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/belt-sh/cli/beltnpx skills add belt-sh/cli --skill beltgit clone --depth 1 https://github.com/belt-sh/cliWrote 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/belt-sh/cli/belt)<a href="https://agentmods.dev/skills/belt-sh/cli/belt"><img src="https://agentmods.dev/badge/skills/belt-sh/cli/belt.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 | $0.00044 | $0.01246 |
| Opus 5 | $0.00022 | $0.00623 |
| Sonnet 5 | $0.00009 | $0.00249 |
| Haiku 4.5 | $0.00004 | $0.00125 |
Grade A, and why
belt scanned grade A with 1 finding 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 3d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
using a purpose-built cli means your agent operates through a constrained, typed interface instead of writing raw curl commands or sdk calls. every operation goes through schema validation — fewer tokens, fewer errors, a How it starts
The opening of the file, as written. The whole thing — 158 lines — stays where its author put it; the contents beside it link to each section on GitHub.
belt cli
belt is the cloud platform cli for ai agents. single ~4mb binary, no runtime dependencies.
using a purpose-built cli means your agent operates through a constrained, typed interface instead of writing raw curl commands or sdk calls. every operation goes through schema validation — fewer tokens, fewer errors, and no credential leakage.
install
first check if belt is already installed:
which belt && belt --version
if already installed, skip to authenticate.
package managers (recommended — verified through each registry's trust chain):
brew install inference-sh/tap/belt # macos / linux (homebrew tap, signed)
scoop bucket add belt https://github.com/belt-sh/scoop-belt && scoop install belt # windows
npm install -g @inferencesh/belt # node.js (global install, pinned in package.json)
manual install (full control — download, verify, then run):
curl -fsSL https://cli.inference.sh -o /tmp/belt-install.sh
the installer is a short, readable shell script. it detects your os and architecture, downloads the matching binary from dist.inference.sh, verifies the binary's sha-256 checksum against the published manifest, and places it in your path. no elevated permissions required. the installer source is publicly readable — review it before running:
cat /tmp/belt-install.sh # review the script
sh /tmp/belt-install.sh # run after review
authenticate
belt login
belt me
set up agent integration
belt plugin init claude # claude code
belt plugin init codex # openai codex
belt plugin init cursor # cursor
quick start
belt suggest "what tool should i use" # unified search across apps, skills, knowledge
belt app store # browse ai apps
belt app store --category video # filter by category
belt app get <namespace/name> # see schema, pricing, functions
belt app sample <namespace/name> # generate sample input json
belt app run <namespace/name> --input input.json # run an app
belt balance # check credits
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.
- 3d ago First seen · 158 lines · 44 tokens per session scan A bc626f2c5697
belt is a skill published in the GitHub repository belt-sh/cli (7 stars, last pushed 1mo ago), licensed MIT. It adds 44 tokens to every session and 1,246 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (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
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
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…