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 oyi77/1ai-skills --skill deploy-agentgit clone --depth 1 https://github.com/oyi77/1ai-skillsWrote 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/oyi77/1ai-skills/deploy-agent)<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>- NVIDIA SkillSpector pass
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 | $0.00017 | $0.01057 |
| Opus 5 | $0.00009 | $0.00528 |
| Sonnet 5 | $0.00003 | $0.00211 |
| Haiku 4.5 | $0.00002 | $0.00106 |
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" 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))
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.
- 8d ago First seen · 119 lines · 17 tokens per session scan A cb17d8aa2f80
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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no-mistakes
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git-workflow
Guides you through Git workflows — branching strategies, commit conventions, merge conflict resolution, and release management. Use when working with Git repositories or when the user asks about version control best practices.
release-sync
Syncs latest release content to NotebookLM and HQ Knowledge Base after version tagging. Reads CHANGELOG, CLAUDE.md, and hook README, updates notebook sources, and ingests release digest. Optionally generates podcast from updated knowledge base. Use after tagging a new version to propagate release knowledge.
release-new-version
Use when the user wants to release a new Zafiro version — drafting bilingual release notes, deciding the next version number, bumping app/build.gradle.kts, tagging, and publishing to GitHub (main repo, optionally the Xposed repo).