:

Module	Role
constants.py	File-type sets, ignore rules, output filenames
discovery.py	Git-aware file listing (privacy-safe)
categorize.py	Node category classification
refs.py	Import/link extraction per language
resolver.py	Specifier → project path resolution
parsing.py	Docstring/summary extraction
flows.py	Auto/manifest flow detection and reachability
knowledge.py	project-graph.knowledge.json load and flow matching
build.py	Assemble nodes, edges, flows, knowledge payload
render.py + template.html	Self-contained HTML dashboard
verify.py	Output verification and PROJECT_GRAPH_RESULT
cli.py	Argument parsing and orchestration

Import example (e.g. to inspect a payload without writing HTML): from project_graph.build import build_payload.

Reference Index

• CLI flags, privacy rules, file discovery, import resolution, supported languages: references/script-usage.md
• Flow auto-detection rules and the flows manifest schema: references/flows.md
• Living knowledge file: schema, growth protocol, context budget, flow diagram authoring: references/knowledge.md
• Verification contract, exit codes, and recovery protocol: references/self-healing.md
• Dashboard tabs, node and edge semantics, reading the output: references/dashboard-guide.md
• Any machine, any project, skill install locations, path resolution: references/portability.md

When To Use This Skill

• Generating a visual map or mind map of a codebase.
• Explaining how files in a project connect to each other.
• Documenting flows (routes, APIs, business processes) and the files behind them.
• Onboarding someone onto an unfamiliar project with a high-level overview.
• Answering questions about a mapped project (technologies, services, how a flow behaves) - record findings in the knowledge file and regenerate so the answer is preserved in the dashboard.
• Authoring a flow diagram for a flow the user selected in the dashboard.
• Verifying or refreshing stale flow diagrams (dashboard "Copy & update" on a documented flow, or automatically on graph regeneration when diagrams exist).