Borrowing it
Nothing to install: this file belongs to OneHillAI/ASDD. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/OneHillAI/ASDD/main/.claude/commands/asdd/setup.mdgit clone --depth 1 https://github.com/OneHillAI/ASDDWrote 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/commands/onehillai/asdd/setup)<a href="https://agentmods.dev/commands/onehillai/asdd/setup"><img src="https://agentmods.dev/badge/commands/onehillai/asdd/setup/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/commands/onehillai/asdd/setup"><img src="https://agentmods.dev/badge/commands/onehillai/asdd/setup.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 | $0.00011 | $0.00259 |
| Opus 5.5 | $0.00004 | $0.00104 |
| Sonnet 5.5 | $0.00002 | $0.00052 |
| Haiku 4.5 | $0.00001 | $0.00026 |
Grade A, and why
setup 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 2mo 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.
What it actually says
Set up ASDD's agents for this repo.
Run asdd setup and walk the maintainer through it in plain language. It reads the roles from
.asdd.yml, writes the chosen model names back, and asks which spec tool validates readiness -
builtin (ASDD's definition of ready) or openspec (delegate to openspec validate). If they pick
OpenSpec, remind them to install it where the gate runs (npm install -g @fission-ai/openspec).
Hold these rules; do not let a convenient answer break them:
- Model NAMES go in
.asdd.yml. A key never does: that file is version-controlled. Credentials belong in Goose's own config (goose configure) or a CI secret (ASDD_RUNTIME_TOKEN). developeris bring-your-own: the contributor's own agent, not something the deployment runs.- The test models MUST differ from the developer's, or their blind spots line up and the tests
cheerfully confirm the bug.
asdd check-models --strictenforces it.
Finish by showing what is set and what is still unset.
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.
- 2mo ago First seen · 19 lines · 11 tokens per session scan A db8df3ecf8ad
setup is a command published in the GitHub repository OneHillAI/ASDD (6 stars, last pushed 8d ago), licensed Apache-2.0. It adds 11 tokens to every session and 259 once invoked, about $0.0000 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other commands, from other repositories
devops
Author CI/CD, containers, or IaC following the repo's platform — deploy and secrets flagged for human approval, never executed.
default-ai-review
Extract findings from the provided diff. Rank by severity. Produce a structured review. Emit one recommendation.
validate-pr-description
Use when validating a PR title and description for conventional commit format, issue linking keywords, and template compliance before submission.
analyst
Use when performing local analyst review before pushing PR changes. Assesses code quality, impact analysis, and maintainability.
boot
Start a new PromptKit prompt assembly session (alias for /bootstrap).
mlops
Invoke the MLOps Engineer for model serving & inference infrastructure, ML/AI pipelines, model deployment & monitoring, and infrastructure-level AI cost optimization.