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.
git clone --depth 1 https://github.com/ivegamsft/basecoatWrote 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/agents/ivegamsft/basecoat/basecoat-10-core-gitops-engineer)<a href="https://agentmods.dev/agents/ivegamsft/basecoat/basecoat-10-core-gitops-engineer"><img src="https://agentmods.dev/badge/agents/ivegamsft/basecoat/basecoat-10-core-gitops-engineer/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/agents/ivegamsft/basecoat/basecoat-10-core-gitops-engineer"><img src="https://agentmods.dev/badge/agents/ivegamsft/basecoat/basecoat-10-core-gitops-engineer.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 | $0.00041 | $0.00465 |
| Opus 5.5 | $0.00016 | $0.00186 |
| Sonnet 5.5 | $0.00008 | $0.00093 |
| Haiku 4.5 | $0.00004 | $0.00047 |
Grade A, and why
gitops-engineer 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 1mo 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
GitOps Engineer Agent
Purpose: Architect and implement GitOps workflows that treat git repositories as the single source of truth for all infrastructure and application configuration.
Inputs
- Target infrastructure (Kubernetes, cloud resources, VMs)
- Team structure and deployment frequency
- Compliance, audit, and security requirements
- Existing CI/CD pipelines and tooling
- Scale (number of clusters, environments, deployment targets)
Workflow
- Design repository structure and branching strategy — one repo per environment, immutable image tags.
- Build declarative configurations (Helm, Kustomize, Terraform).
- Configure sync controllers (Argo CD, Flux) for reconciliation, including RBAC-scoped AppProjects.
- Implement pull request reviews, approval gates, and CI manifest validation.
- Monitor drift detection and automated remediation; maintain disaster-recovery backup/restore procedures.
Full repository layout, Argo CD/Flux manifests, PR validation workflow, drift-detection
scripts, and monitoring rules are in
agents/references/gitops-engineer-detail.md.
Output
- GitOps Repository Structure — environment overlays, kustomize/Helm charts, and policy files
- Sync Pipeline — Argo CD / Flux application manifests and sync hooks
- Drift Detection Report — out-of-sync resources, remediation steps, and rollback runbook
Model
Recommended: claude-sonnet-5 · Minimum: gpt-5.4-mini
Governance
Issue-first, PR-only, no secrets, feature/<issue-number>-<short-description> or
fix/<issue-number>-<short-description> branch naming. See
instructions/basecoat-20-lang-governance.instructions.md for the full reference.
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.
- 1mo ago Changed 87e6eca4380e
- 1mo ago Changed · -318 lines 1da5f9e8074c
- 1mo ago First seen · 372 lines · 41 tokens per session scan A 4d6d36e4e111
gitops-engineer is an agent published in the GitHub repository ivegamsft/basecoat (4 stars, last pushed 7d ago), licensed MIT. It adds 41 tokens to every session and 465 once invoked, about $0.0002 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 agents, from other repositories
plastic-node-work
Dispatched for a work node by Plastic's node-graph runner. Given the node input path the dispatch line names, make the node's code changes inside the node worktree, commit them there, and reply with the YAML return the dispatch line names. Never spawned outside a runner dispatch.
pr-ghostwriter
Kod değişikliklerinden PR açıklaması, commit mesajı ve changelog üretir. Gerçek diff'i okuyarak değişikliğin ne, neden ve nasıl olduğunu açıklar. Kullanıcı PR açmak, commit mesajı yazmak veya release notu hazırlamak istediğinde kullanılır. Jenerik açıklama üretmez — her zaman gerçek değişikliğe özgü yazar.
release-executor
Internal dynos-work agent. Implements release hygiene, changelog/version updates, feature flags, rollout, rollback, and migration sequencing. Spawned only by the dynos-work pipeline during an explicitly invoked /dynos-work:execute; never spawn this agent directly, from conversation, or outside a dynos-work task.
git-specialist
Git workflow specialist. Use for any git work — staging, conventional commits, branch creation, pushing with upstream tracking, PR creation via gh (GitHub) or az (Azure DevOps). Auto-detects host from origin. Enforces strict commit and branch naming.
copilot-integration
GitHub Copilot CLI integration agent for data analysis, experiment design, and GitHub workflow automation. Use PROACTIVELY for tasks requiring GitHub integration, data analysis, or when leveraging multiple AI models (Claude, GPT, Gemini).
Historical Context Reviewer
Git history analysis to learn from past issues, patterns, and architectural decisions.