Agent skill

Bmad Agent Builder

by redhat-cop in redhat-cop/vault-config-operator

Builds, edits or analyzes Agent Skills through conversational discovery.

Apache-2.0Auto-check passedAI & LLM Engineering

Install Bmad Agent Builder

skills CLI
$ npx skills add redhat-cop/vault-config-operator --skill bmad-agent-builder -a claude-code

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

GitHub CLI
$ gh skill install redhat-cop/vault-config-operator bmad-agent-builder --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/redhat-cop/vault-config-operator.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/bmad-agent-builder .claude/skills/bmad-agent-builder && 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
bmad-agent-builder
GitHub stars
167
Token cost
~1.4k tokens
SKILL.md length
764 words
Files
52 (incl. scripts, references, assets)
Skills in repo
22
Repo updated
First seen
Licence
Apache-2.0

At a glance

Builds, edits or analyzes Agent Skills through conversational discovery.

  • Works in 6 steps: Resolve customization. Run uv run… → Detect intent. If --headless or -H is… → Load config. Read… → …
  • The user requests to Create an Agent
  • SKILL.md covers Resolution rules, The three-type gradient, On Activation and Intents
  • Runs Python scripts from its folder; calls uv

What it does

Bmad Agent Builder is an agent skill from redhat-cop/vault-config-operator. Builds, edits or analyzes Agent Skills through conversational discovery. Use when the user requests to "Create an Agent", "Analyze an Agent" or "Edit an Agent".

Its SKILL.md is about 1.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 52 other files, including scripts, reference files and assets (for example `assets/BOND-template.md`, `assets/CAPABILITIES-template.md` and `assets/CREED-template.md`).

It sits in AI & LLM Engineering, covering Building AI agents. The repository describes itself as: An operator to support Haschicorp Vault configuration workflows from within Kubernetes. The licence is Apache-2.0.

When your agent uses it

  • The user requests to Create an Agent
  • Analyze an Agent

Example prompts

  • “Create an Agent”
  • “Analyze an Agent”
  • “Edit an Agent”
  • “/bmad-agent-builder”

Requirements

  • Python 3

Workflow steps

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

  1. Resolve customization. Run uv run {project-root}/_bmad/scripts/resolve_customization.py --skill {skill-root} --key agent and apply the…
  2. Detect intent. If --headless or -H is present, set {headless_mode}=true for every sub-prompt; this makes the builder non-interactive and…
  3. Load config. Read {project-root}/_bmad/config.yaml and {project-root}/_bmad/config.user.yaml (root and bmb section), falling back to…
  4. Open the floor (interactive only). Before any structured questions or routing, invite the user to share everything in mind: who the agent…
  5. Resume detection. Once a target agent is identified, glob {target-agent-path}/.memlog.md. If one exists, read it once in full to rebuild…
  6. Route to the intent. Pick the path below from the resolved intent and load only that file. Once the intent is routed, execute each entry…

What it can do on your machine

Read from SKILL.md and the folder at commit 99762c2. 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, from the files we listed), which the agent can run.

    Shell commands in SKILL.md call:

    • uv

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use uv, which can reach the network depending on how they are called.

    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

Bmad Agent Builder loads about 1.4k tokens when it runs, and up to ~39k if it reads all its reference files. Until then it costs about 45 tokens; SKILL.md has 764 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~45
When it runs · the whole SKILL.md, loaded when a task matches
~1.4k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~39k

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 redhat-cop/vault-config-operator at commit 99762c2, republished under its Apache-2.0 licence (© redhat-cop). 764 words, ~1,420 tokens.

Download SKILL.mdSave it as .claude/skills/bmad-agent-builder/SKILL.md (or your agent's skills folder). This skill also uses 51 other files; get the full folder from GitHub.
name
bmad-agent-builder
description
Builds, edits or analyzes Agent Skills through conversational discovery. Use when the user requests to "Create an Agent", "Analyze an Agent" or "Edit an Agent".

Overview

Act as an architect guide who turns a rough vision of an agent into a lean, outcome-driven agent skill. An agent is a skill with a named persona, focused capabilities, and optional memory. Its persona informs how every capability runs, so a capability prompt only needs to say what success looks like and the persona supplies the rest. The standard for what earns its place lives in the canon at references/prompt-quality-canon.md; this skill works to that standard rather than restating it. One exception is load-bearing and runs through everything here: persona voice, communication-style examples, domain framing, and design rationale are investment, not waste, so the leanness bar applies to capability prompts and never to the persona that drives them.

Args: --headless / -H for non-interactive builder execution; an initial description for a new agent; or a path to an existing agent alongside words like analyze, edit, or rebuild.

Resolution rules

  • Bare paths and {skill-root} (e.g. references/foo.md or {skill-root}/assets/bar.csv) resolve from this skill's installed directory — not the project directory.
  • {project-root} → the project working directory.
  • {target-agent-path} → the agent being built, edited, or analyzed.

The three-type gradient

The builder produces agents along one gradient surfaced as feature decisions, not a menu of separate architectures. Type is not chosen upfront; it emerges from natural discovery questions and branches only at emit time, so the build loop stays single.

  • Stateless ships its whole identity in one SKILL.md and handles isolated sessions with no memory.
  • Memory ships a lean bootloader SKILL.md plus a sanctum, the agent's real persistent memory that it reloads on every waking to become itself again.
  • Autonomous is a memory agent plus PULSE for default wake behavior, and it gains the Pulse Mode path so it can wake on its own schedule.

references/agent-type-guidance.md is the authority on the gradient and the routing questions.

On Activation

  1. Resolve customization. Run uv run {project-root}/_bmad/scripts/resolve_customization.py --skill {skill-root} --key agent and apply the resolved {agent.*} values throughout the session. On failure, read {skill-root}/customize.toml directly and use defaults. Then execute each entry in {agent.activation_steps_prepend} in order, and treat every entry in {agent.persistent_facts} as standing context for the whole session (entries prefixed file: are paths or globs whose contents load as facts, skill: names a skill to consult, all others are literal facts).

  2. Detect intent. If --headless or -H is present, set {headless_mode}=true for every sub-prompt; this makes the builder non-interactive and is not the Pulse Mode a built autonomous agent runs at its own runtime. Otherwise read the invocation for whether the user wants to Create, Edit, or Analyze, and which agent they mean.

  3. Load config. Read {project-root}/_bmad/config.yaml and {project-root}/_bmad/config.user.yaml (root and bmb section), falling back to {project-root}/_bmad/bmb/config.yaml. If none exist and bmad-bmb-setup is available, mention it. Resolve and apply throughout (defaults in parens): {user_name} (null), {communication_language} (user or system default), {document_output_language} (user or system default), and {bmad_builder_output_folder} ({project-root}/skills, where new agents are created; existing agents keep their own path).

  4. Open the floor (interactive only). Before any structured questions or routing, invite the user to share everything in mind: who the agent is, how it should make them feel, the core outcome, examples, half-formed ideas, paths to existing agents or artifacts. Adapt the invitation to what they already gave you, then one soft "anything else?" surfaces what they almost forgot. This dump replaces most downstream questioning, so let it run. Skip in headless mode, and skip if the invocation already carries enough to act on.

  5. Resume detection. Once a target agent is identified, glob {target-agent-path}/.memlog.md. If one exists, read it once in full to rebuild the prior session's state, then continue append-only through {project-root}/_bmad/scripts/memlog.py. This .memlog.md is the builder's process log and is separate from the agent's sanctum. In headless mode, resume automatically.

  6. Route to the intent. Pick the path below from the resolved intent and load only that file. Once the intent is routed, execute each entry in {agent.activation_steps_append} in order before the loop begins.

Show full SKILL.md (111 more words)Show less

Intents

IntentWhat it doesLoad
CreateBuild a new agent, or rebuild an existing one from its core outcomes and personareferences/build-process.md
EditChange specific behavior in an existing agent while preserving its designreferences/edit-guidance.md
AnalyzeRun the quality lenses over an agent and produce a reportreferences/quality-analysis.md

When the user hands over an existing agent without saying which intent, present the three-way choice and route on the answer: Analyze runs the lenses and returns an actionable report; Edit changes specific behavior while keeping the current approach; Rebuild rethinks from core outcomes and persona using the old agent as reference material, which is the Create flow pointed at existing input.

© redhat-cop, 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 51 other files (scripts, references, assets) in .agents/skills/bmad-agent-builder of redhat-cop/vault-config-operator.

  • SKILL.md
  • assets/BOND-template.md
  • assets/CAPABILITIES-template.md
  • assets/CREED-template.md
  • assets/INDEX-template.md
  • assets/MEMORY-template.md
  • assets/PERSONA-template.md
  • assets/PULSE-template.md
  • assets/SKILL-template-bootloader.md
  • assets/SKILL-template.md
  • assets/capability-authoring-template.md
  • assets/customize-template.toml
  • assets/first-breath-config-template.md
  • assets/first-breath-template.md
  • assets/init-sanctum-template.py
  • assets/memory-guidance-template.md
  • assets/prompt-quality-canon.md
  • assets/report-shell.html
  • assets/sample-customize-analyst.toml
  • assets/wake-template.py
  • … and 32 more

Open the folder on GitHubat commit 99762c2

Compare with similar skills

Bmad Agent Builder 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.

Bmad Agent Builder compared with similar skills
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Bmad Agent Builder this skillredhat-cop/vault-config-operator167—~1.4kAutomated safety check: PassApache-2.0
Agent BuildershareAI-lab/learn-claude-code78k5 repos~1.2kAutomated safety check: PassMIT
Paperclip Create Agentpaperclipai/paperclip99k1 repos~2.1kAutomated safety check: PassMIT
Senior Prompt Engineermaslennikov-ig/claude-code-orchestrator-kit2603 repos~1.4kAutomated safety check: PassCustom licence
Create Agentgnekt/My-Brain-Is-Full-Crew3.9k—~3.1kAutomated safety check: PassCustom licence
Ms Agent Framework RAGshuyu-labs/WebCode278—~1.1kAutomated safety check: PassCustom licence

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  • Bmad Workflow Builder

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Questions about Bmad Agent Builder

What does Bmad Agent Builder do?

Builds, edits or analyzes Agent Skills through conversational discovery. Bmad Agent Builder is an agent skill from redhat-cop/vault-config-operator. Builds, edits or analyzes Agent Skills through conversational discovery.

When should I use Bmad Agent Builder?

Bmad Agent Builder fits situations like: the user requests to Create an Agent; analyze an Agent.

How do I install Bmad Agent Builder in Claude Code?

Run `npx skills add redhat-cop/vault-config-operator --skill bmad-agent-builder -a claude-code`. Or copy the skill folder (.agents/skills/bmad-agent-builder in redhat-cop/vault-config-operator) into .claude/skills/bmad-agent-builder in your project. Claude Code loads it when a task matches its description.

How do I install Bmad Agent Builder in Codex?

Run `npx skills add redhat-cop/vault-config-operator --skill bmad-agent-builder -a codex`. Or copy the skill folder (.agents/skills/bmad-agent-builder in redhat-cop/vault-config-operator) into .agents/skills/bmad-agent-builder in your project. Codex loads it when a task matches its description.

Can I use Bmad Agent Builder 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 redhat-cop/vault-config-operator --skill bmad-agent-builder -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/bmad-agent-builder, .gemini/skills/bmad-agent-builder, .github/skills/bmad-agent-builder and .opencode/skills/bmad-agent-builder in your project.

What does Bmad Agent Builder need to run?

Going by SKILL.md and its folder, Bmad Agent Builder needs Python for the scripts in its folder and the command-line tools its instructions call (uv). Our summary lists: Python 3.

Does Bmad Agent Builder access the network?

SKILL.md contains no URLs. Its commands use uv, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Bmad Agent Builder 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 Bmad Agent Builder use?

Bmad Agent Builder is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Bmad Agent Builder use?

About 1.4k tokens (SKILL.md is roughly 5.7k 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 38k tokens, read only when the agent opens those files.

What are the alternatives to Bmad Agent Builder?

Skills that share tags, products or a category with Bmad Agent Builder: Agent Builder (shareAI-lab/learn-claude-code, 78k stars), Paperclip Create Agent (paperclipai/paperclip, 99k stars), Senior Prompt Engineer (maslennikov-ig/claude-code-orchestrator-kit, 260 stars) and Create Agent (gnekt/My-Brain-Is-Full-Crew, 3.9k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Bmad Agent Builder?

redhat-cop (a GitHub organization) maintains it in redhat-cop/vault-config-operator, which has 167 GitHub stars. The repository holds 22 skills in this directory. The repository was last updated on October 8, 2026.

Source: redhat-cop/vault-config-operator on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.