Borrowing it
Nothing to install: this file belongs to bottobot/touchdesigner-mcp-server. 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/bottobot/touchdesigner-mcp-server/main/.claude/commands/td-mcp-improvement.mdgit clone --depth 1 https://github.com/bottobot/touchdesigner-mcp-serverWrote 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/commands/bottobot/touchdesigner-mcp-server/td-mcp-improvement)<a href="https://agentmods.dev/commands/bottobot/touchdesigner-mcp-server/td-mcp-improvement"><img src="https://agentmods.dev/badge/commands/bottobot/touchdesigner-mcp-server/td-mcp-improvement/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/commands/bottobot/touchdesigner-mcp-server/td-mcp-improvement"><img src="https://agentmods.dev/badge/commands/bottobot/touchdesigner-mcp-server/td-mcp-improvement.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 | $0.00005 | $0.02395 |
| Opus 5 | $0.00003 | $0.01197 |
| Sonnet 5 | $0.00001 | $0.00479 |
| Haiku 4.5 | $0.00001 | $0.00239 |
Grade A, and why
td-mcp-improvement 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 10d 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 — 145 lines — stays where its author put it; the contents beside it link to each section on GitHub.
flowchart TD
start_node_default([Start])
analyze_codebase[Sub-Agent: analyze-codebase]
implement_version_system[Sub-Agent: implement-version-system]
implement_experimental_kb[Sub-Agent: implement-experimental-kb]
implement_core_tools[Sub-Agent: implement-core-tools]
implement_experimental_builds[Sub-Agent: implement-experimental-builds]
validate_changes[Sub-Agent: validate-changes]
update_documentation[Sub-Agent: update-documentation]
completion_prompt[TD MCP Server v2.8.0 improv...]
end_node_default([End])
start_node_default --> analyze_codebase
analyze_codebase --> implement_version_system
implement_version_system --> implement_experimental_kb
implement_experimental_kb --> implement_core_tools
implement_core_tools --> implement_experimental_builds
implement_experimental_builds --> validate_changes
validate_changes --> update_documentation
update_documentation --> completion_prompt
completion_prompt --> end_node_default
Workflow Execution Guide
Follow the Mermaid flowchart above to execute the workflow. Each node type has specific execution methods as described below.
Execution Methods by Node Type
- Rectangle nodes (Sub-Agent: ...): Execute Sub-Agents
- Diamond nodes (AskUserQuestion:...): Use the AskUserQuestion tool to prompt the user and branch based on their response
- Diamond nodes (Branch/Switch:...): Automatically branch based on the results of previous processing (see details section)
- Rectangle nodes (Prompt nodes): Execute the prompts described in the details section below
Sub-Agent Node Details
analyze_codebase(Sub-Agent: analyze-codebase)
Description: Analyzes current TD MCP Server state and plans improvements
Model: sonnet
Prompt:
Analyze the current state of the TouchDesigner MCP Server project at /home/robert/Documents/TD-MCP/touchdesigner-mcp-server. Read CHANGELOG.md, README.md, MCP-ARCHITECTURE.md, package.json, and scan the tools/ and wiki/data/ directories. Identify: (1) the 12 existing tools and their capabilities, (2) improvements already made in v2.7.0, (3) the five planned improvement streams: Version System, Experimental Techniques KB, Core Tool Enhancements, Experimental Build Support, and All Streams Combined. Return a concise summary of current state and confirm readiness to proceed with all improvements automatically.
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.
- 10d ago First seen · 145 lines · 5 tokens per session scan A 04db645a6ff6
td-mcp-improvement is a command published in the GitHub repository bottobot/touchdesigner-mcp-server (77 stars, last pushed 21d ago), licensed MIT. It adds 5 tokens to every session and 2,395 once invoked, about $0.0000 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 2026-08-30.
Other commands, from other repositories
create
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checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.