
LoopSkill
io.github.wisechef-aiv0.9.65更新於 Oct 11, 2026
Search, install and compose AI agent skills, loops and bundles from the LoopSkill marketplace.
概覽
讓助理透過 MCP 從 LoopSkill 市集搜尋、安裝並組合 AI 代理技能、迴圈與套件包。
- 功能
- LoopSkill 提供遠端 MCP 端點,背後是技能、迴圈與套件包的登錄庫。助理可以混合全文與向量搜尋技能、取得技能詳情與版本、瀏覽公開套件包、列出迴圈及其安全契約,並執行迴圈的驗證以取得客觀的通過或失敗結果。發布、執行與評分迴圈,以及安裝付費層級技能,都需要 API 金鑰;大多數讀取端點無需驗證。
- 適用情境
- 當助理需要從目錄中探索並拉取可重用的技能或套件包時,或當你希望執行迴圈內建的成功檢查並取得通過/失敗判定時,適合使用。若你打算自架同一登錄庫並讓 CLI 指向自己的執行個體,也同樣適用。
- 執行需求
- 需要透過網路存取供應商託管的遠端 MCP 端點。API 金鑰透過 x-api-key 標頭傳遞,金鑰以 rec_ 開頭,發布、執行、評分與安裝付費層級技能時必須提供。免費層級技能可匿名安裝。免費金鑰可透過代理自助註冊或在供應商網站建立取得。
安裝
在 SourceWeft 中
- 開啟 儀表板中的 LoopSkill,將其新增到工作區。
- 為需要使用其工具的對話啟用該服務。
Web executable,透過 Streamable HTTP。 遠端服務在工作區中設定後即可從網頁執行環境執行。
其他 MCP 客戶端
把它新增到你客戶端的 mcpServers 設定中。
{
"mcpServers": {
"loopskill": {
"type": "http",
"url": "https://app.loopskill.io/api/mcp/http/"
}
}
}README
LoopSkill
A local CLI for skills you already have, plus a self-hostable registry to
pull more from. Start with the CLI — it needs no account and makes no
network call for import/diff.
[CI] [License: MPL-2.0] [Stars] [loopskill.io] [MCP native]
60 seconds: see skill drift, reproducibly
This is a literal, self-contained transcript. Paste every command below into a clean shell — it fabricates two fake "skill" installs, edits one and deletes the other to simulate drift, then diffs. You will get exactly this output; nothing here depends on skills you already have installed.
Exit code is 1 — drift found, script- and CI-friendly. Run loopskill diff again with nothing changed and exit code is 0. That's the whole
pitch: two snapshots, one command, drift visible in the time it took to
read this paragraph — on the skills you actually have, not a demo, once
you drop --home.
import and diff make zero network calls — this isn't a promise in
a docstring, it's a structural guarantee: the network-capable code lives
in exactly one module (loopskill.pull) that import/diff never
import, and
cli/tests/test_loopskill_cli.py::test_import_and_diff_make_zero_network_calls
proves it by breaking socket.socket for the duration of those commands.
Full CLI reference, lockfile format, and pull/apply (the two commands
that DO touch the network, opt-in): cli/README.md.
What this repo actually is
Two things, and the CLI is the one to start with:
cli/— a local, offline-by-construction tool for the skills you already have on disk. No account, no server, no LoopSkill dependency forimport/diff. Point it at any registry that serves the same well-known bundle-index shape forpull/apply, or never call those commands at all.app/— a self-hostable FastAPI registry (this repo) that the CLI'spull/applycan optionally talk to, and that also serves a browsable catalog at loopskill.io. The registry is not the reason to start here — the CLI working on your own machine, before you've made an account, is.
Loops: two limits stated up front
The registry also serves 10 vetted loops (scripts/seed_starter_catalog.py).
POST /api/loops/{slug}/run is synchronous and works anywhere. Putting a loop on
a fleet member so it fires on a schedule is a second path with two constraints
worth knowing before you build on it:
- A loop reports nothing unless its own prompt says to. Telemetry exists
only because the loop's prompt calls
scripts/loopskill-emit-run.sh. Nothing else observes a fire — not the scheduler, not the server. Omit that line and the loop runs forever while every dashboard shows zero. This is the reasonloop_runssat at 1 for a year. - Cron materialization is Hermes-only.
app/loop_apply.pywrites the Hermes scheduler's~/.hermes/cron/jobs.json, and nothing else speaks that format yet. On Codex, Claude or OpenCode hostsscripts/install-loop-apply.shrefuses rather than installing a cron that can never converge. The skill path is cross-vendor; the scheduled-loop path is not.
Both are covered end to end in docs/SELF_HOST.md.
Self-host the registry (optional, for pull/apply against your own instance)
Zero-config: SQLite, auto-generated dev secrets, a seeded starter catalog. Your dev API key is printed on first boot. Full guide, including the Postgres/production path: docs/SELF_HOST.md.
Then run a loop — the runner is live (no LLM needed for verify-mode):
A fresh registry that doesn't just list a loop — it executes the loop's success check under enforced bounds and hands you a verdict.
What's actually in this codebase (and the honest answer to "why so big")
333 app Python files, 81,817 lines of app code, 118 Alembic migrations,
482 test files (clean-checkout counts; the local tree carries one untracked
junk test that CI never sees), 2 GitHub stars, 0 forks (measured 2026-08-21 via gh repo view wisechef-ai/loopskill-api --json stargazerCount,forkCount).
That ratio is real and it isn't a good one. Issue
#68 asked about
it; the honest answer — including why the codebase grew from a working
recipe-search product's battle-tested auth/Stripe/sandbox stack rather
than from a blank registry, and the concrete cuts committed as a result —
is in
docs/decisions/2026-08-11-bundles0811-p4-issue-68-codebase-size.md.
Every number above is checked against a live filesystem measurement by
tests/test_readme_claims.py on every run — it fails the build if this
paragraph drifts from reality the way #68's original numbers did.
Core API surface (self-hosted registry)
MCP-native: agents (Claude Code, Cursor, anything speaking MCP) discover and install over the protocol. There's also a signed-URL tarball path for direct fetch.
Architecture
FastAPI + SQLAlchemy. The same alembic migration chain runs on SQLite
(self-host) and Postgres (hosted) — no create_all drift; the SQLite boot
replays the real migrations, so what you self-host is what production
runs. Full module layout: AGENTS.md.
Auth flow
API keys are rec_-prefixed and passed in the x-api-key header. Most
read endpoints (search, detail, discover) are unauthenticated. Free-tier
skills install anonymously with no key (200 + tarball); installing a
paid-tier skill, publishing, running, and rating all require a key.
Develop
Sandbox (Linux only): the kernel sandbox (
app/sandbox/) needs firejail or bubblewrap. Where neither is functional (macOS, hardened containers), the loop runner falls back to bounded mode — POSIX rlimits + scrubbed env + isolated workspace — so loops still run; the response declares whichconfinementlevel it achieved. Multi-tenant fleet owners setWR_LOOP_RUN_REQUIRE_SANDBOX=trueto refuse bounded-mode execution and require a real kernel sandbox.
Contributor guide for AI agents: AGENTS.md.
Why open-core
The whole registry is the OSS product (MPL-2.0). Self-host it anywhere —
docker compose up is the complete experience, not a teaser, and nothing
phones home. The hosted plan is "don't run it yourself," never a feature
gate. Same posture as n8n / PostHog / Supabase.
License
MPL-2.0 — see LICENSE. The whole registry is open source; we only charge for hosting it.
Links
- CLI (start here): cli/README.md
- Home: loopskill.io
- Self-host guide: docs/SELF_HOST.md
- Issue #68 answer: docs/decisions/2026-08-11-bundles0811-p4-issue-68-codebase-size.md
- Contributing (AI agents): AGENTS.md
來源:README.md,提交 2e141d1
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1- v0.9.65最新Oct 11, 2026


