Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add CDCgov/cdc-open-viz --skill bmad-create-architecturegit clone --depth 1 https://github.com/CDCgov/cdc-open-vizWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/cdcgov/cdc-open-viz/bmad-create-architecture)<a href="https://agentmods.dev/skills/cdcgov/cdc-open-viz/bmad-create-architecture"><img src="https://agentmods.dev/badge/skills/cdcgov/cdc-open-viz/bmad-create-architecture/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/cdcgov/cdc-open-viz/bmad-create-architecture"><img src="https://agentmods.dev/badge/skills/cdcgov/cdc-open-viz/bmad-create-architecture.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00039 | $0.00727 |
| Opus 5 | $0.00019 | $0.00364 |
| Sonnet 5 | $0.00008 | $0.00145 |
| Haiku 4.5 | $0.00004 | $0.00073 |
Grade A, and why
bmad-create-architecture scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
77% identical to bmad-checkpoint-preview — 44 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 75 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Architecture Workflow
Goal: Create comprehensive architecture decisions through collaborative step-by-step discovery that ensures AI agents implement consistently.
Your Role: You are an architectural facilitator collaborating with a peer. This is a partnership, not a client-vendor relationship. You bring structured thinking and architectural knowledge, while the user brings domain expertise and product vision. Work together as equals to make decisions that prevent implementation conflicts.
Conventions
- Bare paths (e.g.
steps/step-01-init.md) resolve from the skill root. {skill-root}resolves to this skill's installed directory (wherecustomize.tomllives).{project-root}-prefixed paths resolve from the project working directory.{skill-name}resolves to the skill directory's basename.
WORKFLOW ARCHITECTURE
This uses micro-file architecture for disciplined execution:
- Each step is a self-contained file with embedded rules
- Sequential progression with user control at each step
- Document state tracked in frontmatter
- Append-only document building through conversation
- You NEVER proceed to a step file if the current step file indicates the user must approve and indicate continuation.
On Activation
Step 1: Resolve the Workflow Block
Run: python3 {project-root}/_bmad/scripts/resolve_customization.py --skill {skill-root} --key workflow
If the script fails, resolve the workflow block yourself by reading these three files in base → team → user order and applying the same structural merge rules as the resolver:
{skill-root}/customize.toml— defaults{project-root}/_bmad/custom/{skill-name}.toml— team overrides{project-root}/_bmad/custom/{skill-name}.user.toml— personal overrides
Any missing file is skipped. Scalars override, tables deep-merge, arrays of tables keyed by code or id replace matching entries and append new entries, and all other arrays append.
Step 2: Execute Prepend Steps
What ships with it
13 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- architecture-decision-template.md 337 B
- customize.toml 1.6 KB
- data/domain-complexity.csv 2.1 KB
- data/project-types.csv 758 B
- steps/step-01-init.md 6.4 KB
- steps/step-01b-continue.md 6.1 KB
- steps/step-02-context.md 8.4 KB
- steps/step-03-starter.md 12 KB
- steps/step-04-decisions.md 10 KB
- steps/step-05-patterns.md 11 KB
- steps/step-06-structure.md 11 KB
- steps/step-07-validation.md 12 KB
- steps/step-08-complete.md 3.3 KB
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 75 lines · 39 tokens per session scan A b12711f1655c
bmad-create-architecture is a skill published in the GitHub repository CDCgov/cdc-open-viz (55 stars, last pushed today), licensed Apache-2.0. It adds 39 tokens to every session and 727 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 77% identical to bmad-checkpoint-preview, differing in 44 lines, and is treated as a copy.
Other skills, from other repositories
build-with-tinybase
Scaffold, extend, and verify reactive local-first JavaScript or TypeScript applications with TinyBase. Use when choosing TinyBase for in-memory tabular or key-value state, generating an app with create-tinybase, adding schemas or UI bindings, configuring browser or database persistence, configuring MergeableStore…
chatgpt-app-submission
Inspect a ChatGPT Apps MCP server codebase and generate chatgpt-app-submission.json with app info suggestions, tool hint justifications, test cases, and negative test cases, then report review-check findings and outputSchema warnings for submission review.
semiotic-charts
Build, repair, and verify charts in an existing Semiotic project, when Semiotic is explicitly requested, or when evaluating its documented capabilities against a visualization task. Preserve the project's dependency and runtime constraints; routine changes in another charting stack and tasks without a chart do not…
infographic-item-creator
Generate or update infographic Item components for this repo (TypeScript/TSX in src/designs/items). Use when asked to design, implement, or modify data item visuals, layout logic, or registerItem composites.
data_analyst
Analyses datasets with professional rigour — statistical summaries, clear narratives, and well-chosen visualisations.
frame-data-rollup
A native Remotion data frame — bars grow from zero by real data via spring physics while the figures roll 0→target in sync. The numbers come alive in a way a static HTML chart can't.