modal
Skill389 starsChanged 23 days agoMITView on GitHub
Use when the user needs to run isolated code remotely — a disposable container, optional GPU access (T4 → H100), or a safer place for untrusted / heavy code. Prefer local execution for normal repo work; use Modal sandboxes for isolation, hardware access, or one-shot heavy compute.
- Installs packages
17 other projects use a version of this.
Adopted by
Most recently taken up by
- BrightGold70/skillstheir own version, first seen Nov 8, 2026
- lobehub/lobehubtheir own version, first seen Nov 5, 2026
- MarieLynneBlock/arcanum-artifextheir own version, first seen Nov 1, 2026
- nagisanzenin/modal-skilltheir own version, first seen Oct 25, 2026
- patoangolano/bio-stacktheir own version, first seen Oct 15, 2026
- longtermrisk/claude-skillstheir own version, first seen Oct 13, 2026
Used with
Projects with it also use torch-geometric, get-available-resources and 9 more.
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Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
Hongyu-yuSkilltorch-geometric
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SoljournerSkillget-available-resources
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Hugging FaceSkilltransformers
Agent-invokable Transformers commands. Pass `--format json` at the top level (e.g. `transformers --format json classify ...`) to receive the structured output documented in each capability's `outputs` schema.
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Lightning-fast DataFrame library written in Rust for high-performance data manipulation and analysis. Use when user wants blazing fast data transformations, working with large datasets, lazy evaluation pipelines, or needs better performance than pandas. Ideal for ETL, data wrangling, aggregations, joins, and reading/writing CSV, Parquet, JSON files.
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Other versions17 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 17
- lobehub/lobe-chat★ 84ksame name
- lobehub/lobehub★ 84ksame name
- LeonChaoX/qinyan-academic-skills★ 910same name
- qinyan-ai/qinyan-academic-skills★ 80same name
- MarieLynneBlock/arcanum-artifex★ 4same name
- JunMA98/Computer-science-claude-skills★ 3same name
- modal-projects/skills★ 3same name
- BrightGold70/skills★ 1same name
- garri333/Skills★ 1same name
- longtermrisk/claude-skills★ 1same name
7 more not listed
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository18 other files it gives its agents.
- AGENTS.mdAGENTS.md
- CLAUDE.mdCLAUDE.md
- blockrun-debugSkill
- blockrun-setupSkill
- blockrunSkill
- blockrun-upgradeSkill
- crypto-dataSkill
- decideSkill
- exa-researchSkill
- gentech-blockrunSkill
- image-promptingSkill
- phoneSkill
- polymarket-tradingSkill
- prediction-marketsSkill
- rpcSkill
- searchSkill
- signal-to-trade-demoSkill
- surfSkill
The file
Files
Modal Sandboxes
Disposable remote containers (with optional GPU) via Modal, paid per call in USDC. No Modal account, no GPU procurement.
Base only in wallet mode; fine on an API key. sol.blockrun.ai carries the /v1/modal/* routes but has no Modal backend
configured, so every action — create, exec, status, terminate — answers 503. That reads
as "the sandbox service is down" rather than "wrong chain", which is exactly the wrong
conclusion to act on: retrying will not help. The tool checks the active chain first and
says so. Switch with blockrun_wallet action:"chain" chain:"base". Prices below are Base
prices and include its per-transaction fee.
READ THIS BEFORE SETTING timeout
timeout is the BILLED lifetime, charged upfront in full, and never refunded — not an idle timeout. Above 300s the price switches from a flat rate to per-hour billing for the entire duration you ask for, whether you use it or not. Terminating early refunds nothing.
That makes timeout the single most expensive field in this MCP:
| what you ask for | what you pay |
{ timeout: 300 } | $0.0110 |
{ timeout: 300, gpu: "A100" } | $0.2010 |
{ timeout: 600, gpu: "A100" } | $1.1677 |
{ timeout: 300, gpu: "T4", cpu: 8, memory: 32768 } | $0.3343 |
{ timeout: 86400, gpu: "H100" } | $216.0010 |
A 24h H100 sandbox costs $216 upfront, non-refundable, even if your job finishes in a minute.
So: ask for the time you need, not a safe-looking ceiling. Need 20 minutes of H100? timeout: 1200 is $3.00, not $216. Keep timeout ≤ 300, cpu ≤ 1 and memory ≤ 1024 and you stay on the flat rate.
How to Call from MCP
// 1. Create — timeout: 300 keeps you on the FLAT rate ($0.0110, or $0.2010 with A100).
// Anything above 300, or above 1 CPU or 1024 MB, bills hourly for the full requested lifetime, no refund.
blockrun_modal({ path: "sandbox/create", body: {
image: "python:3.11",
gpu: "A100",
timeout: 300,
setup_commands: ["pip install torch transformers"]
}})
// returns { sandbox_id, ... }
// 2. Exec
blockrun_modal({ path: "sandbox/exec", body: {
sandbox_id: "sb_abc...",
command: ["python", "-c", "import torch; print(torch.cuda.get_device_name(0))"]
}})
// 3. Terminate
blockrun_modal({ path: "sandbox/terminate", body: { sandbox_id: "sb_abc..." } })Endpoint Catalog
| Path | Method | Body | Price |
sandbox/create | POST | { image?, timeout?, cpu?, memory?, gpu?, setup_commands? } | depends on timeout, gpu, cpu and memory — see below |
sandbox/exec | POST | { sandbox_id, command: ["python","-c","..."], timeout? } | $0.0020 |
sandbox/status | POST | { sandbox_id } | $0.0020 |
sandbox/terminate | POST | { sandbox_id } | $0.0020 |
sandbox/create pricing is bimodal
timeout ≤ 300s, cpu ≤ 1 and memory ≤ 1024 — flat rate, charged once:
| gpu | price |
| (none, CPU) | $0.0110 |
T4 | $0.0510 |
L4 | $0.0810 |
A10G | $0.1010 |
A100 | $0.2010 |
H100 | $0.4010 |
Anything longer or larger — per-hour × the full requested lifetime, never below the flat rate, upfront, no refund:
| gpu | per hour | 1h | 24h (max) |
| (none, CPU) | $0.25 | $0.2510 | $6.0010 |
T4 | $4.00 | $4.0010 | $96.0010 |
L4 | $4.50 | $4.5010 | $108.0010 |
A10G | $5.00 | $5.0010 | $120.0010 |
A100 | $7.00 | $7.0010 | $168.0010 |
H100 | $9.00 | $9.0010 | $216.0010 |
Hours are exact, not rounded up — timeout: 1800 on A100 is 0.5h = $3.5010. Every figure above includes the $0.001 flat transaction fee. Max timeout is 86400 (24h).
Field Reference
| Field | Default | Notes |
image | python:3.11 | Any public Docker image. nvidia/cuda:12-runtime if you bring GPU code. |
timeout | 300 | BILLED lifetime in seconds — charged upfront for the full amount, never refunded. NOT idle eviction: you pay for what you ask for, not what you use. ≤300 = flat rate; >300 switches to per-hour billing (see the tables above). Max 86400 (24h). This is the field that turns a $0.01 sandbox into a $192 one. |
cpu | 1 | CPU cores |
memory | 512 | Memory in MB |
gpu | none | T4 / L4 / A10G / A100 / H100 — those five only. Anything else is rejected: {"gpu":"A100-80GB"} returns HTTP 400 "Unsupported GPU type. Allowed: T4, L4, A10G, A100, H100". Drives the price hard — see the tables above. |
setup_commands | [] | Shell commands run once during sandbox provisioning |
command (exec) | required | Array form: ["python","-c","print(2+2)"] |
Worked Examples
1. Quick Python eval
const { structuredContent: sb } = await blockrun_modal({ path: "sandbox/create", body: {} })
await blockrun_modal({ path: "sandbox/exec", body: {
sandbox_id: sb.sandbox_id,
command: ["python", "-c", "import numpy; print(numpy.__version__)"]
}})
await blockrun_modal({ path: "sandbox/terminate", body: { sandbox_id: sb.sandbox_id } })Cost: $0.0150 — create $0.0110 + exec $0.0020 + terminate $0.0020. Every call carries the $0.001 transaction fee, so three calls pay it three times; batch your work into one exec rather than several.
2. GPU inference, A100, with deps pre-installed
blockrun_modal({ path: "sandbox/create", body: {
gpu: "A100",
timeout: 1200,
memory: 16384
}})Then sandbox/exec with your inference command.
Cost: $1.3383 — create $1.3343 + exec $0.0020 + terminate $0.0020. timeout: 1200 is above the 300s flat tier, so the A100 bills hourly for the full 20 minutes you asked for: $4.00/h × (1200/3600) = $1.3333, + the $0.001 fee. It is charged upfront and never refunded — it does NOT auto-evict when idle, and terminating after 30 seconds still costs the full $1.3343. Ask for the time you actually need.
3. Test untrusted code Claude generated
blockrun_modal({ path: "sandbox/exec", body: {
sandbox_id,
command: ["bash", "-c", "<the generated script>"],
timeout: 60
}})Output is captured. No risk to your local machine.
When NOT to Use Modal
- Normal repo edits / dev work — use local tools, Modal adds latency and cost
- Long-running services — sandboxes are ephemeral, not server hosts
- Anything you'd run hundreds of times per minute — payment overhead dominates at high QPS
Notes
sandbox_idis returned bycreateand required by every other endpointexecis sync — blocks until command finishes or hits itstimeoutterminateis cheap; call it to free the sandbox even iftimeoutwould expire shortly- The free-tier
nvidia/*LLM models inblockrun_chatare different infrastructure — Modal is for your arbitrary code
Reference
- Endpoints:
POST /v1/modal/sandbox/{create,exec,status,terminate} - Upstream: Modal
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