MCP Server Builder
anthropics/skills
Guides the design and implementation of Model Context Protocol servers in TypeScript or Python, from tool naming and error messages to evaluation.
Programmatic Deep Daze API use for Imagine and DeepDaze construction, prompt/image/encoding workflows, progress saving, optimizers, and troubleshooting.
MITAuto-check passed
Install Python API
$ npx skills add VectorSpaceLab/AREX-Skill --skill python-api -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill python-api --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/VectorSpaceLab/AREX-Skill.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/repositories/repo-skills/deep-daze/sub-skills/python-api .claude/skills/python-api && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "python-api" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/deep-daze/sub-skills/python-api into .claude/skills/python-api/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "python-api", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/deep-daze/sub-skills/python-apiType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add VectorSpaceLab/AREX-Skill --skill python-api -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill python-api --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/VectorSpaceLab/AREX-Skill.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/repositories/repo-skills/deep-daze/sub-skills/python-api .agents/skills/python-api && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "python-api" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/deep-daze/sub-skills/python-api into .agents/skills/python-api/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "python-api", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add VectorSpaceLab/AREX-Skill --skill python-api -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill python-api --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/VectorSpaceLab/AREX-Skill.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/repositories/repo-skills/deep-daze/sub-skills/python-api .cursor/skills/python-api && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "python-api" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/deep-daze/sub-skills/python-api into .cursor/skills/python-api/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "python-api", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/VectorSpaceLab/AREX-Skill.git --path skills/repositories/repo-skills/deep-daze/sub-skills/python-api--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add VectorSpaceLab/AREX-Skill --skill python-api -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill python-api --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/VectorSpaceLab/AREX-Skill.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/repositories/repo-skills/deep-daze/sub-skills/python-api .gemini/skills/python-api && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "python-api" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/deep-daze/sub-skills/python-api into .gemini/skills/python-api/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "python-api", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install VectorSpaceLab/AREX-Skill python-apiInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add VectorSpaceLab/AREX-Skill --skill python-api -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/VectorSpaceLab/AREX-Skill.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/repositories/repo-skills/deep-daze/sub-skills/python-api .github/skills/python-api && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "python-api" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/deep-daze/sub-skills/python-api into .github/skills/python-api/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "python-api", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add VectorSpaceLab/AREX-Skill --skill python-api -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill python-api --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/VectorSpaceLab/AREX-Skill.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/repositories/repo-skills/deep-daze/sub-skills/python-api .opencode/skills/python-api && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "python-api" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/deep-daze/sub-skills/python-api into .opencode/skills/python-api/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "python-api", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
python-api- GitHub stars
- 328
- Token cost
- ~770 tokens
- SKILL.md length
- 296 words
- Files
- 5 (incl. scripts, references)
- Skills in repo
- 157
- Repo updated
- First seen
- Licence
- MIT
At a glance
Programmatic Deep Daze API use for Imagine and DeepDaze construction, prompt/image/encoding workflows, progress saving, optimizers, and troubleshooting.
- Works in 6 steps: Decide the objective input: exactly one… → Pick conservative constructor settings… → Set open_folder=False for noninteractive… → …
- Imagine and DeepDaze construction
- SKILL.md covers Route first, Operating sequence, Minimal Python pattern and Safety notes
- Runs Python scripts from its folder
What it does
Python API is an agent skill from VectorSpaceLab/AREX-Skill. Programmatic Deep Daze API use for Imagine and DeepDaze construction, prompt/image/encoding workflows, progress saving, optimizers, and troubleshooting.
Its SKILL.md is about 770 tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files, including scripts and reference files (for example `references/api-reference.md`, `references/troubleshooting.md` and `references/workflow-recipes.md`).
It works with Python. The repository describes itself as: A Skill Library for Automated Machine Learning. The licence is MIT.
When your agent uses it
- Imagine and DeepDaze construction
- Prompt/image/encoding workflows
- Progress saving
- Troubleshooting
Example prompts
- “/python-api”
Requirements
- Python 3
Workflow steps
6 steps, taken from the first numbered list in SKILL.md.
- Decide the objective input: exactly one of text-only, image-only, combined text+image, or a precomputed CLIP encoding unless deliberately…
- Pick conservative constructor settings before instantiating Imagine; construction loads CLIP and may perform model retrieval through the…
- Set open_folder=False for noninteractive agents and control the current working directory because images, progress frames, story…
- Use exact optimizer names: AdamP, Adam, or DiffGrad.
- Run scripts/probe_api_surface.py --verify when a lightweight API check is needed without constructing Imagine or downloading CLIP weights.
- Use the references for details
What it can do on your machine
Read from SKILL.md and the folder at commit ac3fe1a. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships 1 file in scripts/ (Python), which the agent can run.
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Python API loads about 770 tokens when it runs, and up to ~6.5k if it reads all its reference files. Until then it costs about 41 tokens; SKILL.md has 296 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.
SKILL.md
The full file from VectorSpaceLab/AREX-Skill at commit ac3fe1a, republished under its MIT licence (© VectorSpaceLab). 296 words, ~770 tokens.
Download SKILL.mdSave it as
.claude/skills/python-api/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.- name
- python-api
- description
- Programmatic Deep Daze API use for Imagine and DeepDaze construction, prompt/image/encoding workflows, progress saving, optimizers, and troubleshooting.
- disable-model-invocation
- true
- metadata.disco-role
- operating
- license
- MIT
python-api
Use this sub-skill when a task needs to call Deep Daze from Python rather than assemble shell commands. It covers safe Imagine and DeepDaze construction, prompt/image/custom-encoding choices, story mode, start-image priming, output path helpers, progress saving, optimizers, reproducibility knobs, and API-level failure modes.
Route first
- For command-line invocation, flags, quoting, overwrite prompts, and shell workflow construction, route to
../cli-workflows/SKILL.md. - For installation, CLIP model cache/download/network diagnosis, CUDA/CPU availability, package compatibility, and backend memory diagnosis, route to
../runtime-and-models/SKILL.md. - Stay here for Python call structure, constructor arguments, API defaults, helper methods, and code-level troubleshooting after the environment is usable.
Operating sequence
- Decide the objective input: exactly one of text-only, image-only, combined text+image, or a precomputed CLIP encoding unless deliberately replacing the target later with
set_clip_encoding. - Pick conservative constructor settings before instantiating
Imagine; construction loads CLIP and may perform model retrieval through the runtime cache. - Set
open_folder=Falsefor noninteractive agents and control the current working directory because images, progress frames, story transitions, GIFs, and videos are written relative to it. - Use exact optimizer names:
AdamP,Adam, orDiffGrad. - Run
scripts/probe_api_surface.py --verifywhen a lightweight API check is needed without constructingImagineor downloading CLIP weights. - Use the references for details:
references/api-reference.mdfor signatures, defaults, helper behavior, and method semantics.references/workflow-recipes.mdfor Python recipes including low/high VRAM presets.references/troubleshooting.mdfor constructor, prompt, image, story, optimizer, saving, determinism, and memory pitfalls.
Minimal Python pattern
from deep_daze import Imagine
imagine = Imagine(
text="a house in the forest",
epochs=1,
iterations=100,
save_every=25,
save_progress=True,
open_folder=False,
)
imagine()Safety notes
Imagine(...)is not a cheap metadata operation: instantiate only when ready for CLIP loading and potential cache use.- Regular text prompts must fit CLIP tokenization; use story mode for longer prose but still validate each story segment.
DeepDazeis the lower-level SIREN module and requires an already loaded CLIP perceptor, normalization transform, input resolution, total-batch count, and batch settings.
© VectorSpaceLab, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 4 other files (scripts, references) in skills/repositories/repo-skills/deep-daze/sub-skills/python-api of VectorSpaceLab/AREX-Skill.
- SKILL.md
- references/api-reference.md
- references/troubleshooting.md
- references/workflow-recipes.md
- scripts/probe_api_surface.py
Open the folder on GitHubat commit ac3fe1a
Compare with similar skills
Python API next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Python API this skillVectorSpaceLab/AREX-Skill | 328 | — | ~770 | Automated safety check: Pass | MIT | |
| MCP Server Builderanthropics/skills | 180k | 64 repos | ~2.3k | Automated safety check: Pass | Apache-2.0 | |
| PDF Processinganthropics/skills | 180k | 48 repos | ~2k | Automated safety check: Pass | Proprietary | |
| NotebookLM Research AssistantPleasePrompto/notebooklm-skill | 7.8k | 14 repos | ~2.4k | Automated safety check: Notes | MIT | |
| Manim Video Productionbrowser-use/video-use | 28k | 6 repos | ~3k | Automated safety check: Pass | MIT | |
| Code Review ChecklistshareAI-lab/learn-claude-code | 78k | 5 repos | ~1.1k | Automated safety check: Pass | MIT |
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Related
Works with
Questions about Python API
What does Python API do?
Programmatic Deep Daze API use for Imagine and DeepDaze construction, prompt/image/encoding workflows, progress saving, optimizers, and troubleshooting. Python API is an agent skill from VectorSpaceLab/AREX-Skill. Programmatic Deep Daze API use for Imagine and DeepDaze construction, prompt/image/encoding workflows, progress saving, optimizers, and troubleshooting.
When should I use Python API?
Python API fits situations like: imagine and DeepDaze construction; prompt/image/encoding workflows; progress saving; troubleshooting.
How do I install Python API in Claude Code?
Run `npx skills add VectorSpaceLab/AREX-Skill --skill python-api -a claude-code`. Or copy the skill folder (skills/repositories/repo-skills/deep-daze/sub-skills/python-api in VectorSpaceLab/AREX-Skill) into .claude/skills/python-api in your project. Claude Code loads it when a task matches its description.
How do I install Python API in Codex?
Run `npx skills add VectorSpaceLab/AREX-Skill --skill python-api -a codex`. Or copy the skill folder (skills/repositories/repo-skills/deep-daze/sub-skills/python-api in VectorSpaceLab/AREX-Skill) into .agents/skills/python-api in your project. Codex loads it when a task matches its description.
Can I use Python API in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add VectorSpaceLab/AREX-Skill --skill python-api -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/python-api, .gemini/skills/python-api, .github/skills/python-api and .opencode/skills/python-api in your project.
What does Python API need to run?
Going by SKILL.md and its folder, Python API needs Python for the scripts in its folder. Our summary lists: Python 3.
Does Python API access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Python API safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
What licence does Python API use?
Python API is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Python API use?
About 770 tokens (SKILL.md is roughly 3.1k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 5.7k tokens, read only when the agent opens those files.
What are the alternatives to Python API?
Skills that share tags, products or a category with Python API: MCP Server Builder (anthropics/skills, 180k stars), PDF Processing (anthropics/skills, 180k stars), NotebookLM Research Assistant (PleasePrompto/notebooklm-skill, 7.8k stars) and Manim Video Production (browser-use/video-use, 28k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Python API?
VectorSpaceLab (a GitHub organization) maintains it in VectorSpaceLab/AREX-Skill, which has 328 GitHub stars. The repository holds 157 skills in this directory. The repository was last updated on September 3, 2026.
Source: VectorSpaceLab/AREX-Skill on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.