Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add peteromallet/Arnold --skill vibecomfy_executorgit clone --depth 1 https://github.com/peteromallet/ArnoldWrote 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/skills/peteromallet/arnold/vibecomfy_executor)<a href="https://agentmods.dev/skills/peteromallet/arnold/vibecomfy_executor"><img src="https://agentmods.dev/badge/skills/peteromallet/arnold/vibecomfy_executor/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/skills/peteromallet/arnold/vibecomfy_executor"><img src="https://agentmods.dev/badge/skills/peteromallet/arnold/vibecomfy_executor.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 | $0.00044 | $0.01203 |
| Opus 5.5 | $0.00018 | $0.00481 |
| Sonnet 5.5 | $0.00009 | $0.00241 |
| Haiku 4.5 | $0.00004 | $0.00120 |
Grade A, and why
vibecomfy-executor 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 1mo 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 — 136 lines — stays where its author put it; the contents beside it link to each section on GitHub.
vibecomfy-executor
A small decision-graph pipeline for routing user queries through the right mix of research and action.
Topology
classify → research → implement → reply → halt
- classify — decides which downstream steps are needed.
- research — searches the Hivemind/Banodoco corpus when the plan asks for it.
- implement — produces an implementation artifact when the plan asks for it.
If a workflow
graphis supplied, this stage runs VibeComfy's agent-edit machinery and mutates the workflow. - reply — always runs; synthesizes the final response.
Execution plan
The classifier outputs a JSON plan with three booleans:
{
"research": true,
"implement": true,
"reply": true
}
research— query asks about facts, settings, best practices, comparisons, etc.implement— query asks to create, edit, fix, build, or change something.reply— almost always true; the final textual answer.
CLI usage
python -m arnold run vibecomfy-executor --inputs query="What are the best Wan settings?"
To edit a workflow, pass a graph input that points to a ComfyUI UI JSON file
(or an inline JSON object):
python -m arnold run vibecomfy-executor \
--inputs 'query=Set the KSampler seed to 12345 and the steps to 30,graph=tests/fixtures/agent_edit/flat.json' \
--plan-dir /tmp/qo-edit-run
The positional input_file argument maps to ctx.inputs["draft"] if provided;
--inputs query=... is the recommended way to pass the user query.
Profiles
The pipeline ships a default profile in profiles/default.toml that maps each
stage to a Hermes-backed DeepSeek subagent:
[profiles.default]
classify = "hermes:deepseek:deepseek-v4-flash"
research = "hermes:deepseek:deepseek-v4-pro"
implement = "hermes:deepseek:deepseek-v4-pro"
reply = "hermes:deepseek:deepseek-v4-pro"
At runtime the resolved profile is available in ctx.profile, and each step
constructs the matching AIAgent via the canonical key pool. If no worker can
be resolved, steps fall back to fast heuristics or placeholders so the pipeline
can still be tested without a model backend.
What ships with it
12 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- __init__.py 2.0 KB runs code
- _helpers.py 21 KB runs code
- pipelines.py 4.7 KB runs code
- profiles/anthropic.toml 408 B
- profiles/default.toml 400 B
- profiles/openai.toml 386 B
- profiles/opensource.toml 493 B
- steps.py 20 KB runs code
- tests/fixtures/edit_flat_ksampler.json 119 B
- tests/fixtures/edit_qwen_image_scale.json 190 B
- tests/fixtures/edit_wan_t2v_size.json 122 B
- tests/run_matrix.py 6.8 KB runs code
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.
- 1mo ago First seen · 136 lines · 44 tokens per session scan A 6de4956372c4
vibecomfy-executor is a skill published in the GitHub repository peteromallet/Arnold (98 stars, last pushed yesterday), licensed Apache-2.0. It adds 44 tokens to every session and 1,203 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-09-04.
Other skills, from other repositories
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…