Reproduce Chat States
different-ai/openwork
Fires known chat states in the running OpenWork desktop app, such as provider errors, retries and tool steps, so you can check how each renders.
Use this DeepCTR sub-skill for Keras-style CTR and recommender models, model selection, compile-fit-predict workflows, save/load, and tiny DeepFM smoke tests.
Install Keras Model Workflows
$ npx skills add VectorSpaceLab/AREX-Skill --skill keras-model-workflows -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill keras-model-workflows --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/deepctr/sub-skills/keras-model-workflows .claude/skills/keras-model-workflows && 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 "keras-model-workflows" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/deepctr/sub-skills/keras-model-workflows into .claude/skills/keras-model-workflows/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "keras-model-workflows", 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/deepctr/sub-skills/keras-model-workflowsType 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 keras-model-workflows -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill keras-model-workflows --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/deepctr/sub-skills/keras-model-workflows .agents/skills/keras-model-workflows && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "keras-model-workflows" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/deepctr/sub-skills/keras-model-workflows into .agents/skills/keras-model-workflows/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "keras-model-workflows", 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 keras-model-workflows -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill keras-model-workflows --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/deepctr/sub-skills/keras-model-workflows .cursor/skills/keras-model-workflows && 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 "keras-model-workflows" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/deepctr/sub-skills/keras-model-workflows into .cursor/skills/keras-model-workflows/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "keras-model-workflows", 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/deepctr/sub-skills/keras-model-workflows--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 keras-model-workflows -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill keras-model-workflows --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/deepctr/sub-skills/keras-model-workflows .gemini/skills/keras-model-workflows && 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 "keras-model-workflows" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/deepctr/sub-skills/keras-model-workflows into .gemini/skills/keras-model-workflows/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "keras-model-workflows", 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 keras-model-workflowsInstalls 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 keras-model-workflows -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/deepctr/sub-skills/keras-model-workflows .github/skills/keras-model-workflows && 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 "keras-model-workflows" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/deepctr/sub-skills/keras-model-workflows into .github/skills/keras-model-workflows/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "keras-model-workflows", 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 keras-model-workflows -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 keras-model-workflows --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/deepctr/sub-skills/keras-model-workflows .opencode/skills/keras-model-workflows && 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 "keras-model-workflows" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/deepctr/sub-skills/keras-model-workflows into .opencode/skills/keras-model-workflows/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "keras-model-workflows", 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
keras-model-workflows- GitHub stars
- 328
- Token cost
- ~888 tokens
- SKILL.md length
- 260 words
- Files
- 6 (incl. scripts, references)
- Skills in repo
- 157
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Use this DeepCTR sub-skill for Keras-style CTR and recommender models, model selection, compile-fit-predict workflows, save/load, and tiny DeepFM smoke tests.
- Tasks that involve QA and bug reports
- SKILL.md covers When to use this sub-skill, Route map, Minimal Keras CTR workflow and Smoke test, plus 1 more section
- Runs Python scripts from its folder; calls python
What it does
Keras Model Workflows is an agent skill from VectorSpaceLab/AREX-Skill. Use this DeepCTR sub-skill for Keras-style CTR and recommender models, model selection, compile-fit-predict workflows, save/load, and tiny DeepFM smoke tests.
Its SKILL.md is about 890 tokens, which your agent loads only when the skill is triggered. The skill folder holds 7 other files, including scripts and reference files (for example `references/api-reference.md`, `references/model-catalog.md` and `references/troubleshooting.md`).
It sits in Testing & QA, covering QA and bug reports. The repository describes itself as: A Skill Library for Automated Machine Learning. The licence is Apache-2.0.
When your agent uses it
- Tasks that involve QA and bug reports
Example prompts
- “/keras-model-workflows”
Requirements
- Python 3
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.
Shell commands in SKILL.md call:
python
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
Keras Model Workflows loads about 888 tokens when it runs, and up to ~10k if it reads all its reference files. Until then it costs about 45 tokens; SKILL.md has 260 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 Apache-2.0 licence (© VectorSpaceLab). 260 words, ~888 tokens.
Download SKILL.mdSave it as
.claude/skills/keras-model-workflows/SKILL.md (or your agent's skills folder). This skill also uses 5 other files; get the full folder from GitHub.- name
- keras-model-workflows
- description
- Use this DeepCTR sub-skill for Keras-style CTR and recommender models, model selection, compile-fit-predict workflows, save/load, and tiny DeepFM smoke tests.
- disable-model-invocation
- true
- metadata.disco-role
- operating
- license
- Apache 2.0
Keras Model Workflows
Use this sub-skill for DeepCTR's primary tf.keras.Model-style API: choose a CTR/recommender model, build Keras feature columns, compile, fit, evaluate, predict, save/load, and smoke-test an installation.
When to use this sub-skill
- The user asks for DeepFM, WDL, DCN, xDeepFM, AutoInt, FiBiNET, AFM, NFM, PNN, FGCNN, EDCN, or another single-output DeepCTR model.
- The task is binary CTR classification or scalar regression with tabular sparse/dense features.
- The user needs Keras
compile,fit,predict,evaluate,save_model,load_model, custom objects, callbacks, optimizers, or embedding extraction. - The user wants a safe DeepCTR smoke test that does not depend on example files.
Route map
- Read references/model-catalog.md to choose a model family and understand the supported Keras model constructors.
- Read references/workflows.md for end-to-end preprocessing, model creation, fitting, prediction, evaluation, save/load, embedding extraction, and AFM attention recipes.
- Read references/api-reference.md for verified constructor signatures and Keras method contracts.
- Read references/troubleshooting.md for TensorFlow compatibility, missing dependencies, input shape, string hashing, save/load, and optional GPU issues.
- Run scripts/keras_tiny_ctr_smoke.py to verify a public DeepCTR installation using synthetic data.
Minimal Keras CTR workflow
from deepctr.feature_column import DenseFeat, SparseFeat, get_feature_names
from deepctr.models import DeepFM
feature_columns = [
SparseFeat("user_id", vocabulary_size=10000, embedding_dim=8),
SparseFeat("item_id", vocabulary_size=50000, embedding_dim=8),
DenseFeat("score", 1),
]
feature_names = get_feature_names(feature_columns)
model_input = {name: frame[name].values for name in feature_names}
model = DeepFM(feature_columns, feature_columns, task="binary")
model.compile("adam", "binary_crossentropy", metrics=["binary_crossentropy"])
model.fit(model_input, labels, batch_size=256, epochs=3, validation_split=0.2)
pred = model.predict(model_input, batch_size=256)For feature-column construction details, route to ../data-and-feature-columns/SKILL.md.
Smoke test
From the generated skill root, run:
python sub-skills/keras-model-workflows/scripts/keras_tiny_ctr_smoke.py --task binary --save-load --jsonA successful run builds a tiny DeepFM, trains one epoch on synthetic data, predicts a (n, 1) output, and optionally verifies H5 save/load with DeepCTR custom_objects.
Boundaries
- Use ../sequence-models/SKILL.md for DIN, DIEN, DSIN, and BST history/session-specific conventions.
- Use ../multitask-models/SKILL.md for SharedBottom, ESMM, MMOE, and PLE multi-output models.
- Use ../estimator-workflows/SKILL.md for legacy
tf.estimatorworkflows. - Do not depend on original example scripts or sample files at runtime; use the bundled smoke script and references here.
© VectorSpaceLab, Apache-2.0. 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 5 other files (scripts, references) in skills/repositories/repo-skills/deepctr/sub-skills/keras-model-workflows of VectorSpaceLab/AREX-Skill.
- SKILL.md
- references/api-reference.md
- references/model-catalog.md
- references/troubleshooting.md
- references/workflows.md
- scripts/keras_tiny_ctr_smoke.py
Open the folder on GitHubat commit ac3fe1a
Compare with similar skills
Keras Model Workflows 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 |
|---|---|---|---|---|---|---|
| Keras Model Workflows this skillVectorSpaceLab/AREX-Skill | 328 | — | ~888 | Automated safety check: Pass | Apache-2.0 | |
| Reproduce Chat Statesdifferent-ai/openwork | 24k | — | ~673 | Automated safety check: Pass | Custom licence | |
| Dynamo Jira TicketDynamoDS/Dynamo | 2k | — | ~1.1k | Automated safety check: Pass | Apache-2.0 | |
| Minimal Run And Auditlllllllama/RigorPilot-Skills | 497 | 2 repos | ~691 | Automated safety check: Pass | MIT | |
| Moav E2EMotherofallVPNs/MoaV | 448 | — | ~1.9k | Automated safety check: Notes | MIT | |
| Anchor Reprolynxlangya/techne | 105 | 1 repos | ~1.2k | Automated safety check: Pass | MIT |
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Run and debug MoaV's end-to-end tests — real protocol connectivity (client-test.sh) and the moav CLI smoke test — against a LIVE server, via the self-hosted e2e workflow or a local test VPS.
Anchor Repro
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Author a new CORAL task — the three pieces that must line up (task.yaml, seed/, a packaged grader/), the coral init → coral validate → smoke-test loop, and how to pick a grader pattern (stdout…
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Related
Categories
Questions about Keras Model Workflows
What does Keras Model Workflows do?
Use this DeepCTR sub-skill for Keras-style CTR and recommender models, model selection, compile-fit-predict workflows, save/load, and tiny DeepFM smoke tests. Keras Model Workflows is an agent skill from VectorSpaceLab/AREX-Skill. Use this DeepCTR sub-skill for Keras-style CTR and recommender models, model selection, compile-fit-predict workflows, save/load, and tiny DeepFM smoke tests.
When should I use Keras Model Workflows?
Keras Model Workflows fits situations like: tasks that involve QA and bug reports.
How do I install Keras Model Workflows in Claude Code?
Run `npx skills add VectorSpaceLab/AREX-Skill --skill keras-model-workflows -a claude-code`. Or copy the skill folder (skills/repositories/repo-skills/deepctr/sub-skills/keras-model-workflows in VectorSpaceLab/AREX-Skill) into .claude/skills/keras-model-workflows in your project. Claude Code loads it when a task matches its description.
How do I install Keras Model Workflows in Codex?
Run `npx skills add VectorSpaceLab/AREX-Skill --skill keras-model-workflows -a codex`. Or copy the skill folder (skills/repositories/repo-skills/deepctr/sub-skills/keras-model-workflows in VectorSpaceLab/AREX-Skill) into .agents/skills/keras-model-workflows in your project. Codex loads it when a task matches its description.
Can I use Keras Model Workflows 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 keras-model-workflows -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/keras-model-workflows, .gemini/skills/keras-model-workflows, .github/skills/keras-model-workflows and .opencode/skills/keras-model-workflows in your project.
What does Keras Model Workflows need to run?
Going by SKILL.md and its folder, Keras Model Workflows needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3.
Does Keras Model Workflows 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 Keras Model Workflows 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 Keras Model Workflows use?
Keras Model Workflows is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Keras Model Workflows use?
About 888 tokens (SKILL.md is roughly 3.6k 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 9.6k tokens, read only when the agent opens those files.
What are the alternatives to Keras Model Workflows?
Skills that share tags, products or a category with Keras Model Workflows: Reproduce Chat States (different-ai/openwork, 24k stars), Dynamo Jira Ticket (DynamoDS/Dynamo, 2k stars), Minimal Run And Audit (lllllllama/RigorPilot-Skills, 497 stars) and Moav E2E (MotherofallVPNs/MoaV, 448 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Keras Model Workflows?
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.