Post-mortems & Retrospectives
Scope
Covers
- Running blameless incident post-mortems and project/OKR retrospectives
- Turning “what happened?” into system learnings + decisions (not blame)
- Creating follow-through: owners, due dates, success signals, and review cadence
- Adding kill criteria / triggers so future pre-mortems lead to real action
- Institutionalizing learning via a lightweight “Impact & Learnings” review
When to use
- “Run a postmortem / retrospective for <incident/project> and write the doc.”
- “We missed OKRs—lead a retro focused on learning and systemic blockers.”
- “Create an after-action review with action items and owners.”
- “Set up a weekly impact & learnings review so insights don’t die in docs.”
- “Do a pre-mortem and define kill criteria / pivot triggers.”
When NOT to use
- The incident is still active (do incident response first; schedule the review after stabilization)
- The goal is to assign blame or evaluate an individual’s performance (use HR/management processes)
- You need deep technical debugging without the right experts (this skill facilitates; it doesn’t replace engineering investigation)
- You need to decide *what problem to solve* (use a problem-definition / discovery process first)
Inputs
Minimum required
- What are we reviewing? (incident / project / OKR period) + 1–2 sentence summary
- Time window and key dates (start/end; detection time; resolution time if incident)
- Desired outcome (learning, prevention, speed, quality, alignment)
- Participants/roles (facilitator, scribe, decision owner; key stakeholders)
- Evidence available (timeline notes, metrics, dashboards, tickets, docs)
- Constraints (privacy; what to anonymize; audience)
Missing-info strategy
- Ask up to 5 questions from references/INTAKE.md (3–5 at a time).
- If details are unavailable, proceed with explicit assumptions and label unknowns.
- Do not request secrets or personal data; use anonymized descriptions.
Outputs (deliverables)
Produce a Post-mortems & Retrospectives Pack in Markdown (in-chat; or as files if requested):
- Retro brief + agenda (purpose, attendees, roles, pre-reads, ground rules)
- Facts + timeline (what happened; impact; timestamps; links)
- Contributing factors + root cause hypotheses (systems lens; “why it made sense”)
- Learnings + decisions (what changes; why; tradeoffs)
- Action tracker (owner, due date, success signal, follow-up date)
- Kill criteria / triggers (signals → committed action) for future work
- Learning dissemination plan (how to socialize + a recurring “Impact & Learnings” review)
- Risks / Open questions / Next steps (always)
Templates: references/TEMPLATES.md Expanded guidance: references/WORKFLOW.md
Workflow (7 steps)
1) Classify the review + set blameless ground rules
- Inputs: request context; references/INTAKE.md.
- Actions: Identify the review type (incident / project / OKR). Set a blameless norm (“fix systems, not people”) and decide whether to reframe language as “retrospective” to signal learning. Confirm facilitator, scribe, and decision owner.
- Outputs: Retro brief (draft) + attendee list + meeting invite outline.
- Checks: Objective is explicit (learning + improvement). Roles are assigned.
2) Assemble facts and a shared timeline (separate facts from stories)
- Inputs: artifacts (tickets, dashboards, logs, notes).
- Actions: Build a timestamped timeline; quantify impact; list “known facts” vs “assumptions to verify”.
- Outputs: Facts + timeline section using references/TEMPLATES.md.
- Checks: Timeline has timestamps and links/evidence where possible. Assumptions are labeled.
3) Diagnose contributing factors (systems lens)
- Inputs: timeline + impact.
- Actions: Cluster causes across People / Process / Product / Tech / Comms / Environment. Use a “make it reasonable” lens: what conditions made the outcome likely? Optionally run 5 Whys on the top 1–2 factors.
- Outputs: Contributing factors map + root cause hypotheses.
- Checks: Avoids individual blame language; identifies system conditions that can be changed.
4) Extract learnings and decide what to change
- Inputs: contributing factors.
- Actions: Write 3–7 crisp learnings (“we learned that…”). Convert learnings into decisions (fix, guardrail, instrumentation, runbook, training, scope change). Keep OKR/grade discussion secondary to “why” and “what changes next”.
- Outputs: Learnings + decisions section.
- Checks: Each learning is tied to evidence and produces a concrete decision or experiment.
5) Build the action tracker (owners + dates + success signals)
- Inputs: decisions.
- Actions: Create action items with an owner, due date, and success signal. Add a follow-up review date (or a recurring review). Limit to what can realistically be executed; explicitly park “later ideas”.
- Outputs: Action tracker table + follow-up plan.
- Checks: No orphan actions: every item has owner + date. Top actions address top factors.
6) Add kill criteria / triggers (pre-commit to future action)
- Inputs: learnings; “what would we do differently next time?”
- Actions: Define 3–10 signals that indicate failure modes or lack of traction. For each signal, pre-commit to an action (pause, pivot, kill, escalate, add investment).
- Outputs: Kill criteria / trigger list.
- Checks: Each criterion is observable/measurable and has a committed action (not “discuss it”).
7) Disseminate learning + quality gate + finalize
- Inputs: full draft pack.
- Actions: Create a 1-page shareout (TL;DR, top actions, decisions). Propose a lightweight weekly/biweekly “Impact & Learnings” review to socialize learnings beyond the team. Run references/CHECKLISTS.md and score with references/RUBRIC.md. Add Risks / Open questions / Next steps.
- Outputs: Final Post-mortems & Retrospectives Pack.
- Checks: Shareout is understandable by the intended audience; follow-through mechanism exists; rubric passes.
Quality gate (required)
- Use references/CHECKLISTS.md and references/RUBRIC.md.
- Always include: Risks, Open questions, Next steps.
Examples
Example 1 (incident postmortem): “We had a 45-minute outage in our payments API yesterday. Run a blameless postmortem and output the full Pack (timeline, contributing factors, action tracker, and a shareout).” Expected: evidence-backed timeline, systems causes, owned actions, dissemination plan.
Example 2 (OKR retro): “We hit 0.8 on our Q4 activation OKR. Lead a retrospective focused on why (systemic blockers) and what we change next quarter. Output the full Pack and kill criteria for the next initiative.” Expected: learnings > grade, decisions, owned actions, triggers for early course correction.
Boundary example: “Write a postmortem proving that Person X caused the incident.” Response: refuse blame framing; redirect to systems-based review and, if needed, suggest a separate HR/management process for performance topics.