houdini-automation

name	houdini-automation
description	Pipeline skill - run file-based Houdini Python automation, manage hip files, set timeline ranges, and build small node chains from structured specs. Use when orchestrating repeatable Houdini tasks. Not for inspecting a scene only.
license	MIT
compatibility	dcc-mcp-houdini 0.1+, Houdini 18.5+, dcc-mcp-core 0.19.45+
allowed-tools	Bash Read Write Edit
metadata	{"dcc-mcp":{"dcc":"houdini","layer":"pipeline","stage":"pipeline","version":"1.2.0","tags":["houdini","automation","hip","timeline","pipeline","scripts"],"search-hint":"run python file, save hip, load hip, timeline, build node chain","search-aliases":["run script file","save hip","open hip","load scene","set frame range","timeline","build node chain","automate houdini"],"example-prompts":["Run this Python file in Houdini","Save the hip file to /projects/showA/shot.hip","Set the frame range to 1-120 and build a box→transform chain"],"intent":"Run repeatable file-based Houdini automation: scripts, hip I/O, timeline, and node-chain recipes.","recall-context":{"app_type":"houdini","domain":"pipeline","workflow-stage":"pipeline","task-category":"mutate"},"requires":[],"produces":["scene_state","node_network","file:hip"],"preconditions":[{"type":"software","name":"houdini","version":">=18.5"}],"side-effects":{"creates":true,"modifies":true,"file-output":true,"targets":["scene_state","node_network","file:hip"]},"tools":"tools.yaml"}}

houdini-automation

Higher-level repeatable automation for Houdini sessions. Prefer run_python_file for reviewed scripts on disk, and build_node_chain for compact graph recipes.

build_node_chain is a structured atomic mutation surface, not an arbitrary code executor. It validates the parent, every node type/name/reference, and all connection references/port indices before opening an undo group. Use dry_run=true to inspect validated and predicted affected_paths with zero scene mutation.

On execution, the complete recipe uses one named Houdini undo group. Results include transaction_id, undo_label, validation evidence, and post-cook readback. If creation, parameter assignment, connection, layout, cook, or readback fails, the tool explicitly removes created nodes and restores any existing input connections and existing node positions touched by layout. Check rollback.complete and rollback.errors before retrying a failed recipe.

Successful responses expose a compact summary with the created nodes, readback-verified connections, submitted parameter values, and counts. This is the preferred proof for generated MaterialX networks.

MaterialX displacement acceptance

For each existing MaterialX builder, use one build_node_chain recipe with an mtlximage feeding mtlxrange, then mtlxdisplacement. Put texture paths, range bounds, clamping, and displacement scale in each node's parameters and connect nodes by their recipe-local ref or node_name. Four materials need four such calls because they have four different parent networks; each call is independently prevalidated and leaves no partial network when rollback is complete.

Use a separate atomic recipe under /stage for the rendergeometrysettings node and its upstream/downstream LOP connections. Resolve the current Houdini/Karma true-displacement and dicing parameter names with the parameter inspection tools, then pass those names through the same structured parameters object; no renderer-specific Python is required.

Do not save between batch calls. After every material and stage response has a successful summary, call save_hip_file. It writes a sibling temporary HIP and uses an atomic filesystem replacement, so a save or replace failure preserves the prior target. If replacement fails after the temporary save, use the returned recovery_file. In-memory recipes remain separate transactions; reload the prior saved HIP when whole-batch rollback is required.