anyshift-io/sre-skills

sre-skills

SRE methodology skills for AI agents. Each skill packages one reliability workflow (investigating a live incident, handing over oncall, writing a postmortem) as a self-contained module your agent loads and runs.

Built and maintained by Anyshift.

Why use these

Your agent already writes code and runs commands. It does not know how a seasoned SRE actually works an incident: which signals to correlate first, when a deploy is the prime suspect, when to stop digging and page a human. These skills encode that methodology so the agent follows a real playbook instead of improvising.

Every skill runs end-to-end with no Anyshift account and no external credentials. The methodology, the worked examples, and the replay tests all work offline against fixtures.

Skills

Each skill targets one real product and one job over it: audit an IAM policy, triage a Terraform plan, resolve an S3 bucket's effective access. It does that job end-to-end, offline, against fixtures. Not a wrapper that dumps the API response back: each one carries the judgment a senior engineer applies to that one source, the thresholds and known-bad combinations that separate signal from a clean-looking config.

Then it stops. A single source only knows itself. The moment a question needs a join (this role to everything it can actually reach, this queue to its producers and consumers, this plan to the running infrastructure it will move) the data runs out. Each skill names exactly where that happens and what's missing, so the boundary is explicit instead of a silent wrong answer. That boundary is the same one every time: the join across resources, across sources, or across time.

| Skill | Domain | What it does | |---|---|---| | sqs-queue-auditor | AWS | Audits redrive/DLQ wiring, maxReceiveCount, retention ordering against the DLQ, and a visibil