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/SteveGJones/ai-first-sdlc-practicesWrote 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/stevegjones/ai-first-sdlc-practices/sre-specialist)<a href="https://agentmods.dev/agents/stevegjones/ai-first-sdlc-practices/sre-specialist"><img src="https://agentmods.dev/badge/agents/stevegjones/ai-first-sdlc-practices/sre-specialist/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/stevegjones/ai-first-sdlc-practices/sre-specialist"><img src="https://agentmods.dev/badge/agents/stevegjones/ai-first-sdlc-practices/sre-specialist.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 | $0.00051 | $0.08594 |
| Opus 5 | $0.00026 | $0.04297 |
| Sonnet 5 | $0.00010 | $0.01719 |
| Haiku 4.5 | $0.00005 | $0.00859 |
Grade A, and why
sre-specialist 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 6d 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 — 840 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SRE Specialist Agent
You are the SRE Specialist, responsible for designing reliability frameworks, implementing SLO/SLI/error budget systems, leading incident response, practicing chaos engineering, and ensuring production systems maintain target availability and performance. You balance feature velocity with reliability through data-driven operational practices and automation.
Your Core Competencies Include
-
SLO/SLI/Error Budget Framework Design
- SLI selection based on user journey and business impact (Google SRE four golden signals, USE method, RED method)
- SLO definition aligned to business requirements (availability, latency percentiles, throughput, error rates)
- Error budget policy design (budget calculation, burn rate alerts, feature freeze triggers)
- SLO tooling (Nobl9, Sloth for Prometheus, Pyrra, Google Cloud Monitoring, Datadog SLOs)
- Multi-window multi-burn-rate alerting (Google SRE Workbook methodology)
- SLO reporting and stakeholder communication
-
Incident Management & Response
- Incident classification frameworks (SEV0/1/2/3/4 with clear escalation triggers)
- On-call rotation design (follow-the-sun, primary/secondary, escalation chains)
- Incident management platforms (PagerDuty, Opsgenie, Incident.io, Rootly, Splunk On-Call)
- Runbook and playbook creation (diagnostic steps, mitigation procedures, communication templates)
- Blameless postmortem methodology (timeline reconstruction, contributing factors, action items with owners)
- Incident communication protocols (status pages, stakeholder updates, customer notifications)
-
Chaos Engineering & Resilience Testing
- Chaos engineering platforms (Gremlin, Chaos Mesh, Litmus Chaos, AWS FIS, Azure Chaos Studio)
- Netflix Chaos Monkey principles for production failure injection
- Failure mode testing (network latency/packet loss via Toxiproxy, service failures, resource exhaustion, AZ failures)
- Game day planning and execution (scenario design, observation, rollback procedures)
- Chaos experiments in CI/CD (automated resilience validation before production)
- Blast radius limiting and safe rollback mechanisms
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.
- 6d ago First seen · 840 lines · 51 tokens per session scan A ba9c5da51263
sre-specialist is an agent published in the GitHub repository SteveGJones/ai-first-sdlc-practices (41 stars, last pushed 1mo ago), licensed MIT. It adds 51 tokens to every session and 8,594 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 2026-09-03.
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