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 rules/votruongdanh/skills-agent/deploygit clone --depth 1 https://github.com/VoTruongDanh/Skills-AgentWrote 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/rules/votruongdanh/skills-agent/deploy)<a href="https://agentmods.dev/rules/votruongdanh/skills-agent/deploy"><img src="https://agentmods.dev/badge/rules/votruongdanh/skills-agent/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 | $0.00056 | $0.00759 |
| Opus 5 | $0.00028 | $0.00380 |
| Sonnet 5 | $0.00011 | $0.00152 |
| Haiku 4.5 | $0.00006 | $0.00076 |
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 3d 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 — 71 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Memory Protocol
START: Read .ai-memory.md from project root. Check deployment history, target environments, known infrastructure, CI/CD setup, past deployment issues.
END: Update .ai-memory.md using Memory Compaction Rules with: target, steps, env details, issues, rollback notes, and infra decisions.
Goal
Prepare a safe, repeatable deployment or release plan.
Agent Routing
- For infrastructure/CI/CD → read
.kiro/skills/agents/agents/devops-engineer.mdand apply its knowledge - For security review before deploy → read
.kiro/skills/agents/agents/security-auditor.mdand apply its knowledge - For database migrations → read
.kiro/skills/agents/agents/database-architect.mdand apply its knowledge - For performance validation → read
.kiro/skills/agents/agents/performance-optimizer.mdand apply its knowledge
Socratic Gate
Before deploying, verify:
- What is the target environment? (staging, production, preview?)
- Are all tests passing?
- Are there database migrations or breaking changes? If any answer is unclear, ASK before proceeding.
Workflow
- Read Memory — Load
.ai-memory.mdfor deployment history and environment details. - Detect the app type, runtime, dependencies, and target environment.
- List prerequisites: secrets, env vars, build steps, infrastructure, database migrations, and health checks.
- Produce a deployment sequence from pre-checks to rollback.
- Call out risky steps explicitly.
- Include post-deploy verification and monitoring.
- Quality Gate — Read
.kiro/skills/_scripts/pre-deploy.mdfor pre-deploy checklist, then.kiro/skills/_scripts/checklist.mdfor cross-cutting checks. - Update Memory — Save deployment details and outcomes to
.ai-memory.md.
Output format
- Target environment
- Preconditions
- Deployment steps (numbered, with risk flags)
- Verification (health checks, smoke tests)
- Rollback plan
Checklist
- Target environment confirmed
- All tests passing
- Secrets/env vars configured
- Database migrations planned
- Build artifacts ready
- Rollback plan documented
- Health checks defined
- Post-deploy verification planned
- Memory file updated
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.
- 3d ago First seen · 71 lines · 56 tokens per session scan A 1ba4447264de
deploy is a cursor rule published in the GitHub repository VoTruongDanh/Skills-Agent (2 stars, last pushed 4mo ago), licensed MIT. It adds 56 tokens to every session and 759 once invoked, about $0.0003 per session on Opus 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 cursor rules, from other repositories
workflow
BDD-style workflow, UI screenshots in the PR, HTTP OpenAPI and config schema sync before lint, final checks.
agents-shipgate
Run Agents Shipgate as the deterministic merge gate for AI-generated agent capability changes.
stackhawk-hawkscan-ci-failure
HawkScan CI failure semantics reference: exit codes 0/1/42, failureThreshold tuning, block-on-42 vs warn-only vs scheduled-baseline modes, retry strategy (don't retry 42), caching strategy (cache CLI/image, never findings), scheduled-vs-PR-trigger tradeoffs.
adr-004
Cursor rule "adr-004" from actual-software/actual-cli, covering adopt secure secrets management in ci/cd pipeline and policies.
linting-and-formatting
Linting, formatting, pre-commit, and CI enforcement.
sciclaw-papercuts
Papercuts from Sol/ImageMagick/release cycles — release, brew, CI, and workspace hygiene for sciClaw agents.