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Agent skill

AI ML Governance

by cbrock84 in cbrock84/headcount

Governs models and AI systems in production — intended use, evaluation, monitoring, human oversight, documentation, and the decision to deploy or retire.

MITAuto-check passedLegal & Compliance

Install AI ML Governance

skills CLI
$ npx skills add cbrock84/headcount --skill ai-ml-governance -a claude-code

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

GitHub CLI
$ gh skill install cbrock84/headcount ai-ml-governance --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/cbrock84/headcount.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/data-analytics/skills/ai-ml-governance .claude/skills/ai-ml-governance && 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
ai-ml-governance
GitHub stars
2k
Token cost
~1k tokens
SKILL.md length
567 words
Files
2 (incl. references)
Skills in repo
178
Repo updated
First seen
Licence
MIT

At a glance

Governs models and AI systems in production — intended use, evaluation, monitoring, human oversight, documentation, and the decision to deploy or retire.

  • Tasks that involve AI governance
  • SKILL.md covers Define intended use before…, Evaluation, Monitoring and Human oversight, plus 4 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

AI ML Governance is an agent skill from cbrock84/headcount. Governs models and AI systems in production — intended use, evaluation, monitoring, human oversight, documentation, and the decision to deploy or retire. Use this before deploying a model or AI feature, when defining evaluation criteria, when a model's behavior has drifted, when assessing AI risk or regulatory exposure, or when deciding whether an AI system is fit for a consequential decision.

Its SKILL.md is about 1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files, including reference files (for example `references/sources.md`).

It sits in Legal & Compliance, covering AI governance. The repository describes itself as: An agent organization structured as a company — 15+ departments, 125+ skills, each independently installable, citing the standards and regulators that settle the question. Runs… The licence is MIT.

When your agent uses it

  • Tasks that involve AI governance

Example prompts

  • “Use the ai-ml-governance skill to govern models and AI systems in production — intended use, evaluation, monitoring, human oversight, documentation…”
  • “/ai-ml-governance”

What it can do on your machine

Read from SKILL.md and the folder at commit 98d1c17. 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.

Context cost

AI ML Governance loads about 1k tokens when it runs, and up to ~2k if it reads all its reference files. Until then it costs about 103 tokens; SKILL.md has 567 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~103
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
~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 cbrock84/headcount at commit 98d1c17, republished under its MIT licence (© cbrock84). 567 words, ~1,038 tokens.

Download SKILL.mdSave it as .claude/skills/ai-ml-governance/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
ai-ml-governance
description
Governs models and AI systems in production — intended use, evaluation, monitoring, human oversight, documentation, and the decision to deploy or retire. Use this before deploying a model or AI feature, when defining evaluation criteria, when a model's behavior has drifted, when assessing AI risk or regulatory exposure, or when deciding whether an AI system is fit for a consequential decision.

AI and ML governance

Regimes governing automated decision-making differ by jurisdiction and sector and are changing quickly. Anything affecting credit, employment, housing, insurance, healthcare, or education carries specific legal obligations — involve Legal & Risk and qualified counsel rather than treating it as an engineering question.

Define intended use before evaluating anything

Write down what the system is for, what it is not for, who is affected by its output, and what happens when it is wrong. Most AI failures are use outside intended scope by someone who did not know the scope existed.

Then decide the consequence tier, because it sets everything after it:

  • Advisory — a human decides, the model suggests. Lightest oversight.
  • Assistive — the model acts, a human reviews before effect.
  • Autonomous — the model acts with effect. Highest bar, and rarely appropriate where a person is materially affected.

Evaluation

A held-out evaluation set that reflects real inputs, including the awkward ones. Built before deployment and kept stable, or you cannot compare versions.

  • Measure the failure that matters. Aggregate accuracy hides the errors you care about. A model that is 95% accurate and wrong disproportionately on one group is not 95% good.
  • Evaluate by segment, always. This is where fairness problems and quiet degradation appear.
  • Both error directions. False positives and false negatives usually have different costs, and the threshold should reflect that ratio rather than a default.
  • Establish a baseline. Compare against the current process — often a simple rule — not against zero. Plenty of models fail to beat the heuristic they replaced.

Monitoring

Models degrade silently: the world moves, inputs drift, and accuracy falls without any error being raised.

Monitor input distribution against training, output distribution over time, performance against whatever ground truth arrives later, and the rate of human override. A rising override rate is the best early warning you have, and it is usually already visible in a queue nobody reads.

Human oversight

Meaningful, not nominal. A reviewer approving hundreds of decisions an hour is not overseeing anything — they are laundering the model's output through a person.

Meaningful oversight requires the reviewer to see why the model decided, to have time to disagree, and to have their disagreement change the outcome and be recorded.

Show full SKILL.md (201 more words)Show less

Documentation

Per model: intended use and exclusions, training data and its provenance, evaluation results by segment, known limitations, monitoring in place, and the owner. This is what you need when someone asks why a decision was made — and increasingly what a regulator expects to see.

Retirement

Have a way to turn it off. Know what happens to the process when you do, and confirm the fallback still works — a manual path that has not been exercised in two years is not a fallback.

Sources

references/sources.md in this skill lists the outside authorities that settle the questions here — what each one is authoritative for, and what you may do with it. Check them before answering on anything they cover, and cite what you used. Most are free to read and not free to reproduce; the use note on each is binding.

Never

  • Deploy without an evaluation set and a monitoring plan.
  • Use a model outside its documented intended use because it seems to work.
  • Train or fine-tune on customer data without confirming the lawful basis covers it. The basis for collecting it rarely extends to this.
  • Let a model make a consequential decision about a person with no route to human review.

© cbrock84, 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 1 other file (references) in plugins/data-analytics/skills/ai-ml-governance of cbrock84/headcount.

  • SKILL.md
  • references/sources.md

Open the folder on GitHubat commit 98d1c17

Compare with similar skills

AI ML Governance 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.

AI ML Governance compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
AI ML Governance this skillcbrock84/headcount2k—~1kAutomated safety check: PassMIT
Caio Reviewalirezarezvani/claude-skills28k—~1.5kAutomated safety check: PassMIT
AI Ethics Reviewmohitagw15856/pm-claude-skills1.4k—~3.4kAutomated safety check: PassMIT
Iso42001Sushegaad/Claude-Skills-Governance-Risk-and-Compliance9461 repos~3.7kAutomated safety check: PassMIT
AI Risk Managementbriiirussell/cybersecurity-skills413—~3.7kAutomated safety check: NotesMIT
EU AI Act System Inventoryanthropics/claude-for-legal9.6k3 repos~2.8kAutomated safety check: PassApache-2.0

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Categories

Questions about AI ML Governance

What does AI ML Governance do?

Governs models and AI systems in production — intended use, evaluation, monitoring, human oversight, documentation, and the decision to deploy or retire. AI ML Governance is an agent skill from cbrock84/headcount. Governs models and AI systems in production — intended use, evaluation, monitoring, human oversight, documentation, and the decision to deploy or retire.

When should I use AI ML Governance?

AI ML Governance fits situations like: tasks that involve AI governance.

How do I install AI ML Governance in Claude Code?

Run `npx skills add cbrock84/headcount --skill ai-ml-governance -a claude-code`. Or copy the skill folder (plugins/data-analytics/skills/ai-ml-governance in cbrock84/headcount) into .claude/skills/ai-ml-governance in your project. Claude Code loads it when a task matches its description.

How do I install AI ML Governance in Codex?

Run `npx skills add cbrock84/headcount --skill ai-ml-governance -a codex`. Or copy the skill folder (plugins/data-analytics/skills/ai-ml-governance in cbrock84/headcount) into .agents/skills/ai-ml-governance in your project. Codex loads it when a task matches its description.

Can I use AI ML Governance 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 cbrock84/headcount --skill ai-ml-governance -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/ai-ml-governance, .gemini/skills/ai-ml-governance, .github/skills/ai-ml-governance and .opencode/skills/ai-ml-governance in your project.

What does AI ML Governance need to run?

SKILL.md names no scripts, command-line tools or credentials: AI ML Governance is instructions for the agent only.

Does AI ML Governance 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 AI ML Governance 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 AI ML Governance use?

AI ML Governance 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 AI ML Governance use?

About 1k tokens (SKILL.md is roughly 4.2k 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 982 tokens, read only when the agent opens those files.

What are the alternatives to AI ML Governance?

Skills that share tags, products or a category with AI ML Governance: Caio Review (alirezarezvani/claude-skills, 28k stars), AI Ethics Review (mohitagw15856/pm-claude-skills, 1.4k stars), Iso42001 (Sushegaad/Claude-Skills-Governance-Risk-and-Compliance, 946 stars) and AI Risk Management (briiirussell/cybersecurity-skills, 413 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains AI ML Governance?

cbrock84 (a GitHub user) maintains it in cbrock84/headcount, which has 2,022 GitHub stars. The repository holds 178 skills in this directory. The repository was last updated on September 17, 2026.

Source: cbrock84/headcount on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.