deploy-agent

deploy-agent is a skill for Claude Code from oyi77/1ai-skills. It costs 17 tokens per session (1,057 once invoked), scanned A, original, MIT.

A deployment workflow for moving software artifacts through staging and production with checks before and after release. It includes rollback planning, so a failed release can be reversed.

In plain words
What is it for?
Use it for blue-green, rolling, canary, or hotfix deployments, health checks, database migrations, automatic rollbacks, and post-deployment monitoring.
Why use it?
It reduces the risk of deploying unverified code or database changes without a tested way back.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the 1ai-skills plugin — 187 skills, 4 commands shipped together

Good fit Use it for blue-green, rolling, canary, or hotfix deployments, health checks, database migrations, automatic rollbacks, and post-deployment monitoring.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/oyi77/1ai-skills/deploy-agent
Install

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.

Any agent
npx skills add oyi77/1ai-skills --skill deploy-agent
Clone the repo
git clone --depth 1 https://github.com/oyi77/1ai-skills

Made for: Claude Code.

Or install 1ai-skills, the plugin that ships this one along with the rest of its 187 skills, 4 commands.

Wrote 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.

agentmods badge for deploy-agent

README.md
[![agentmods](https://agentmods.dev/badge/skills/oyi77/1ai-skills/deploy-agent.svg)](https://agentmods.dev/skills/oyi77/1ai-skills/deploy-agent)
Your own site
<a href="https://agentmods.dev/skills/oyi77/1ai-skills/deploy-agent"><img src="https://agentmods.dev/badge/skills/oyi77/1ai-skills/deploy-agent.svg" alt="Measured on agentmods" height="20"></a>
Per session 17 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,057 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00017 $0.01057
Opus 5 $0.00009 $0.00528
Sonnet 5 $0.00003 $0.00211
Haiku 4.5 $0.00002 $0.00106

Measured 8d ago against content hash cb17d8aa2f80, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

deploy-agent scanned grade A with 1 finding 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 8d 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.

Runs shell commandslowCapability

Expected in a hook, worth knowing in a rule or an instructions file.

assert subprocess.run(["git", "diff", "--quiet"], cwd=".").returncode == 0, "Dirty working tree"
agents/autonomous/deploy-agent/SKILL.md · 119 lines

How it starts

The opening of the file, as written. The whole thing — 119 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Deploy Agent

Quick Reference — see parent for full agent ecosystem.

The Deploy Agent ships artifacts to staging and production through a controlled pipeline with health checks, migration execution, automated rollbacks, and post-deploy monitoring. Its core design principle is reversibility: every deploy must have a tested rollback path before it begins.

When Not to Use

  • Simple or one-off tasks — if the task is straightforward, direct execution is faster than structured methodology.
  • Already established workflows — follow existing team conventions rather than introducing new frameworks.
  • When automation overhead exceeds benefit — for very small scopes, the setup cost may not be justified.

Dependencies

  • Python 3.8+ or Node.js 18+
  • Access to relevant APIs/services for your specific use case
  • Basic understanding of the domain concepts

Commands

# Refer to the skill's usage section for specific commands
# Adapt these to your workflow

Key Responsibilities

  • Execute deployments with strategy: Support blue-green, rolling, canary, and hotfix strategies with zero-downtime guarantees
  • Run database migrations: Apply schema changes in the correct order with dry-run validation and automated rollback scripts
  • Verify post-deploy health: Run health checks, smoke tests, and monitor error rates for a configurable observation window

Code Example

"""Minimal deploy agent pattern — ship with verification."""

import json, subprocess, sys
from pathlib import Path

def deploy(target: str, tag: str, strategy: str = "rolling") -> dict:
    # 1. Pre-deploy checks
    assert subprocess.run(["git", "diff", "--quiet"], cwd=".").returncode == 0, "Dirty working tree"
    assert subprocess.run([sys.executable, "-m", "pytest", "-x", "-q"]).returncode == 0

    # 2. Build artifact
    build = subprocess.run(["docker", "build", "-t", f"app:{tag}", "."], capture_output=True, text=True)
    if build.returncode != 0:
        return {"status": "failed", "error": build.stderr}

    # 3. Run migrations (dry-run first)
    dry = subprocess.run([sys.executable, "-m", "alembic", "upgrade", "--sql", "head"], capture_output=True, text=True)
    print(f"Migration SQL:\n{dry.stdout}")

    # 4. Deploy
    push = subprocess.run(["docker", "push", f"app:{tag}"])
    if target == "production":
        subprocess.run(["kubectl", "set", "image", f"deployment/app=app:{tag}"])
        subprocess.run(["kubectl", "rollout", "status", "deployment/app"])

    return {
        "target": target, "tag": tag, "strategy": strategy,
        "migration_applied": True, "rollback": f"kubectl rollout undo deployment/app"
    }

if __name__ == "__main__":
    result = deploy(sys.argv[1], sys.argv[2], sys.argv[3] if len(sys.argv) > 3 else "rolling")
    print(json.dumps(result, indent=2))

Read the full file on GitHub · 119 lines

Changes

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.

  1. 8d ago First seen · 119 lines · 17 tokens per session scan A cb17d8aa2f80

Subscribe to this mod's changes

deploy-agent is a skill published in the GitHub repository oyi77/1ai-skills (12 stars, last pushed yesterday), licensed MIT. It adds 17 tokens to every session and 1,057 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 1 finding (runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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