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 agentmods add skills/meltflexdevs/skills/meltflex-designnpx skills add MeltFlexDevs/skills --skill meltflex-designgit clone --depth 1 https://github.com/MeltFlexDevs/skillsWrote 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/meltflexdevs/skills/meltflex-design)<a href="https://agentmods.dev/skills/meltflexdevs/skills/meltflex-design"><img src="https://agentmods.dev/badge/skills/meltflexdevs/skills/meltflex-design.svg" alt="Measured on agentmods" 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 | $0.00129 | $0.01982 |
| Opus 5 | $0.00064 | $0.00991 |
| Sonnet 5 | $0.00026 | $0.00396 |
| Haiku 4.5 | $0.00013 | $0.00198 |
Grade B, and why
meltflex-design scanned grade B with 2 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 today.
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.
Sends data to an external URLmediumData exfiltration
A POST to an outside endpoint may be telemetry or may be exfiltration; either way the mod talks to somewhere, and you should know where.
resp = requests.post( "https://www.meltflexai.com/api/v1/generate", Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
resp = requests.post( How it starts
The opening of the file, as written. The whole thing — 122 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MeltFlex — AI Interior Design
MeltFlex turns a real photo of a space into a redesigned, photorealistic result. This skill calls the MeltFlex REST API directly, so it works in any agent without installing anything else. (If the meltflex-mcp server is configured, prefer its generate_interior tool — it handles file I/O for you.)
To place specific furniture products into a room, use the companion skill /meltflex:furniture.
Prerequisites
The user needs a MeltFlex API key (mf_sk_...), available on an active subscription at https://www.meltflexai.com/settings. Expect it in the MELTFLEX_API_KEY environment variable. If missing, ask them to set it:
export MELTFLEX_API_KEY="mf_sk_..."
Each generation costs 10 credits, deducted upfront and auto-refunded on failure. Credits belong to the user's account.
The one endpoint
POST https://www.meltflexai.com/api/v1/generate
Headers: Authorization: Bearer $MELTFLEX_API_KEY, Content-Type: application/json.
Body:
prompt(string, required) — the redesign instruction.imageUrl(string) orimage(base64 data URL) — the source photo. PreferimageUrl.resolution(string, optional) — output quality:"512","1K", or"2K"(sharpest). Defaults to model auto.designLevel(string, optional) —"lite"(faster & cheaper, −2 credits),"quick"(default), or"pro"(most detailed, HIGH thinking, +5 credits).mask(boolean, optional) — region edit. Whentrue, the source image must have the area to change painted solid red; the prompt is applied only to those red-marked regions and the rest is preserved. Use for "change only this part".variations(number, optional) — batch of1–3images in one call (default 1). Charged per image; failed variations are refunded.
Response: { "success": true, "image": "data:...", "images": ["data:..."], "count": 1, "creditsUsed": 10 }.
Modes
Every mode is the same endpoint with a mode-tuned prompt. Pick the mode that matches the request and lead the prompt with it.
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.
- today Changed 73a373d0acd5
- 3d ago First seen · 122 lines · 129 tokens per session scan B 50c8e6c2afc1
meltflex-design is a skill published in the GitHub repository MeltFlexDevs/skills (1 stars, last pushed today), licensed MIT. It adds 129 tokens to every session and 1,982 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it B with 2 findings (sends data to an external url, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
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brainstorming
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auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
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…