New: 97,000+ skills indexed from GitHub repos with 100+ stars · every skill safety-checkedBrowse top skillsTop skills
Esc to closeBrowse all skills
Submit a skillBrowse skills
Submit a skillBrowse skills

Agent skill

Mcaf ML AI Delivery

by managedcode in managedcode/Storage

Apply ML/AI project delivery guidance for data exploration, feasibility, experimentation, testing, responsible AI, and operating ML systems.

MITAuto-check passedAI & LLM Engineering

Install Mcaf ML AI Delivery

skills CLI
$ npx skills add managedcode/Storage --skill mcaf-ml-ai-delivery -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install managedcode/Storage mcaf-ml-ai-delivery --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/managedcode/Storage.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.codex/skills/mcaf-ml-ai-delivery .claude/skills/mcaf-ml-ai-delivery && rm -rf skills-src

Use ~/.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/

Facts

Skill name
mcaf-ml-ai-delivery
GitHub stars
138
Token cost
~1k tokens
SKILL.md length
461 words
Files
9 (incl. references)
Skills in repo
41
Repo updated
First seen
Licence
MIT

At a glance

Apply ML/AI project delivery guidance for data exploration, feasibility, experimentation, testing, responsible AI, and operating ML systems.

  • Works in 3 steps: Read the nearest AGENTS.md and confirm… → Run this skill's Workflow through the… → Return the Required Result Format with…
  • The repo includes model training
  • SKILL.md covers Trigger On, Value, Do Not Use For and Inputs, plus 7 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Mcaf ML AI Delivery is an agent skill from managedcode/Storage. Apply ML/AI project delivery guidance for data exploration, feasibility, experimentation, testing, responsible AI, and operating ML systems. Use when the repo includes model training, inference, data science workflows, or ML-specific delivery planning.

Its SKILL.md is about 1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 9 other files, including reference files (for example `references/data-exploration.md`, `references/feasibility-studies.md` and `references/ml-ai-projects.md`). Compatibility notes: Requires repository access when ML/AI docs, experiments, or delivery guidance live in the repo.

It sits in AI & LLM Engineering, covering LLM guardrails, A/B testing and Data analysis. The repository describes itself as: Storage library provides a universal interface for accessing and manipulating data in different cloud blob storage providers. The licence is MIT.

When your agent uses it

  • The repo includes model training
  • Data science workflows
  • ML-specific delivery planning

Example prompts

  • “/mcaf-ml-ai-delivery”

Requirements

  • Compatibility (from SKILL.md): Requires repository access when ML/AI docs, experiments, or delivery guidance live in the repo.

Workflow steps

3 steps, taken from the first numbered list in SKILL.md.

  1. Read the nearest AGENTS.md and confirm scope and constraints.
  2. Run this skill's Workflow through the Ralph Loop until outcomes are acceptable.
  3. Return the Required Result Format with concrete artifacts and verification evidence.

What it can do on your machine

Read from SKILL.md and the folder at commit 5d32121. 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

    No scripts in the folder and no shell commands in SKILL.md.

    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.

  • Compatibility

    Requires repository access when ML/AI docs, experiments, or delivery guidance live in the repo.

    From compatibility in the SKILL.md frontmatter.

Context cost

Mcaf ML AI Delivery loads about 1k tokens when it runs, and up to ~9.2k if it reads all its reference files. Until then it costs about 68 tokens; SKILL.md has 461 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~68
When it runs · the whole SKILL.md, loaded when a task matches
~1k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~9.2k

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); files beside SKILL.md are not scanned.

SKILL.md

The full file from managedcode/Storage at commit 5d32121, republished under its MIT licence (© managedcode). 461 words, ~1,014 tokens.

Download SKILL.mdSave it as .claude/skills/mcaf-ml-ai-delivery/SKILL.md (or your agent's skills folder). This skill also uses 8 other files; get the full folder from GitHub.
name
mcaf-ml-ai-delivery
description
Apply ML/AI project delivery guidance for data exploration, feasibility, experimentation, testing, responsible AI, and operating ML systems. Use when the repo includes model training, inference, data science workflows, or ML-specific delivery planning.
compatibility
Requires repository access when ML/AI docs, experiments, or delivery guidance live in the repo.

MCAF: ML/AI Delivery

Trigger On

  • the repo contains model training, inference, experimentation, or data-science workflow
  • ML work needs explicit process, testing, or responsible-AI guidance
  • delivery discussion is mixing product, data, and model concerns

Value

  • produce a concrete project delta: code, docs, config, tests, CI, or review artifact
  • reduce ambiguity through explicit planning, verification, and final validation skills
  • leave reusable project context so future tasks are faster and safer

Do Not Use For

  • generic software delivery with no ML or data-science component
  • loading all ML references when only one stage is active

Inputs

  • the current ML stage: framing, data exploration, experimentation, training, inference, or operations
  • product assumptions, data assumptions, and model assumptions
  • current verification and responsible-AI expectations

Quick Start

  1. Read the nearest AGENTS.md and confirm scope and constraints.
  2. Run this skill's Workflow through the Ralph Loop until outcomes are acceptable.
  3. Return the Required Result Format with concrete artifacts and verification evidence.

Workflow

  1. Separate product assumptions, data assumptions, and model assumptions.
  2. Keep experimentation traceable and testable.
  3. Treat responsible AI, data quality, and ML-specific verification as first-class requirements.
  4. Load only the references that match the current ML stage.

Deliver

  • clearer ML/AI delivery guidance
  • better links between data, experimentation, verification, and responsible AI
  • docs that match how the ML system is built and validated

Validate

  • the active ML stage is explicit
  • experimentation and evaluation are traceable
  • responsible-AI and data-quality requirements are not bolted on at the end
Show full SKILL.md (221 more words)Show less

Ralph Loop

Use the Ralph Loop for every task, including docs, architecture, testing, and tooling work.

  1. Plan first (mandatory):
    • analyze current state
    • define target outcome, constraints, and risks
    • write a detailed execution plan
    • list final validation skills to run at the end, with order and reason
  2. Execute one planned step and produce a concrete delta.
  3. Review the result and capture findings with actionable next fixes.
  4. Apply fixes in small batches and rerun the relevant checks or review steps.
  5. Update the plan after each iteration.
  6. Repeat until outcomes are acceptable or only explicit exceptions remain.
  7. If a dependency is missing, bootstrap it or return status: not_applicable with explicit reason and fallback path.
Required Result Format
  • status: complete | clean | improved | configured | not_applicable | blocked
  • plan: concise plan and current iteration step
  • actions_taken: concrete changes made
  • validation_skills: final skills run, or skipped with reasons
  • verification: commands, checks, or review evidence summary
  • remaining: top unresolved items or none

For setup-only requests with no execution, return status: configured and exact next commands.

Load References

  • read references/ml-ai-projects.md first
  • open references/data-exploration.md, references/feasibility-studies.md, references/ml-fundamentals-checklist.md, references/model-experimentation.md, references/testing-data-science-and-mlops-code.md, references/responsible-ai.md, or references/ml-model-checklist.md only when that stage is active

Example Requests

  • "Define the delivery workflow for this ML feature."
  • "We need responsible-AI and testing guidance for this model."
  • "Separate product, data, and model decisions in our docs."

© managedcode, 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 8 other files (references) in .codex/skills/mcaf-ml-ai-delivery of managedcode/Storage.

  • SKILL.md
  • references/data-exploration.md
  • references/feasibility-studies.md
  • references/ml-ai-projects.md
  • references/ml-fundamentals-checklist.md
  • references/ml-model-checklist.md
  • references/model-experimentation.md
  • references/responsible-ai.md
  • references/testing-data-science-and-mlops-code.md

Open the folder on GitHubat commit 5d32121

Compare with similar skills

Mcaf ML AI Delivery 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.

Mcaf ML AI Delivery compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Mcaf ML AI Delivery this skillmanagedcode/Storage138—~1kAutomated safety check: PassMIT
Constitutional AI TrainingOrchestra-Research/AI-Research-SKILLs13k3 repos~2kAutomated safety check: PassMIT
Olore Tensorzero Latestolorehq/olore103—~1.6kAutomated safety check: PassMIT
Data Engineering Data Driven Featureaiskillstore/marketplace4306 repos~3kAutomated safety check: PassNone
Power Analysisgaasher/Agent-Loop-Skills174—~2.2kAutomated safety check: PassMIT
Amazon Bedrockaws/agent-toolkit-for-aws2.8k—~8.6kAutomated safety check: PassApache-2.0

Similar skills

  • Constitutional AI Training

    Orchestra-Research/AI-Research-SKILLs

    Explains how to train a model to be harmless with self-critique, revision and AI-generated preference feedback, with Hugging Face and TRL code for each stage.

    13k GitHub starsUsed in 3 repos~2k tokens
    AI & LLM EngineeringAuto-check passed

  • Olore Tensorzero Latest

    olorehq/olore

    Local TensorZero documentation reference (latest). An agent skill from olorehq/olore.

    103 GitHub stars~1.6k tokensUpdated today
    AI & LLM EngineeringAuto-check passed

  • Data Engineering Data Driven Feature

    aiskillstore/marketplace

    Build features guided by data insights, A/B testing, and continuous measurement using specialized agents for analysis, implementation, and experimentation.

    430 GitHub starsUsed in 6 repos~3k tokens
    Data & AnalyticsAuto-check passed

  • Power Analysis

    gaasher/Agent-Loop-Skills

    A skill your agent uses when the user is planning a two-arm comparison (an A/B test, a simple RCT, a behavioral study, or a two-model/two-config evaluation) and needs to size it and preregister it…

    174 GitHub stars~2.2k tokensUpdated 3 mo ago
    Data & AnalyticsAuto-check passed

  • Amazon Bedrock

    aws/agent-toolkit-for-aws

    Official

    Builds generative AI applications on Amazon Bedrock. An agent skill from aws/agent-toolkit-for-aws.

    2.8k GitHub stars~8.6k tokensUpdated today
    AI & LLM EngineeringAuto-check passed

  • Data Science

    majiayu000/claude-skill-registry

    A skill your agent uses when performing exploratory data analysis, statistical testing, data visualization, or building predictive models.

    666 GitHub starsUsed in 1 repo~4.3k tokens
    Data & AnalyticsAuto-check passed

More from managedcode/Storage

All 41 skills in this repo

  • Mcaf Adr Writing

    managedcode/Storage

    Create or update an ADR under docs/ADR/ for architectural decisions, dependency changes, data-model changes, or cross-cutting policy shifts.

    138 GitHub stars~951 tokensUpdated today
    Auto-check passed

  • Mcaf Agile Delivery

    managedcode/Storage

    Shape delivery workflow around backlog quality, roles, ceremonies, and engineering feedback.

    138 GitHub stars~883 tokensUpdated today
    Auto-check passed

  • Mcaf Architecture Overview

    managedcode/Storage

    Create or update docs/Architecture.md as the global architecture map for a solution.

    138 GitHub stars~950 tokensUpdated today
    Auto-check passed

  • Mcaf CI CD

    managedcode/Storage

    Design or refine CI/CD workflows, quality gates, release flow, and safe AI-assisted pipeline authoring.

    138 GitHub stars~981 tokensUpdated today
    Auto-check passed

  • Mcaf Code Review

    managedcode/Storage

    Prepare for, perform, or tighten code review workflow: PR scope, review checklist, reviewer expectations, and merge hygiene.

    138 GitHub stars~957 tokensUpdated today
    Auto-check passed

  • Mcaf Devex

    managedcode/Storage

    Improve developer experience for multi-component solutions: onboarding, F5 contract, cross-platform tasks, local inner loop, and reproducible setup.

    138 GitHub stars~911 tokensUpdated today
    Auto-check passed

Categories

Questions about Mcaf ML AI Delivery

What does Mcaf ML AI Delivery do?

Apply ML/AI project delivery guidance for data exploration, feasibility, experimentation, testing, responsible AI, and operating ML systems. Mcaf ML AI Delivery is an agent skill from managedcode/Storage. Apply ML/AI project delivery guidance for data exploration, feasibility, experimentation, testing, responsible AI, and operating ML systems.

When should I use Mcaf ML AI Delivery?

Mcaf ML AI Delivery fits situations like: the repo includes model training; data science workflows; ML-specific delivery planning.

How do I install Mcaf ML AI Delivery in Claude Code?

Run `npx skills add managedcode/Storage --skill mcaf-ml-ai-delivery -a claude-code`. Or copy the skill folder (.codex/skills/mcaf-ml-ai-delivery in managedcode/Storage) into .claude/skills/mcaf-ml-ai-delivery in your project. Claude Code loads it when a task matches its description.

How do I install Mcaf ML AI Delivery in Codex?

Run `npx skills add managedcode/Storage --skill mcaf-ml-ai-delivery -a codex`. Or copy the skill folder (.codex/skills/mcaf-ml-ai-delivery in managedcode/Storage) into .agents/skills/mcaf-ml-ai-delivery in your project. Codex loads it when a task matches its description.

Can I use Mcaf ML AI Delivery 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 managedcode/Storage --skill mcaf-ml-ai-delivery -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/mcaf-ml-ai-delivery, .gemini/skills/mcaf-ml-ai-delivery, .github/skills/mcaf-ml-ai-delivery and .opencode/skills/mcaf-ml-ai-delivery in your project.

What does Mcaf ML AI Delivery need to run?

SKILL.md names no scripts, command-line tools or credentials: Mcaf ML AI Delivery is instructions for the agent only. Compatibility (from SKILL.md): Requires repository access when ML/AI docs, experiments, or delivery guidance live in the repo..

Does Mcaf ML AI Delivery 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 Mcaf ML AI Delivery 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. Review the folder before installing.

What licence does Mcaf ML AI Delivery use?

Mcaf ML AI Delivery is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Mcaf ML AI Delivery use?

About 1k tokens (SKILL.md is roughly 4.1k 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 8.2k tokens, read only when the agent opens those files.

What are the alternatives to Mcaf ML AI Delivery?

Skills that share tags, products or a category with Mcaf ML AI Delivery: Constitutional AI Training (Orchestra-Research/AI-Research-SKILLs, 13k stars), Olore Tensorzero Latest (olorehq/olore, 103 stars), Data Engineering Data Driven Feature (aiskillstore/marketplace, 430 stars) and Power Analysis (gaasher/Agent-Loop-Skills, 174 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Mcaf ML AI Delivery?

managedcode (a GitHub organization) maintains it in managedcode/Storage, which has 138 GitHub stars. The repository holds 41 skills in this directory. The repository was last updated on October 7, 2026.

Source: managedcode/Storage on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.