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Rebuild the graphify knowledge graph (graphify-out/) by shelling out to `issue-flow graphify` or `graphify` directly.
Instalar con Codex o Claude Copia este prompt, pégalo en Codex, Claude u otro asistente, y deja que revise la página de la skill y la instale por ti.
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.
Basado en la clasificación ocupacional SOC
| 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.