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 agentmods add skills/alanhuang168/ai-project-workflow/deploynpx skills add AlanHuang168/AI-Project-Workflow --skill deploygit clone --depth 1 https://github.com/AlanHuang168/AI-Project-WorkflowWrote 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/alanhuang168/ai-project-workflow/deploy)<a href="https://agentmods.dev/skills/alanhuang168/ai-project-workflow/deploy"><img src="https://agentmods.dev/badge/skills/alanhuang168/ai-project-workflow/deploy.svg" alt="Measured on agentmods" 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 | $0.00000 | $0.00579 |
| Opus 5 | $0.00000 | $0.00290 |
| Sonnet 5 | $0.00000 | $0.00116 |
| Haiku 4.5 | $0.00000 | $0.00058 |
Grade A, and why
deploy 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 5d 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.
How it starts
The opening of the file, as written. The whole thing — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.
name: deploy description: Prepare deployment instructions, release checks, verification, monitoring, and rollback. Use when the project is ready for deployment or when the user invokes /deploy. version: 1.0.0 stage: deploy
Goal
Produce a deployment guide that can be executed and verified for the target environment.
Use Cases
- The user invokes
/deploy. - Review is complete and the project needs release instructions.
- The user asks for deployment, release, rollback, or operational verification guidance.
Preconditions
reviewis complete, or the user explicitly approves skipping review and records the reason.- Build and runtime requirements are known.
Required Context
AGENTS.md.ai-workflow/state.jsoncore/templates/DEPLOY.template.mddocs/PRD.mddocs/ARCH.mddocs/SDD.mddocs/TEST.mddocs/REVIEW.md- Relevant build, configuration, and deployment files
Inputs
- Target environment.
- Build and runtime constraints.
- User-provided deployment requirements.
Allowed Changes
docs/DEPLOY.md- Necessary deployment configuration when explicitly within scope
.ai-workflow/state.json
Steps
- Read review results, architecture, build files, and deployment constraints.
- Identify environment requirements, configuration, secrets handling, build steps, deployment steps, verification, monitoring, and rollback.
- Do not invent credentials or claim access to systems that were not verified.
- Write
docs/DEPLOY.md. - Run available deployment-related validation if it is safe and in scope.
- Update workflow state:
documents.deploy = "complete", adddeploytocompletedStages, setcurrentStage = "retro", and update the timestamp.
Outputs
docs/DEPLOY.md- Updated
.ai-workflow/state.json
Acceptance Criteria
- DEPLOY includes environment requirements, configuration, build, deployment, migration, verification, monitoring, rollback, and release checklist.
- Secrets are not written into the repository.
- Verification results are reported honestly.
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.
- 5d ago First seen · 84 lines · 0 tokens per session scan A 375f2898ec0f
deploy is a skill published in the GitHub repository AlanHuang168/AI-Project-Workflow (27 stars, last pushed 1mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 579 tokens. 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 2026-08-30.
Other skills, from other repositories
data-charts-tako
Search and visualize the world's data - get charts, insights, and embeddable knowledge cards for finance, economics, demographics, sports, and more.
apollo-lead-finder
Two-phase Apollo.io prospecting: free People Search to discover ICP-matching leads, then selective enrichment to reveal emails/phones (credits per contact). Creates Apollo lists. Deduplicates against existing contacts by LinkedIn URL.
monorepo-management
Master monorepo management with Turborepo, Nx, and pnpm workspaces to build efficient, scalable multi-package repositories with optimized builds and dependency management. Use when setting up monorepos, optimizing builds, or managing shared dependencies.
browse-and-evaluate
Use when exploring the ai-agent-skills catalog to find, compare, and evaluate skills before installing. Always use --fields to limit output size and --dry-run before committing to an install.
render-3d-product-showcase
Assemble a premium 3D product-showcase ad from a config — four beat clips (an orbiting hero rotation, a macro push-in, a physics reveal, a typographic close) normalized to the brand-color canvas, hard-concatenated in order, closed on a deterministic Playwright brand end card, and mixed under one instrumental bed at…
render-airdrop-carousel
Assemble a viral iOS "AirDrop" notification-carousel video ad (≈6–8s, 9:16) from a brand line plus 6–16 real product photos — a native AirDrop share-sheet card ("Brand would like to share a · Decline / Accept") springs up and its preview window CYCLES through the products, landing on a range/lineup payoff with an…