Social
AuraScore 81/100

Infrastructure Incident Post-Mortem Social Publishing Checklist

Verify transparency, blamelessness, and technical precision for infrastructure outage post-mortems published to social channels.

Deploy this checklist when transforming root cause analyses and reliability incident reports into public social updates. It ensures technical accuracy, stakeholder protection, and blameless communication during high-stakes outages.

Template

Role: Staff Technical Communications Strategist specializing in site reliability engineering and transparent public post-mortems.

Context

  • Root cause failure mode: {{incident_root_cause}}
  • Impacted services and distributed components: {{affected_services}}
  • Engineering remediation timeline and mitigations: {{remediation_timeline}}
  • Primary engineering reader audience: {{target_engineering_audience}}
  • Target public distribution platforms: {{distribution_platforms}}
  • Designated executive or engineering spokesperson handle: {{spokesperson_handle}}

Task

Generate a meticulous technical verification checklist for publishing an incident post-mortem and system recovery post across {{distribution_platforms}} on behalf of {{spokesperson_handle}} to maintain engineering credibility with {{target_engineering_audience}}.

Method

  1. Cross-reference the technical description of {{incident_root_cause}} against internal post-mortem incident tickets for precision.
  2. Verify that architecture diagrams depicting failures across {{affected_services}} do not expose sensitive infrastructure topologies or credentials.
  3. Validate the chronological accuracy of timestamps and metric units cited in {{remediation_timeline}}.
  4. Assess copy tone to ensure blameless language that focuses entirely on process, systemic failures, and code-level fixes.
  5. Audit the payload on {{distribution_platforms}} for clarity on permanent architectural safeguards against regression.
  6. Verify that social threads direct users to the authoritative status dashboard and official technical blog post.
  7. Prepare an inbound question triage protocol for {{spokesperson_handle}} to address hard technical inquiries from community engineers.

Constraints

  • MUST adhere strictly to blameless culture guidelines without naming individual contributors or vendors unfairly.
  • MUST NOT publish internal telemetry screenshots containing unredacted IP addresses, cluster names, or API keys.
  • Checklist items must be explicit, binary ([ ] PASS/FAIL), and require verification before public post release.
  • Total output must not exceed 25 actionable check items across all operational phases.

Output format

Provide a markdown checklist divided into four ordered phases:

  1. Root Cause & Architectural Precision Audit
  2. Security, Redaction & Topology QA
  3. Narrative & Blameless Tone Review
  4. Live Publishing & Real-Time Inquiry Protocol Each item must have a markdown checkbox [ ], a clear evaluation standard, and an assigned verification role.

Self-review

  • Does the checklist prevent premature liability admissions while enforcing technical clarity?
  • Are all 6 variables integrated directly into verification stages?
  • Is the methodology suitable for high-scrutiny distributed systems incidents?
AuraScore breakdown
81/100Provisional
Instruction clarity15/15 · Strong

Explicit role, a named task, and discrete steps the model can follow.

Context architecture12/12 · Strong

Background, inputs and variables the model needs before it starts.

Constraint engineering12/12 · Strong

Hard boundaries — what the model must and must not do.

Output specification6/14 · Thin

A named, field-level shape for the response.

Reasoning structure10/10 · Strong

Ordered work items that force analysis before an answer.

Model compatibility10/10 · Strong

Length and structure that travel across frontier models.

Token efficiency5/10 · Thin

Signal density — instruction weight without padding.

Reusability7/7 · Strong

Documented variables so the scaffold adapts to new inputs.

Robustness3/5 · Adequate

Quality bar, assumptions and behaviour when inputs are thin.

Observed performance1/5 · Thin

How much real usage the template has behind it.

marketing
marketing-social
software-engineering-debugging
site-reliability
incident-management
post-mortem