Borrowing it
Nothing to install: this file belongs to lecopivo/another-houdini-mcp. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/lecopivo/another-houdini-mcp/main/AGENTS.mdgit clone --depth 1 https://github.com/lecopivo/another-houdini-mcpWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/instructions/lecopivo/another-houdini-mcp/agents-md)<a href="https://agentmods.dev/instructions/lecopivo/another-houdini-mcp/agents-md"><img src="https://agentmods.dev/badge/instructions/lecopivo/another-houdini-mcp/agents-md/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/instructions/lecopivo/another-houdini-mcp/agents-md"><img src="https://agentmods.dev/badge/instructions/lecopivo/another-houdini-mcp/agents-md.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.01052 | $0.01052 |
| Opus 5 | $0.00526 | $0.00526 |
| Sonnet 5 | $0.00210 | $0.00210 |
| Haiku 4.5 | $0.00105 | $0.00105 |
Grade A, and why
another-houdini-mcp AGENTS.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 92 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AGENTS.md
What This MCP Does
This project provides a Houdini MCP server and plugin that let an agent inspect, build, edit, and validate Houdini scenes and HDAs programmatically.
You can use it to:
- Explore scene structure and node connections
- Create/delete/connect nodes
- Read/set node parameters
- Execute HScript or Python (HOM) when needed
- Inspect local Houdini documentation in
help/ - Build validation workflows around geometry and node behavior
Tools Available
Scene and Node Exploration
get_folder_infoget_node_infoget_node_presentationget_node_connectionsget_scene_infoget_sticky_notes
Node Graph Editing
create_nodedelete_nodeconnect_nodesremove_connectionset_parameter
Example and HDA Loading
install_hda_filelist_example_nodesinstantiate_example_assetload_exampleinstantiate_hda
Node and Parameter Discovery
list_node_categorieslist_node_typesget_parameter_infoget_node_parametersget_parameter_overridesget_node_documentation
Python/HScript Execution
execute_pythonexecute_hscript
HOM (Python API) Documentation
list_python_commandssearch_python_documentationget_python_documentation
Local Documentation File Tools (help/)
search_documentation_filesread_documentation_file
Documentation Expectations
- Before implementing or changing behavior, look up relevant docs.
- Use documentation tools first, then fallback to ad-hoc scripts.
- The project documentation corpus is in
help/.
Tutorials Expectations
- Review
memory/index.mdfirst to refresh workflow memory. - Use relevant files in
memory/before inventing a new approach. - When working in a specific context (for example SOP), refresh context memory first by reading
memory/<context>_context.md(for examplememory/sop_context.md). - When working on a specific node, refresh node memory first by reading
memory/nodes/<context>/<node>.mdif it exists. - Treat
memory/<context>_context.mdandmemory/nodes/<context>/<node>.mdas extended documentation, not activity logs. memory/<context>_context.mdshould contain generalized context-level rules, patterns, heuristics, pitfalls, and cross-node workflows.memory/nodes/<context>/<node>.mdshould contain reusable node-level guidance: intent, setup contracts, parameter interactions, debug tactics, and production usage notes.- Node-note format should follow
memory/node_study_template.mdand mirror the richer reference style ofmemory/nodes/sop/heightfield_erode-2.0.md(Intent, Core Behavior, Key Parameters, Typical Workflow, Production Usage with measured outcomes, Gotchas, Companion Nodes, Study Validation). - Do not record raw chronological "what I clicked/did" logs in these files; abstract concrete experiences into general lessons.
- If an experiment reveals a failure mode, record the transferable rule (why it failed, how to detect, how to fix), not scene-specific narration.
- Keep session chronology and restart checkpoints in
short_term_memory.mdonly. - When reviewing an example scene for a target node, inspect all meaningful companion nodes in that example network (not just the target node) and capture reusable patterns in memory notes.
- If companion-node findings are strong enough, add or update their own
memory/nodes/<context>/<node>.mdfiles even if they were not the primary study target. - Do not defer companion-node note updates: while studying one node, proactively edit other node memory notes in the same session when you discover meaningful behavior, patterns, parameters, or edge cases.
- If you struggle with something and then finally solve it, suggest updating the appropriate tutorial in
memory/with that lesson.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 92 lines · 1,052 tokens per session scan A c369808febbd
another-houdini-mcp AGENTS.md is an instructions file published in the GitHub repository lecopivo/another-houdini-mcp (5 stars, last pushed 5mo ago), licensed MIT. It adds 1,052 tokens to every session, about $0.0053 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other instructions, from other repositories
next.js AGENTS.md
AGENTS.md instructions for vercel/next.js, covering next.js development guide, codebase structure, monorepo overview, core package: packages/next and other important packages.
codex AGENTS.md
AGENTS.md instructions for openai/codex, covering rust/codex-rs, the codex-core crate, code review rules, crate api surface and model visible context.
vscode buildNext.instructions.md
Working notes and architecture documentation for the new esbuild-based build system in build/next. Use when making changes to the new build pipeline (transpile/bundle commands, NLS plugin, source-map handling, resource copying, or self-hosting watch tasks).
vscode oss-third-party-notices.instructions.md
Instructions for microsoft/vscode, covering vs code oss third-party-notices pipeline, architecture, pipeline flow in ci, applying the notice (cutover) and fallback chain (never fail the build).
langchain AGENTS.md
AGENTS.md instructions for langchain-ai/langchain, covering global development guidelines for the langchain monorepo, corridor security analysis, project architecture and context, monorepo structure and development tools & commands.
deepseek-harness AGENTS.md
AGENTS.md instructions for deepseek-ai/deepseek-harness, covering agents.md, pre-stable apis and released session data, repository layout, commands and host sandbox failures.