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free-image-and-video-generation

AI Community
Fluxly profile Facts only: domain, agents, trust score, runtime, permissions and network
Domain
AI
Compatible agents
  • Claude Code
  • Cursor
  • Cline
  • Codex
  • Windsurf
  • Gemini CLI
  • +20
Trust score
83 / 100 · community maintained
Author / version / license
@xramjetx · no license declared
Token usage
Lean
Setup complexity
Guided setup
External API key
Required · Vendor-specific
Operating systems
Unspecified (assume cross-platform)
Runtime requirements
Python >=3.10
Permissions
  • Read-only
  • Write / modify
  • Shell exec
  • Env read
Network behavior
External requests
Install commands
26 variants

Profile is derived at build time from SKILL.md and install vectors. Subject to drift from author intent.

Heads up: 未限定 allowed-tools，默认拥有全部工具权限。; 检出高风险片段：pipe_curl_to_shell

Output preview free-image-and-video-generation.preview 
---
name: free-image-and-video-generation
description: Free local AI image and video processing toolkit with cloud AI generation. Local tools: upscale…
category: ai
runtime: Python
---

# free-image-and-video-generation output preview

## PART A: Task fit
- Use case: Free local AI image and video processing toolkit with cloud AI generation. Local tools: upscale (Real-ESRGAN), face enhance (GFPGAN/CodeFormer), background remove (rembg), object erase (LaMa), face swap (InsightFace), segment (FastSAM), media process (FFmpeg). Cloud tools: AI image/video generation via Atlas Cloud API (300+ models). For cloud generation, ALWAYS first use Atlas Cloud MCP tools (atlas_list_models, atlas_get_model_info) to find the model ID and parameter schema, then call scripts/ai-generate.py with the correct --model and parameters. Use when user asks to process, enhance, upscale, generate, or edit images/videos..
- Inputs: target material, constraints, expected output, and acceptance criteria.
- Evidence boundary: follow “Prerequisites / Available Tools / Usage” and do not present inference as author intent.

## PART B: Execution result
- **01** The card summarizes the use case; runtime output centers on “Free local AI image and video processing toolkit with cloud AI generation. Local tools: upscale (Real-ESRGAN), face enhance (GFPGAN/CodeFormer), background remove (rembg), object erase (LaMa), face swap (InsightFace), segment (FastSAM), media process (FFmpeg). Cloud tools: AI image/video generation via Atlas Cloud API (300+ models). For cloud generation, ALWAYS first use Atlas Cloud MCP tools (atlas_list_models, atlas_get_model_info) to find the model ID and parameter schema, then call scripts/ai-generate.py with the correct --model and parameters. Use when user asks to process, enhance, upscale, generate, or edit images/videos.”.
- **02** When the source has headings, the agent prioritizes “Prerequisites / Available Tools / Usage” so the result follows the author’s structure.
- **03** Typical output includes task judgment, concrete steps, required commands or file edits, validation, and follow-up options.
- **04** Risk context follows the fingerprint: read files, write/modify files, run shell commands, read environment variables; may access external network resources; requires Vendor-specific API keys.

## Running Rules
- read files, write/modify files, run shell commands, read environment variables; may access external network resources; requires Vendor-specific API keys.
- Validate with a small sample before expanding scope.
- Return the result, validation criteria, and next iteration options.
Interpretation is structured for decision-making; original keeps the upstream SKILL.md unchanged.

Decide Fit First

  • Core job: Free local AI image and video processing toolkit with cloud AI generation. Local tools: upscale (Real-ESRGAN), face enhance (GFP…
  • Best fit: Use it when the task has reusable inputs, steps, and validation criteria rather than a one-off answer.
  • Avoid forcing it: If the source lacks commands, platform support, or external-service evidence, keep those fields unknown instead of guessing.

Design Intent

  • Structure: The skill is organized around “Prerequisites”, “Available Tools”, “Usage”, “Image Upscale (Real-ESRGAN)”, showing how the author expects the agent to judge fit, collect context, and produce verifiable output.
  • Trigger evidence: Prioritize the author’s wording around when to use it, what context to collect, and what output shape to produce.
  • Evidence boundary: Author text states facts, repository files prove commands and paths, and Fluxly only adds fit, limits, and usage judgment.

How To Use It

  • Inputs: Provide target material, scope, expected result, forbidden changes, and validation method.
  • Invocation: Name free-image-and-video-generation directly; if the source includes slash commands, start with the command and then add task context.
  • Validation: Start small and check whether the result follows “Prerequisites / Available Tools / Usage” before expanding.

Boundaries And Review

  • Dependencies: Prepare Vendor-specific API keys before running a full task.
  • Permissions: Declared permissions include read / write / shell-exec / env-read; ask the agent to state file, command, and rollback boundaries before acting.
  • Quality bar: A useful result names the deliverable, evidence, and next action. Generic prose means the task needs tighter context.

Discussion

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