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Agent skill

AI Workflow Architect

by cbrock84 in cbrock84/headcount

Designs AI systems, automations, and agent workflows for a business — identifying which manual work is worth automating, how to structure the system, which tools fit, and what could go wrong.

MITAuto-check passed

Install AI Workflow Architect

skills CLI
$ npx skills add cbrock84/headcount --skill ai-workflow-architect -a claude-code

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

GitHub CLI
$ gh skill install cbrock84/headcount ai-workflow-architect --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/technology/skills/ai-workflow-architect .claude/skills/ai-workflow-architect && 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-workflow-architect
GitHub stars
2k
Token cost
~1.3k tokens
SKILL.md length
745 words
Files
2 (incl. references)
Skills in repo
178
Repo updated
First seen
Licence
MIT

At a glance

Designs AI systems, automations, and agent workflows for a business — identifying which manual work is worth automating, how to structure the system, which tools fit, and what could go wrong.

  • SKILL.md covers What is worth automating, Designing the system, Designing specialized assistants and Scoring candidates, plus 5 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

AI Workflow Architect is an agent skill from cbrock84/headcount. Designs AI systems, automations, and agent workflows for a business — identifying which manual work is worth automating, how to structure the system, which tools fit, and what could go wrong. Use this to automate part of an operation, design an agent or MCP workflow, reduce repetitive manual work, connect tools into a system, decide which automation to build first, or audit an automation that is not delivering.

Its SKILL.md is about 1.3k 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 works with Model Context Protocol. 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.

Example prompts

  • “Use the ai-workflow-architect skill to design AI systems, automations, and agent workflows for a business — identifying which manual work is worth…”
  • “/ai-workflow-architect”

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 Workflow Architect loads about 1.3k tokens when it runs, and up to ~1.5k if it reads all its reference files. Until then it costs about 109 tokens; SKILL.md has 745 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~109
When it runs · the whole SKILL.md, loaded when a task matches
~1.3k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~1.5k

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). 745 words, ~1,323 tokens.

Download SKILL.mdSave it as .claude/skills/ai-workflow-architect/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
ai-workflow-architect
description
Designs AI systems, automations, and agent workflows for a business — identifying which manual work is worth automating, how to structure the system, which tools fit, and what could go wrong. Use this to automate part of an operation, design an agent or MCP workflow, reduce repetitive manual work, connect tools into a system, decide which automation to build first, or audit an automation that is not delivering.

AI workflow architect

Most automation fails on selection, not implementation. The wrong process automated well is worse than the right process left manual, because now it is faster and harder to change.

What is worth automating

Score each candidate on four dimensions and require a real answer to each:

  • Frequency — how often, honestly measured. Weekly is usually the floor.
  • Time cost — per occurrence, times frequency. Most "huge time sinks" are twenty minutes a week.
  • Error rate and cost of error — where mistakes are expensive, automation pays even at low volume.
  • Stability — how often the process itself changes. A process that changes monthly will break monthly.

Then apply the disqualifiers. Do not automate a process that:

  • Nobody has documented. Automating an unexamined process encodes its accidents permanently.
  • Requires judgment you cannot specify. If you cannot write the rule, the system will produce confident wrong answers rather than stopping.
  • Fails silently. An automation whose failure is invisible is worse than no automation — the work stops happening and nobody notices for a month.
  • Should be eliminated instead. The best automation is deleting the step. Ask this before designing anything.

Designing the system

Start with the smallest loop that delivers value end to end, not the full vision. Systems that must be complete before they are useful usually never become either.

  • Deterministic where you can, model-driven where you must. Use a model for judgment and language; use ordinary code for routing, validation, and anything with a correct answer. Models are the expensive, non-deterministic part — spend them deliberately.
  • Put a human at the consequential step, not at every step. Approval on an irreversible action; no approval on a draft.
  • Make failure loud. Every automation needs a defined failure mode, a place the failure surfaces, and someone who sees it.
  • Idempotence matters more than it seems. Reruns happen. A workflow that double-sends on retry will eventually double-send.

Designing specialized assistants

Where a workflow needs judgment repeatedly, define a role rather than writing a prompt each time. A role carries: what it is accountable for, the inputs it can rely on, the output shape it must produce, what it must escalate rather than decide, and what it must never do.

Keep roles narrow. A single assistant asked to research, decide, and write produces mediocre versions of all three; three narrow ones with defined handoffs produce work you can inspect at each stage.

Pair any role that produces work with something that checks it — a rule, a test, or a separate reviewing role. A role that reviews its own output approves it.

Show full SKILL.md (321 more words)Show less

Scoring candidates

Score each automation candidate on frequency, time cost, error cost, and stability from one to five, then multiply rather than average — multiplication makes a low score on any dimension disqualifying, which is the correct behavior. A daily task that changes weekly should not survive on frequency alone.

Rank by score ÷ build effort, and take the top item only. Automation programs fail by starting four things.

Choosing tools

Match to the constraint that actually binds — volume, latency, existing stack, who maintains it, and what happens when the vendor changes terms. Prefer the boring option; a workflow platform your team already uses beats a better tool nobody will maintain.

Where an agent needs access to systems, prefer a defined tool interface over screen-driving. Tools fail explicitly; scrapers fail silently and at the worst time.

Sequencing

Build in this order: highest frequency × lowest complexity first. Early wins fund attention for harder ones, and the first automation teaches you what the second should look like.

Risks to state before building

Data leaving your control, model output reaching customers unreviewed, a silent dependency on a vendor's pricing, and the maintenance burden landing on one person. Name the owner of each before building, not after.

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

  • Automate a process nobody has written down. You will automate the exceptions along with the work.
  • Build before you know what a wrong output costs and who is positioned to catch it.
  • Put a model where a rule would do. Deterministic steps should stay deterministic.
  • Ship without a human review point on any step that spends money, sends mail, or deletes data.

© 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/technology/skills/ai-workflow-architect of cbrock84/headcount.

  • SKILL.md
  • references/sources.md

Open the folder on GitHubat commit 98d1c17

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Works with

Questions about AI Workflow Architect

What does AI Workflow Architect do?

Designs AI systems, automations, and agent workflows for a business — identifying which manual work is worth automating, how to structure the system, which tools fit, and what could go wrong. AI Workflow Architect is an agent skill from cbrock84/headcount. Designs AI systems, automations, and agent workflows for a business — identifying which manual work is worth automating, how to structure the system, which tools fit, and what could go wrong.

How do I install AI Workflow Architect in Claude Code?

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

How do I install AI Workflow Architect in Codex?

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

Can I use AI Workflow Architect 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-workflow-architect -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-workflow-architect, .gemini/skills/ai-workflow-architect, .github/skills/ai-workflow-architect and .opencode/skills/ai-workflow-architect in your project.

What does AI Workflow Architect need to run?

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

Does AI Workflow Architect 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 Workflow Architect 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 Workflow Architect use?

AI Workflow Architect 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 Workflow Architect use?

About 1.3k tokens (SKILL.md is roughly 5.3k 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 219 tokens, read only when the agent opens those files.

What are the alternatives to AI Workflow Architect?

Skills that share tags, products or a category with AI Workflow Architect: MCP Server Builder (anthropics/skills, 180k stars), MCP Server Builder (shareAI-lab/learn-claude-code, 78k stars), MCP Integration for Plugins (anthropics/claude-plugins-official, 38k stars) and Figma use_figma Plugin API Rules (warpdotdev/warp, 65k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains AI Workflow Architect?

cbrock84 (a GitHub user) maintains it in cbrock84/headcount, which has 2,016 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.