| 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.
