Wonda CLI
Generate, edit, and publish AI images, video, music, and social content from your terminal — your coding agent already knows how to drive it.
- Source repo
- degausai/wonda
- Stars
- ★ 156
- Last updated
- 4d ago
- Primary language
- TypeScript
- FA score
- 56/100 · Major gaps
At a glance
- How it runs
- Works with
- Platform-specificCodex · Claude Code
- Cost
- Needs a paid subscription or licence
- Setup effort
- Low · running in minutes
- You'll need
- Typical use
- An indie e-commerce seller asks Claude Code to turn one product photo into a sora2 product video, add music and animated captions, then publish it to TikTok in one pipeline.
- Not a fit if
- Teams that need fully offline, local-only media processing
- Individuals unwilling to create a wonda.sh account or buy credits
- Teams needing self-hosting or source modification (proprietary licence)
- Source review
- 56/100 · Major gaps
What does this agent do, and when should you use it?
Wonda is a proprietary command-line tool that brings AI content generation to the terminal, covering images, video, music, speech, and direct social publishing. Every command outputs JSON to stdout, making it trivially consumable by shell scripts and AI coding agents such as Claude Code and Codex; it also ships skill files and native plugins so agents learn every command and workflow automatically. Generation jobs run in the wonda.sh cloud on a credit basis, while publishing connects directly to TikTok, Instagram, X, Reddit, and LinkedIn. Editing targets TikTok/Reels-style short-form video with more than 20 operations including animatedCaptions, splitScreen, lipsync, background removal, and upscaling. Installation is available via npm, Homebrew, and native agent plugins.
Wonda organizes capabilities into subcommands: generate image|video|text|music creates content from text prompts or reference images (example models include nano-banana-2, sora2, and suno-music); audio speech|transcribe|dialogue handles speech; edit video --operation ... offers editing ops such as animatedCaptions, textOverlay, merge, splitScreen, trim, speed, skipSilence, birefnet-bg-removal, topaz-video-upscale, and sync-lipsync-v2-pro; analyze video produces a composite frame grid plus an audio transcript for video understanding; publish instagram|tiktok (with carousel variants) posts directly; the linkedin, x, and reddit command families cover search, browsing, posting, likes, and messaging; scrape social|ads pulls competitor profiles and Meta Ads Library data; and blueprint manages reusable workflows. All output is JSON with built-in --jq field selection; --quiet returns only job IDs for piping, and JSON mode engages automatically when stdout is piped.
- An indie e-commerce seller asks Claude Code to turn one product photo into a sora2 product video, add music and animated captions, then publish it to TikTok in one pipeline.
- A social media manager batch-produces Reels-style clips: merging segments, burning animated captions, removing silence, and adjusting speed.
- A marketing team uses
scrape adsto search the Meta Ads Library for competitor ad creatives. - A personal brand operator manages LinkedIn and X posting, replies, likes, and DMs directly from the CLI.
- A podcast/short-form creator uses transcribe, enhanceAudio, and voiceExtractor to clean up and extract audio tracks.
How do you install or deploy this agent?
Install via npm or Homebrew:
bash
npm i -g @degausai/wondaor:
bash
brew tap degausai/tap && brew install wondaThen authenticate and install the agent skill file (a wonda.sh account is required):
bash
wonda auth loginwonda skill install -o .
Plugin installs for AI agents:
bash
npx skills add degausai/wondaClaude Code:
bash
/plugin marketplace add degausai/wonda
/plugin install wonda@degausaiGemini CLI:
bash
gemini extensions install https://github.com/degausai/wondaHow do you use this agent?
Authenticate and top up first (account at wonda.sh; generation costs credits):
bash
wonda auth login
wonda topup
wonda balanceGenerate an image:
bash
wonda generate image \
--model nano-banana-2 \
--prompt "Product photo of headphones on marble" \
--wait -o photo.pngGenerate a video from a reference image:
bash
MEDIA=$(wonda media upload ./product.jpg --quiet)
wonda generate video --model sora2 \
--prompt "Slow orbit, dramatic lighting" \
--attach "$MEDIA" --duration 8 --wait -o video.mp4Add TikTok-style animated captions:
bash
wonda edit video --operation animatedCaptions --media "$VID_MEDIA" \
--params '{"fontFamily":"TikTok Sans","position":"bottom-center","highlightColor":"#FFD700"}' \
--wait -o captioned.mp4Publish to Instagram:
bash
wonda publish instagram \
--media med_abc123 \
--account ig_acct_456 \
--caption "New drop. Link in bio."Extract fields with built-in jq:
bash
wonda generate image --model nano-banana-2 --prompt "A cat" --wait \
--jq '.outputs[0].media.url'What are this agent's strengths and limitations?
- Native agent integration: the skill file auto-syncs, so an agent reading --help can independently handle model selection and full workflows with no human learning curve.
- All-JSON output with built-in --jq and a --quiet mode makes it naturally scriptable for multi-step content pipelines (generate → music → captions → publish).
- A closed loop from generation through editing to direct publishing on TikTok/Instagram/X/Reddit/LinkedIn, plus competitor ad scraping and analytics.
- Proprietary licence with hard dependency on the wonda.sh cloud: generation consumes credits and cannot be self-hosted or run with local models.
- Requires an account and prepaid credits before any use; cost structure must be estimated via
wonda pricing estimate. - Social publishing and LinkedIn/X/Reddit actions rely on stored session credentials, adding credential-management and platform-compliance considerations.
How does this agent compare with similar options?
Key facts side by side with the most closely related agents.
| Agent | Source review | Form / cost | Stars | Updated | Language | Full support on |
|---|---|---|---|---|---|---|
| Wonda CLI This agent | 56 · Major gaps | CLIPaid | ★ 156 | 4d ago | TypeScript | Codex · Claude Code |
| md2wechat: AI-Agent CLI for WeChat Official Account Publishing | 47 · Major gaps | CLIFreemium | ★ 3.7k | 10d ago | Go | Codex · Claude Code |
| Hypit Video Workflows | 71 · Some gaps | Agent plugin / skillFree + model costs | ★ 19k | 1d ago | TypeScript | Codex · Claude Code |
| AIWriteX Content Studio | 43 · Major gaps | Desktop appFree + model costs | ★ 2k | 17d ago | Python | — |
How does FollowAgents rate this agent?
Why each dimension lost points
Evidence shows a closed-source, proprietary CLI distributed as binaries via npm/brew; code is not auditable, so source_attribution is 1. It can publish to TikTok/Instagram/LinkedIn/X/Reddit, scrape social profiles, store LinkedIn and X session credentials, and auto-accept Reddit chat requests, yet the README describes no confirmation gates, credential encryption, or data-flow details — least_privilege, user_confirmation, sensitive_data_handling, data_flow_transparency, and external_effects each get 1. dependency_security cannot be inspected in a closed binary: 1. Rollback is a relative bright spot: publish history, delete-post, unlike/unfollow/unretweet, and reddit delete provide partial undo paths — 2.
Command tables, examples, and output-format documentation are internally consistent — self_consistency 2. Built-in jq, multi-platform installers, and stdout/stderr separation reduce external dependencies — dependency_availability 2. But only one sentence ('Errors go to stderr') covers failure behavior; no error schema, codes, or retry semantics — failure_messages 1.
Target users (content creators and their AI agents) and scenarios are clearly described, with a skill-file mechanism for agent discovery — audience_and_scenarios and trigger_precision 2. Carousel limits, pricing estimate, and models list give partial capability boundaries — capability_boundaries 2. macOS/Linux/Windows x64+ARM64 coverage is good — environment_fit 2. Not full marks: no FAQ or failure-scenario guidance.
The README is highly structured with categorized command tables and full pipeline examples — information_architecture 3; npm/brew/plugin install paths are complete — install_notes 3. Rich examples but no FAQ — examples_and_faq 2. License is clearly proprietary but terms live off-site — license 2. No known-limitations section (1), no changelog beyond a release badge (1), maintenance responsibility only implied by release channels (1). Naming is consistent — naming_stability 2.
Output design is a standout: JSON stdout, --quiet, --fields, built-in --jq, and auto-JSON when piped make shell/agent orchestration easy — output_usability 3. Generation+editing+publish+analytics in one tool has real marginal value — 2. Credit-based billing with pricing estimate helps cost control — cost_benefit 2, though actual prices are opaque.
The command catalog is detailed but implementations and example results cannot be verified statically — claim_traceability 1. npm/homebrew/release badges are external assertions not corroborated in-repo — cross_source_corroboration 1. The README mostly separates factual command descriptions from marketing content — fact_inference_separation 2.
- The tool can publish, like, follow, DM, and auto-accept chat requests across multiple social platforms; an agent misfire can cause irreversible public actions — require human confirmation before delegating publish/like/follow/DM commands to an agent.
- LinkedIn and X session credentials are stored by the CLI; storage and encryption cannot be audited in a closed binary. Avoid use in high-trust environments.
- Features like 'scrape social' involve third-party platform scraping that may violate platform terms of service; compliance risk is on the user.
- Generation is credit-billed; run 'wonda pricing estimate' before bulk automation to prevent agent loops from draining your balance.
- License is proprietary with terms hosted off-site; review the formal terms at wonda.sh before commercial use.
FAQ
Does Wonda cost money?
wonda topup, check wonda balance, and pre-estimate costs with wonda pricing estimate.Does my AI agent need extra configuration?
wonda skill install, the skill file syncs in the background; the agent reads --help and the built-in skill to discover commands, models, and workflows on its own.Which platforms are supported?
What credentials are needed to publish to social platforms?
auth set and verified with auth check.Can I chain commands into a pipeline?
--quiet prints only IDs, --jq selects fields, and JSON mode activates automatically when stdout is piped. The README shows a full generate → music → captions → publish pipeline.