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Rebuild the graphify knowledge graph (graphify-out/) by shelling out to `issue-flow graphify` or `graphify` directly.
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
Based on SOC occupation classification
Post-merge branch hygiene: switch to the default branch and delete merged local branches under one consolidated confirm. Never -D.
Process many issues hands-off in a row: resolve a queue, then run each through the yolo chain under one up-front confirm. Stops only when input is strictly necessary.
Plan a larger change as a staged epic: draft epic<N>_plan.md with stages of manageable issue specs, then publish confirmed stages as GitHub issues.
Front door: choose the next issue, create the issue branch, and run /iflow-init.
Smart dispatcher: detect where the focus issue stands and dispatch to /iflow-init, /iflow-plan, /iflow-start, or /iflow-close.
Read-only snapshot of where every issue stands, locally and on GitHub.
| name | iflow-graphify |
| description | Rebuild the graphify knowledge graph (graphify-out/) by shelling out to `issue-flow graphify` or `graphify` directly. |
| disable-model-invocation | true |
| issue-flow-version | 0.4.2a4 |
/iflow-graphify)Follow this skill to refresh the project's graphify knowledge graph — a stale graphify-out/ after a large refactor, or the initial graph after installing graphifyy.
Do not use this skill from /iflow-start, /iflow-close, or /iflow. /iflow-graphify is opt-in only.
Invoke: type iflow graphify in chat, or /iflow-graphify from the slash menu (iflow-graphify also works).
Profile: economy — Prioritize speed and token economy over deep reasoning.
In Cursor: use Auto or a fast model before invoking this step.
Keep scope tight to what this step requires.
Prefer issue-flow graphify from the project root:
issue-flow graphify
With no extra args this runs graphify update <project> — AST-only, no LLM API key required, produces the full graphify-out/. To pick a different graphify subcommand, pass it as the first arg: issue-flow graphify extract (adds the slower semantic LLM pass for richer relationships — needs an API key), issue-flow graphify watch (live), issue-flow graphify cluster-only --no-viz, etc. Use -C <dir> to scan a project other than the current directory. Trailing flags pass through verbatim. Do not invent new wrapper flags.
Fallback to graphify directly when issue-flow is unavailable:
graphify update .
graphify is subcommand-based — graphify . on its own is not valid (graphify reports unknown command '.'). Always pick a subcommand: update for the no-LLM AST build, extract for the full semantic pass, watch for a long-running watcher, etc.
If graphify exits with "no LLM API key found", the user picked extract (or another semantic subcommand) without configuring a backend. Cursor's own LLM is not available to subprocesses, so graphify cannot reuse it. Suggest one of:
GEMINI_API_KEY / GOOGLE_API_KEY, ANTHROPIC_API_KEY, OPENAI_API_KEY, or MOONSHOT_API_KEY.issue-flow graphify extract --backend ollama to use a local LLM via Ollama (requires Ollama installed with a model pulled).extract arg and use the default issue-flow graphify (AST-only, no LLM).Handle missing graphify gracefully. If the run reports graphify is not on PATH, do not retry blindly. Tell the user to install it once:
uv tool install graphifyy # recommended
pipx install graphifyy
pip install graphifyy
graphifyy (double-y) is the official PyPI package; the CLI is still graphify. After installing, suggest issue-flow update so graphify cursor install registers the graphify Cursor skill alongside this one.
Verify and report.
graphify-out/graph.json, graphify-out/graph.html, and graphify-out/GRAPH_REPORT.md exist after a successful run.GRAPH_REPORT.md (god nodes, surprising connections) for a short summary./iflow-graphify from another slash command. The user opts in explicitly.graphify-out/cost.json or graphify-out/manifest.json; they are local-only.watch) keep the process alive; ask the user before launching them in an agent context.