auto-remediation-reviewer

name	auto-remediation-reviewer
description	Review automated repair actions for safe limits, dry runs, approval modes, rollback, audit logs, blast radius, and loop protection.
version	1.1.0
since	2026-06-17
last_modified	2026-06-17
authors	["platform-engineering"]
stability	stable
min_platform_version	{"codex":"unknown","amazon-q":"unknown","antigravity":"unknown","auggie":"unknown","bob":"unknown","claude-code":"unknown","cline":"unknown","codebuddy":"unknown","continue":"unknown","costrict":"unknown","crush":"unknown","github-copilot":"unknown","gitlab-duo":"unknown","factory":"unknown","forgecode":"unknown","opencode":"unknown","openhands":"unknown","cursor":"unknown","roo-code":"unknown","kiro":"unknown","junie":"unknown","gemini-cli":"unknown","iflow":"unknown","kilocode":"unknown","kimi":"unknown","lingma":"unknown","pi":"unknown","qoder":"unknown","qwen":"unknown","windsurf":"unknown","ollama":"unknown"}
deprecated_since	null
replaces	null
supersedes	[]
changelog	[{"version":"1.1.0","date":"2026-06-17","change":"Initial generated production-ready SDLC / DevSecOps skill"}]

Auto Remediation Reviewer

Purpose

Review automated repair actions for safe limits, dry runs, approval modes, rollback, audit logs, blast radius, and protection against endless loops. Treat regulatory, security, and operational references as review and evidence guidance, not legal advice.

When to use

• auto-remediation safety decisions, controls, or operating practices need independent review.
• A change affects auto-remediation safety artifacts such as remediation workflow, dry-run mode, approval gate, rollback action, audit log, rate limit.
• The user needs evidence-oriented findings for risks such as runaway loop, wide blast radius, unsafe automatic change, missing rollback, approval bypass, audit blind spot.
• Audit, security, operations, or platform stakeholders need a concise readiness position.
• Existing documentation, tickets, tests, or logs must be turned into actionable remediation items.

Operating model

1. Identify the relevant auto-remediation safety artifacts, owners, systems, environments, and review boundary.
2. Compare the available artifacts against expected signals such as execution history, safety threshold, manual override, change ticket, rollback result, loop breaker.
3. Separate confirmed gaps from assumptions, missing evidence, and advisory improvement opportunities.
4. Rate findings by operational, security, compliance, customer, and auditability impact.
5. Recommend minimal remediation steps, validation evidence, owners, and review cadence.

Spec-Driven Change Context

• Treat repository specs, ADRs, runbooks, change proposals, design notes, and task files as durable context that outlives a chat session.
• For non-trivial changes, prefer a checked-in change artifact or equivalent proposal/design/tasks record before implementation begins.
• Capture requirement deltas explicitly: added, modified, removed, deprecated, or unchanged behavior.
• Keep implementation tasks traceable to acceptance criteria, affected specs, validation commands, and owners.
• During verification, compare the implementation against the proposal, design decisions, task checklist, and spec deltas.
• After completion, sync or archive completed change artifacts so the repository's source of truth reflects the final behavior.
• If the repository has no spec workflow yet, report the missing artifact and provide a minimal proposal/spec/tasks outline instead of relying on chat-only intent.

Skill-Specific Review Scope

• Primary artifacts: remediation workflow, dry-run mode, approval gate, rollback action, audit log, rate limit.
• Risk themes: runaway loop, wide blast radius, unsafe automatic change, missing rollback, approval bypass, audit blind spot.