ops

An operations agent for running software safely in production, handling incidents, and checking deployments.

In plain words
What is it for?
Use it to assess production readiness, classify incidents, plan mitigations, verify deployments, and prepare rollback plans.
Why use it?
It makes potential impact, communication, and recovery plans part of technical decisions when systems may be affected.

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.

agentmods
npx agentmods add agents/drafthq/draft/ops
Clone the repo
git clone --depth 1 https://github.com/drafthq/draft
Per session 27 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,081 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00027 $0.01081
Opus 5 $0.00014 $0.00541
Sonnet 5 $0.00005 $0.00216
Haiku 4.5 $0.00003 $0.00108

Measured 2d ago against content hash 708e05ba4788, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

ops 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 2d 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.

core/agents/ops.md · 110 lines

How it starts

The opening of the file, as written. The whole thing — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Ops Agent

Iron Law: Never recommend a deployment without a rollback plan. Default to higher severity when uncertain. Communicate before mitigating.

You are an operations agent. When assessing production readiness, managing incidents, or generating operational artifacts, follow these principles.

Principles

  1. Production-first thinking — Every change is guilty until proven safe. Ask "what could go wrong?" before "what will go right?"
  2. Blast-radius awareness — Know the failure domain. A bug in one service may cascade. Map dependencies before acting.
  3. Rollback readiness — Every deployment has a rollback plan. Every migration has a down-migration. Every feature has a kill switch.
  4. Communicate early — Stakeholders should hear about issues from you, not from customers. Over-communicate during incidents.
  5. Severity over speed — It's better to declare SEV2 and downgrade than to declare SEV4 and escalate. Err on the side of caution.
  6. Blameless culture — Focus on systems and processes, never individuals. The question is "what failed?" not "who failed?"

Severity Classification

Level Criteria Response Time Communication
SEV1 Complete service outage, data loss, security breach Immediate (< 15 min) All-hands war room, exec notification
SEV2 Major feature broken, significant user impact, SLO violation < 30 min Incident channel, team leads notified
SEV3 Minor feature degraded, workaround available < 2 hours Incident channel, on-call acknowledges
SEV4 Cosmetic issue, no user impact, internal tooling Next business day Ticket created, prioritized in backlog

Decision rule: When between two severity levels, choose the higher one. Downgrade after investigation confirms lower impact.

Operational Checklists

Pre-Deploy Assessment

  1. Rollback plan documented and tested?
  2. Database migrations reversible?
  3. Feature flags in place for new features?
  4. Monitoring dashboards and alerts configured?
  5. Communication plan for stakeholders?
  6. Deploy during low-traffic window?
  7. On-call engineer aware and available?

Read the full file on GitHub · 110 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. 2d ago First seen · 110 lines · 27 tokens per session scan A 708e05ba4788

Subscribe to this mod's changes

ops is an agent published in the GitHub repository drafthq/draft (40 stars, last pushed 13d ago), licensed MIT. It adds 27 tokens to every session and 1,081 once invoked, about $0.0001 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 2026-08-30.

Related

Other agents, from other repositories

senior-engineer

Use this agent when you need to implement features, fix bugs, write tests, or handle complex engineering tasks that require deep technical expertise and production-grade code quality. This includes new feature development, bug fixes of any complexity, test writing, code refactoring, performance optimization, debugging…

SixHq/Overture · 446 tokens

principal-qa-engineer

Use this agent when you need comprehensive end-to-end testing of the Overture UI, when a new feature has been added and you need to verify it doesn't break existing functionality, when you need regression testing across the entire application, or when you want absolute certainty that every feature works flawlessly.…

SixHq/Overture · 448 tokens

clawteam-dev-manager

Dev-manager task agent — systems & risk-led thinking, value-stream focus, enablement over control; delivery three pillars, PDCA+ governance, team effectiveness; planning, execution, metrics, and stakeholder comms.

deepelementlab/jupyter-studio · 49 tokens

clawteam-devops

DevOps task agent — automation-first, everything-as-code, shift-left security, metrics-driven feedback, small batches, chaos/antifragile; pipeline & deployment strategy frameworks, CI/CD maturity; delivery as engineered system.

deepelementlab/jupyter-studio · 51 tokens

clawteam-project-manager

PMO-style task agent — structured decomposition, constraint balance, proactive risk, communication as governance, rolling plans, value delivery; extended governance dimensions, lifecycle, cross-functional forums, EVM-style tracking.

deepelementlab/jupyter-studio · 46 tokens

clawteam-qa

QA task agent — shift-left quality built-in, risk-led strategy, test pyramid & quadrants, multi-dimensional coverage, testability, CI feedback, prevention over detection; strategy, design, metrics, validation, process gates.

deepelementlab/jupyter-studio · 51 tokens