Dstack

Tinjau · 75
Diindeks komunitas

Vendor-agnostic orchestration for training, inference and agentic workloads across NVIDIA, AMD, TPU, and Tenstorrent on clouds, Kubernetes, and bare metal.

Verified installs0
Star2.2K
Versi1.0.0
Kualitas100/100 · Sangat baik
Kepercayaan75/100 · Hanya sandbox
Audit90/100 · Perlu ditinjau

Profil aset

Agent pemrograman dan pengembangan

Code review, repo analysis, testing, CI, GitHub, DevOps, and developer workflow skills.

Lihat kategori

Skenario

GitHub automation

I need my agent to triage GitHub issues, review pull requests, and summarize repository changes.

Kecocokan Agent

Claude Code + Cursor + CLI

Cocok untuk Codex, Claude Code, Cursor, CLI, atau Agent khusus.

Pasang

Siap

npx skills add dstackai/dstack

Pemeliharaan

Terkini

1 hari sejak push

Risiko

Perlu ditinjau

Dependency or permission surface needs review

Kualitas GitHub

2.2K

100/100 Kualitas · 83/100 Kepercayaan

Tag cakupan

CodingGitHub automationagent-skillsskillsagentic-orchestration

Catatan ulasan

Dependency or permission surface needs review · Permission surface may require sandboxing

Kartu adopsi Agent

Kepercayaan, audit, dan kesiapan pemasangan dalam sekali lihat

Skor ini menggabungkan metadata repositori publik, sinyal ulasan OpenAgentSkill, kebaruan pemeliharaan, dan kesiapan pemasangan. Ini adalah sinyal shortlist, bukan pengganti peninjauan manusia.

Kualitas

Sangat baik
100

High-confidence pick with strong adoption and healthy maintenance signals.

Kepercayaan

Hanya sandbox
75

Kandidat berguna dengan sinyal kepercayaan yang kurang atau bercampur. Gunakan di ruang kerja terisolasi hingga loop hasil membuktikan kecocokan tugas.

Audit

Perlu ditinjau
90

Tinjauan yang dapat dibaca mesin tentang kesiapan pemasangan, metadata keamanan, pemeliharaan, dan risiko adopsi.

Trust Score OpenAgentSkill v5

Tinjauan manusia sebelum pemasangan

Jalankan hanya dalam sandbox dan bandingkan alternatif terdekat sebelum digunakan untuk kerja nyata.

PythonAI AgentsCodexClaude CodeCursor

Star

2.2K star GitHub

Aktivitas repositori

2.2K star dan 250 fork

Pemeliharaan

1 hari sejak push

Lisensi

MPL-2.0

Pasang

npx skills add dstackai/dstack

Keamanan pemasangan

Jalur pemasangan paket atau runtime standar

Cakupan izin

secrets or environment access, shell or command execution

Hasil Agent

Belum ada data hasil Agent

Dokumentasi

Konteks README/SKILL.md kuat

Ringkasan risiko

Tinjau sebelum produksi

  • Permission surface needs review: secrets or environment access, shell or command execution
  • Dependency/runtime risk: command execution surface, credential or environment access
  • Permission surface: secrets or environment access, shell or command execution

Kesiapan pemasangan

Jalur pemasangan tersedia

  • Jalur pemasangan tersedia
  • Bukti repositori tersedia
  • Lisensi dinyatakan
  • Belum ada bukti hasil Agent-Proven

Metadata yang dapat dibaca Agent

Data keputusan yang dapat dibaca mesin untuk skill ini.

Gunakan blok ini atau JSON tersemat untuk memutuskan apakah Agent perlu memasang skill ini, memilih alternatif, atau meminta tinjauan manusia terlebih dahulu.

Buka JSON

Tugas yang sesuai

  • alur kerja Local desktop
  • Tim Claude Code
  • Tim yang menghargai sinyal adopsi GitHub
  • Navigate local resources

Agent yang sesuai

PythonAI AgentsCodexClaude CodeCursorOpenAgentSkill CLICLI

Keputusan pemasangan

Perintah
npx skills add dstackai/dstack
Kebijakan
Tinjau
Tinjauan manusia
Ya

Kepercayaan dan risiko

Kepercayaan
75/100
Audit
90/100
Tingkat risiko
Perlu ditinjau

Lingkar hasil

Endpoint
/api/agent/outcome
ID event
resolve
Hasil
5

Perintah pemasangan

npx skills add dstackai/dstack

Jangan gunakan ketika

  • Tim yang membutuhkan SLA dengan dukungan vendor
  • Lingkungan berkompliansi tinggi tanpa tinjauan keamanan internal
  • No major risk signals from current metadata
  • Petunjuk izin berisiko tinggi: Shell or command execution, Secrets or environment access
  • Dependency or permission surface needs review

Keamanan Agent v2

50/100 · Hindari pemasangan otomatis

EksperimentalTinjau

Sparse or mixed signals. Useful for discovery, but not for autonomous installation.

Test manually in an isolated workspace and compare against safer alternatives.

Selesaikan via API

Tinggi

Eksekusi shell atau perintah

Metadata skill merujuk terminal, CLI, shell, subprocess, atau alur kerja eksekusi perintah.

Sedang

Akses jaringan

Skill kemungkinan mengambil halaman jarak jauh, API, repositori, atau layanan eksternal.

Sedang

Akses sistem file

Skill dapat membaca atau menulis file proyek, dokumen, artefak yang dihasilkan, atau status workspace lokal.

Tinggi

Secrets or environment access

Skill metadata references credentials, tokens, environment variables, or secret-bearing workflows.

  • Petunjuk izin berisiko tinggi: Shell or command execution, Secrets or environment access
  • Dependency or permission surface needs review

Target pemasangan

Pasang skill ini di alur Agent Anda

Gunakan endpoint publik untuk mengambil perintah, checklist keamanan, prompt target, dan tautan kanonis.

skill install

OpenAgentSkill CLI

Resolve policy, run the source installer safely, and report a verified install receipt.

$ npx --yes https://github.com/Leon-Drq/openagentskill/releases/download/cli-v0.2.1/openagentskill-0.2.1.tgz install dstackai-dstack

Rencana resolusi Agent

Biarkan Agent memverifikasi kecocokan sebelum memasang.

API Resolve mengembalikan skill utama, alternatif, kebijakan keamanan, catatan audit, target pemasangan, dan prompt siap pakai.

Buka rencana teks

Agent harus memeriksa

  • Task fit and alternatives from Resolve API.
  • Audit score, trust score, and safety policy warnings.
  • Install target compatibility for Codex, Claude Code, Cursor, or CLI.

Salin prompt

Task: Use Dstack in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20Dstack%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/dstackai-dstack/install
Install command: npx skills add dstackai/dstack
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.

Serah-terima Agent

Berikan jalur pemasangan kepada Agent, bukan direktori lain.

Gunakan endpoint publik untuk mengambil perintah, checklist keamanan, prompt target, dan tautan kanonis.

Buka API pemasangan

Prompt Agent

Use Dstack for this task. Review https://www.openagentskill.com/api/skills/dstackai-dstack/install, then install with: npx skills add dstackai/dstack

Metadata Registry

Profil yang dapat dibaca Agent untuk pemilihan skill otomatis.

API Registry menyediakan sinyal keputusan, kepercayaan, audit, use case, dan pemasangan tanpa mengikis UI.

Buka Manifest

Kecocokan Agent

100/100

Local desktop

Platform

Python, AI Agents, Claude Code, Cursor

Laporan audit

Perlu ditinjau · 90/100

Tinjauan yang dapat dibaca mesin tentang kesiapan pemasangan, metadata keamanan, pemeliharaan, dan risiko adopsi.

Lihat laporan auditLihat laporan evaluasi

Panel keputusan Agent

Pilihan utama untuk Local desktop

Use this as a leading candidate, then validate the README and install path in your own agent stack.

100
Kesiapan
Adopsi
Tahap

Peran di stack

Pilihan utama

Kecocokan utama

Local desktop

Label kepercayaan

Siap produksi

Jalur pemasangan

Perintah siap

Gunakan saat

  • alur kerja Local desktop
  • Tim Claude Code
  • Tim yang menghargai sinyal adopsi GitHub

Bukti

  • 2,216 star GitHub
  • recent repository activity
  • install command or GitHub repo available
  • profil kualitas 100/100
  • 19 event interaksi OpenAgentSkill

tinjau dulu

  • No major risk signals from current metadata

Jalur implementasi

  1. 1Pasang di Agent sandbox dan jalankan satu tugas Local desktop dari awal hingga akhir.
  2. 2Compare output quality, latency, and failure behavior against at least one alternative.
  3. 3Promote it into production only after reviewing repository permissions, license, and maintenance signals.

Profil kepercayaan

Hanya sandbox

Kandidat berguna dengan sinyal kepercayaan yang kurang atau bercampur. Gunakan di ruang kerja terisolasi hingga loop hasil membuktikan kecocokan tugas.

75
Trust Score OpenAgentSkill

Adopsi GitHub

Lulus

2.2K star GitHub

Aktivitas star/fork

Lulus

2.2K star dan 250 fork; aktivitas issue tidak tersedia dalam metadata saat ini

Pemeliharaan terbaru

Lulus

1 hari sejak push

Kejelasan lisensi

Lulus

MPL-2.0

Sinyal positif

  • Listing yang diverifikasi manual
  • Tinjauan AI disetujui
  • Jalur pemasangan tersedia
  • Bukti repositori tersedia
  • Repositori yang baru dipelihara
  • Sinyal adopsi GitHub yang bermakna
  • Perintah pemasangan tidak memiliki pola berisiko tinggi yang jelas
  • Loop hasil siap tetapi membutuhkan eksekusi Agent nyata pertama

Tinjau sebelum memasang

  • Permission surface needs review: secrets or environment access, shell or command execution
  • Dependency/runtime risk: command execution surface, credential or environment access
  • Permission surface: secrets or environment access, shell or command execution
  • Belum ada laporan hasil Agent nyata
  • Tinjauan manusia diperlukan sebelum pemasangan tanpa pengawasan

Tindakan yang disarankan

Jalankan hanya dalam sandbox dan bandingkan alternatif terdekat sebelum digunakan untuk kerja nyata.

Profil kualitas

Sangat baik kandidat untuk alur kerja Agent

High-confidence pick with strong adoption and healthy maintenance signals.

100
Star GitHub
2.2K
Keterkinian
1 hari lalu
Siap dipasang
Ya
Lisensi
MPL-2.0

Kecocokan alur kerja

Gunakan skill ini pada skenario berikut

Kecocokan alur kerja

Tambahkan ke alur kerja lengkap

Daftar alternatif

Bandingkan sebelum memasang

Similar skills that may fit this task.

Bandingkan semua

Ringkasan

--- name: dstack description: | dstack is an open-source control plane for GPU provisioning and orchestration across GPU clouds, Kubernetes, and on-prem clusters. ---

# dstack

## Overview

`dstack` provisions and orchestrates workloads across GPU clouds, Kubernetes, and on-prem via fleets.

**When to use this skill:** - Running or managing dev environments, tasks, or services on dstack - Creating, editing, or applying `*.dstack.yml` configurations - Managing fleets, volumes, gateways, and checking available offers

## How it works

`dstack` operates through three core components:

1. `dstack` server - Can run locally, remotely, or via dstack Sky (managed) 2. `dstack` CLI - Applies configurations and manages or inspects fleets, runs, logs, events, volumes, gateways, and offers; it uses project configurations stored in `~/.dstack/config.yml`, which can be managed with `dstack project` 3. `dstack` configuration files - YAML files ending with `.dstack.yml`

`dstack apply` shows a plan and submits configuration changes. For run configurations, it attaches when the run reaches `running` by default: it configures SSH access, forwards declared ports, and streams logs. With `-d`, it submits and exits.

## Quick agent flow (detached runs)

1) Show plan: `echo "n" | dstack apply -f <config>` 2) If plan is OK and user confirms, apply detached: `dstack apply -f <config> -y -d` 3) Check the run: `dstack run get <run-name> --json` 4) If dev-environment or task with ports and running: attach to surface IDE link/ports/SSH alias (agent runs attach in background); ask to open link 5) If attach fails in sandbox: request escalation; if not approved, ask the user to run `dstack attach` locally and share the output

**CRITICAL: Never propose `dstack` CLI commands or YAML syntaxes that don't exist.** - Only use CLI commands and YAML syntax documented here or verified via `--help` - If uncertain about a command or its syntax, check the links or use `--help`

**NEVER do the following:** - Invent CLI flags not documented here or shown in `--help` - Guess YAML property names - verify in configuration reference links - Run `dstack apply` for runs without `-d` in automated contexts (blocks indefinitely) - Retry failed commands without addressing the underlying error - Summarize or reformat tabular CLI output - show it as-is - Use `echo "y" |` when `-y` flag is available - Assume a command succeeded without checking output for errors

## Agent execution guidelines

### Output accuracy - **NEVER reformat, summarize, or paraphrase CLI output.** Display tables, status output, and error messages exactly as returned. - When showing command results, use code blocks to preserve formatting. - If output is truncated due to length, indicate this clearly (e.g., "Output truncated. Full output shows X entries.").

### Verification before execution - **When uncertain about any CLI flag or YAML property, run `dstack <command> --help` first.** - Never guess or invent flags. Example verification commands: ```bash dstack --help # List all commands dstack apply -h <configuration type> # Flags for apply per configuration type (dev-environment, task, service, fleet, etc) dstack fleet --help # Fleet subcommands dstack ps --help # Flags for ps ``` - If a command or flag isn't documented, it doesn't exist.

### Command timing and confirmation handling

**Commands that stream indefinitely in the foreground:** - `dstack attach` - `dstack apply` without `-d` for runs - `dstack ps -w`

Agents should avoid blocking: use `-d`, timeouts, or background attach. When attach is needed, run it in the background by default (`nohup ...`), but describe it to the user simply as "attach" unless they ask for a live foreground session.

When waiting programmatically for a specific run, use `dstack run get <run-name> --json` and read its top-level `status`. Run statuses are `pending`, `submitted`, `provisioning`, `running`, `terminating`, `terminated`, `failed`, and `done`; the last three are terminal. Stop waiting when the run reaches the state needed for the next action or a terminal status. Never parse or grep human-readable `dstack ps` output; its status column may display a job message such as `no offers`.

**All other commands:** Use 10-60s timeout. Most complete within this range. **While waiting, monitor the output** - it may contain errors, warnings, or prompts requiring attention.

**Confirmation handling:** - `dstack apply`, `dstack stop`, `dstack fleet delete` require confirmation - Use `-y` flag to auto-confirm when user has already approved - For `dstack stop`, always use `-y` after the user confirms to avoid interactive prompts - Use `echo "n" |` to preview `dstack apply` plan without executing (avoid `echo "y" |`, prefer `-y`)

**Best practices:** - Prefer modifying configuration files over passing parameters to `dstack apply` (unless it's an exception) - When user confirms deletion/stop operations, use `-y` flag to skip confirmation prompts

### Detached run follow-up (after `-d`)

After submitting a run with `-d` (dev-environment, task, service), first determine whether submission failed. If the apply output shows errors (validation, no offers, etc.), stop and surface the error.

If the run was submitted, check it with `dstack run get <run-name> --json`, then guide the user through relevant next steps: If you need to prompt for next actions, be explicit about the dstack step and command (avoid vague questions). When speaking to the user, refer to the action as "attach" (not "background attach"). - **Monitor status:** Report the current status and offer to keep watching. If watching, poll `dstack run get <run-name> --json` every 10-20 seconds until it reaches the state needed for the next action or a terminal status. - **Attach when running:** For agents, run attach in the background by default so the session does not block. Use it to capture IDE links/SSH alias or enable port forwarding; when describing the action to the user, just say "attach". - **Dev environments or tasks with ports:** Once `running`, attach to surface the IDE link/port forwarding/SSH alias, then ask whether to open the IDE link. Never open links without explicit approval. - **Services:** Prefer using service endpoints. Attach only if the user explicitly needs port forwarding or full log replay. - **Tasks without ports:** Default to `dstack logs` for progress; attach only if full log replay is required.

### Attaching behavior (blocking vs non-blocking)

`dstack attach` runs until interrupted and blocks the terminal. **Agents must avoid indefinite blocking.** If a brief attach is needed, use a timeout to capture initial output (IDE link, SSH alias) and then detach.

Note: `dstack attach` writes SSH alias info under `~/.dstack/ssh/config` (and may update `~/.ssh/config`) to enable `ssh <run name>`, IDE connections, port forwarding, and real-time logs (`dstack attach --logs`). If the sandbox cannot write there, the alias will not be created.

**Permissions guardrail:** If `dstack attach` fails due to sandbox permissions, request permission escalation to run it outside the sandbox. If escalation isn’t approved or attach still fails, ask the user to run `dstack attach` locally and share the IDE link/SSH alias output.

**Background attach (non-blocking default for agents):** ```bash nohup dstack attach <run name> --logs > /tmp/<run name>.attach.log 2>&1 & echo $! > /tmp/<run name>.attach.pid ``` Then read the output: ```bash tail -n 50 /tmp/<run name>.attach.log ``` Offer live follow only if asked: ```bash tail -f /tmp/<run name>.attach.log ``` Stop the background attach (preferred): ```bash kill "$(cat /tmp/<run name>.attach.pid)" ``` If the PID file is missing, fall back to a specific match (avoid killing all attaches): ```bash pkill -f "dstack attach <run name>" ``` **Why this helps:** it keeps the attach session alive (including port forwarding) while the agent remains usable. IDE links and SSH instructions appear in the log file -- surface them and ask whether to open the link (`open "<link>"` on macOS, `xdg-open "<link>"` on Linux) only after explicit approval.

If background attach fails in the sandbox (permissions writing `~/.dstack` or `~/.ssh`, timeouts), request escalation to run attach outside the sandbox. If not approved, ask the user to run attach locally and share the IDE link/SSH alias.

### Interpreting user requests

**"Run something":** When the user asks to run a workload (dev environment, task, service), use `dstack apply` with the appropriate configuration. Note: `dstack run` only supports `dstack run get --json` for retrieving run details -- it cannot start workloads.

**"Connect to" or "open" a dev environment:** If a dev environment is already running, use `dstack attach <run name> --logs` (agent runs it in the background by default) to surface the IDE URL (`cursor://`, `vscode://`, etc.) and SSH alias. If sandboxed attach fails, request escalation or ask the user to run attach locally and share the link.

## Configuration types

`dstack` supports run configurations (dev environments, tasks, and services) and infrastructure configurations (fleets, volumes, and gateways). Configuration files can be named `<name>.dstack.yml` or simply `.dstack.yml`.

**Common parameters:** All run configurations (dev environments, tasks, services) support many parameters including: - **Git integration:** Clone repos automatically (`repo`) or mount existing repos (`repos`) - **File upload:** Upload local files (`files`; see concept docs for examples) - **Docker support:** Use custom Docker images (`image`); use `docker: true` if you want to use Docker from inside the container (VM-based backends only) - **Environment:** Set environment variables (`env`), often via `.envrc`. Secrets are supported but less common. - **Storage:** Persistent network volumes (`volumes`), specify disk size - **Resources:** Define GPU, CPU, memory, and disk requirements

**Best practices:** - Prefer giving configurations a `name` property for easier management - When configurations need credentials (API keys, tokens), list only env var names in the `env` section (e.g., `- HF_TOKEN`), not values. Recommend storing actual values in a `.envrc` file alongside the configuration, applied via `source .envrc && dstack apply`. - `python` and `image` are mutually exclusive in run configurations. If `image` is set, do not set `python`.

### `files` and `repos` intent policy

Use `files` and `repos` only when the user intends to use local/repo files inside the run.

- If user asks to use project code/data/config in the run, then add `files` or `repos` as appropriate. - If it is totally unclear whether files or repos must be mounted, ask one explicit clarification question or default to not mounting.

`files` guidance: - Relative paths are valid and preferred for local project files. - A relative `files` path is placed under the run's `working_dir` (default or set by user).

`repos` + image/working directory guidance: - With non-default Docker images, prefer explicit absolute mount targets for `repos` (e.g., `.:/dstack/run`). - When setting an explicit repo mount path, also set `working_dir` to the same path. - Reason: custom images may have a different/non-empty default working directory, and mounting a repo into a non-empty path can fail. - With `dstack` default images, the default `working_dir` is already `/dstack/run`.

### 1. Dev environments **Use for:** Interactive development with IDE integration (VS Code, Cursor, etc.).

```yaml type: dev-environment name: cursor

python: "3.12" ide: vscode

resources: gpu: 80GB ```

[Concept documentation](https://dstack.ai/docs/concepts/dev-environments.md) | [Configuration reference](https://dstack.ai/docs/reference/dstack.yml/dev-environment.md)

### 2. Tasks **Use for:** Batch jobs, training runs, fine-tuning, web applications, any exe

Kompatibilitas platform

pythonFULL
ai-agentsFULL

Detail teknis

Versi
1.0.0
Lisensi
MPL-2.0
Pembaruan terakhir
21 Agu 2026
Diterbitkan
6 Jun 2026

Framework dan alat

PythonAI Agents

Ringkasan keputusan

Pilihan utama

100
Siap
Adopsi
Tahap

2,216 star GitHub

Audit

Tinjauan pemasangan

Tinjauan pemasangan dan adopsi

90
Perlu ditinjau
Keamanan
77/100
Pemeliharaan
100/100
Pasang
92/100
Buka audit lengkapLihat laporan evaluasi

Bukti tervalidasi Agent

Bukti tervalidasi Agent

Laporan hasil setelah resolve, tinjau, pasang, dan satu eksekusi terbatas.

0
Terbukti
Needs first agent runPasang otomatis: tinjau duluTerakhir: Tidak diketahui
Tingkat sukses
Kegagalan terbaru
Hasil
0
Kualitas output
Gagal
0
Tidak relevan
0
Pemasangan
0
Diblokir risiko
0
Perlu penyiapan
0
Produksi
0

Belum ada data hasil Agent. Eksekusi pertama dapat melaporkan keberhasilan, kebutuhan setup, blok risiko, kegagalan, atau tidak relevan melalui /api/agent/outcome.

Pasang

Tambahkan ke alur Agent

Gratis dan sumber terbuka. Tinjau laporan sebelum memasang pada Agent produksi.

Siklus pertumbuhan

Kit berbagi

X

Draf berbasis skenario untuk Dstack, siap untuk posting manual di X.

Catatan kurator
Dstack: Vendor-agnostic orchestration for training, inference and agentic workloads across NVIDIA, AM...

2.2K stars

https://www.openagentskill.com/skills/dstackai-dstack?ref=x
Buka draf X
Balasan opsional dengan perintah pemasangan
Listing + install path for Dstack:
https://www.openagentskill.com/skills/dstackai-dstack?ref=x

Install: npx skills add dstackai/dstack
Buka draf balasan

Sumber listing

Diindeks komunitas

Dapat diklaim

Listing ini diindeks dari sumber publik dan belum ditandai resmi hingga klaim pemelihara disetujui.

Kreator
dstackai
Diindeks oleh
Indeks komunitas OpenAgentSkill

Atribusi menautkan ke repositori publik atau profil kreator. Kreator dapat mengklaim listing untuk memperbarui sinyal kepemilikan.

Klaim skill ini

Klaim pemilik

Klaim listing skill ini

Listing Diindeks komunitas ini dikaitkan dengan dstackai, tetapi belum ditandai resmi. Klaim untuk menambahkan sinyal pemilik terverifikasi dan membuat pembaruan peluncuran, pemasangan, serta audit berikutnya lebih tepercaya.

Kit backlink kreator

Tambahkan badge bukti ke README Anda

Tampilkan listing kanonis, sinyal kepercayaan dan audit saat ini, serta bukti Agent-Proven nyata di tempat pengembang mengevaluasi repositori.

[![Listed on OpenAgentSkill](https://www.openagentskill.com/api/badge/dstackai-dstack?metric=listed&label=Listed)](https://www.openagentskill.com/skills/dstackai-dstack)
[![OpenAgentSkill Trust](https://www.openagentskill.com/api/badge/dstackai-dstack?metric=trust&label=Trust)](https://www.openagentskill.com/skills/dstackai-dstack)
[![OpenAgentSkill Audit](https://www.openagentskill.com/api/badge/dstackai-dstack?metric=audit&label=Audit)](https://www.openagentskill.com/skills/dstackai-dstack/audit)
[![Agent Proven](https://www.openagentskill.com/api/badge/dstackai-dstack?metric=proven&label=Agent%20Proven)](https://www.openagentskill.com/skills/dstackai-dstack)

Penulis

D

dstackai

@dstackai

Kecocokan platform

Sinyal kesehatan

Star GitHub
2.2K
Skor kualitas
65/100
Push GitHub terakhir
21 Agu 2026
Petunjuk framework
2
Tampilan OpenAgentSkill
14
Salinan pemasangan
0
Klik keluar
0

Sinyal komunitas

Bagikan apakah skill ini bermanfaat untuk alur kerja Agent Anda. Masukan gabungan meningkatkan peringkat dari waktu ke waktu.

Kepercayaan & keamanan

Hanya sandbox

75
  • Adopsi GitHub2.2K star GitHubLulus
  • Aktivitas star/fork2.2K star dan 250 fork; aktivitas issue tidak tersedia dalam metadata saat iniLulus
  • Pemeliharaan terbaru1 hari sejak pushLulus
  • Kejelasan lisensiMPL-2.0Lulus
  • Kelengkapan README/SKILL.mdMetadata memuat konteks penggunaan dan alur kerja yang cukupLulus
  • Risiko dependensi/runtimecommand execution surface, credential or environment accessPerbaiki