Best for
- An incident or outage is resolved and the team needs a structured
- A near-miss happened and a post-mortem is being drafted (near-miss
- The incident-commander hands off a post-mortem skeleton (empty
event4u-app/agent-config/src/skills/blameless-post-mortem/SKILL.md
Use after an incident or outage is resolved — blame-free facilitation, root cause, corrective actions, memory write-back — even for a near-miss. Consumes the incident-commander skeleton.
Decision brief
Facilitate a blame-free post-mortem after an incident or near-miss. Consumes the incident-commander skeleton, derives or fills the root cause, writes the corrective-action plan, and drafts an incident-learnings memory candidate. Systems and processes are examined — never individ…
Compatibility matrix
| Platform | Status | Evidence | What to check |
|---|---|---|---|
| Codex | Not declared | No explicit evidence | Portability before use |
| Claude Code | Not declared | No explicit evidence | Portability before use |
| Cursor | Not declared | No explicit evidence | Portability before use |
| Gemini CLI | Not declared | No explicit evidence | Portability before use |
Installation
The source command is displayed only when detected. A safe inspection prompt is always available so your agent can explain every action before execution.
npx skills add https://github.com/event4u-app/agent-config --skill "src/skills/blameless-post-mortem"Inspect the Agent Skill "blameless-post-mortem" from https://github.com/event4u-app/agent-config/blob/6a5670b7881a676c0da90d2afb950298087c4ccb/src/skills/blameless-post-mortem/SKILL.md at commit 6a5670b7881a676c0da90d2afb950298087c4ccb. List every install step, command, network request, credential, file read/write, external action, and rollback step. Explain whether it fits my task. Do not install or execute anything until I approve.
Workflow
Read the incoming skeleton. Per docs/contracts/analysis-memory-loop.md § 5:
For each identified gap, propose a corrective action. Reuse risk-officer mitigation framing: owner role, size, residual-risk note. Four action types:
An incident or outage is resolved and the team needs a structured
Read the incoming skeleton. Per docs/contracts/analysis-memory-loop.md § 5:
If root cause is unknown or TBD, invoke root-cause-frameworks. That skill returns ranked candidates with confidence levels — do NOT force a verdict. If root cause remains unresolved after the analysis pass, mark the post-mortem status: draft and continue. A draft post-mortem can…
Permission review
No configured static risk pattern was detected
This is not proof of safety. Runtime behavior, indirect dependencies, and hidden external systems are outside the static scan.
Evidence record
| Signal | Value | Evidence type | Meaning |
|---|---|---|---|
| Quality score | 92/100 | Computed | Documentation, specificity, maintenance, and trust rules |
| Repository stars | 9 | Source | Repository attention, not individual Skill quality |
| Compatibility | 0 platforms | Source | Declared in the catalog source record |
| Usage guide | automated source guide | Editorial | Generated or reviewed according to the visible evidence level |
Pinned source
Facilitate a blame-free post-mortem after an incident or near-miss. Consumes the
incident-commanderskeleton, derives or fills the root cause, writes the corrective-action plan, and drafts anincident-learningsmemory candidate. Systems and processes are examined — never individuals.
incident-commander hands off a post-mortem skeleton (empty
root cause is accepted — do not block on it).Do NOT use when:
incident-commander first.root-cause-frameworks.risk-officer.Read the incoming skeleton. Per
docs/contracts/analysis-memory-loop.md § 5:
SEV, State, Started, Timeline, Mitigation,
Root cause (may be unknown or TBD).If no skeleton is provided, reconstruct from the user's description: severity, timeline, impact, mitigation state.
If root cause is unknown or TBD, invoke
root-cause-frameworks. That
skill returns ranked candidates with confidence levels — do NOT force
a verdict. If root cause remains unresolved after the analysis pass,
mark the post-mortem status: draft and continue. A draft post-mortem
can still produce memory candidates (note the open question in the
candidate summary).
Sections in order — no individuals named, systems and processes only:
Near-miss mode: same sections; set SEV: near-miss. Add two
extra questions after "what went wrong":
For each identified gap, propose a corrective action. Reuse
risk-officer mitigation framing: owner role, size, residual-risk
note. Four action types:
| Type | Purpose |
|---|---|
| Immediate | Stop recurrence now (config fix, kill switch, revert) |
| Preventive | Remove the root cause (architectural or process change) |
| Detection | Catch it sooner next time (alert, dashboard, runbook) |
| Process | Address human/coordination gaps (oncall rotation, docs) |
Each action MUST have:
An action without a closure criterion is a wish, not a plan.
Per docs/contracts/analysis-memory-loop.md § 2:
Run the dedup pre-check — find_duplicate(...) from
scripts/memory_lookup.ts over the same key-space (incident type,
affected paths, decision area); a memory:lookup query over the
same keys is the CLI equivalent.
Match found — propose a frequency / supersedes update to
the existing entry; do NOT create a new candidate.
No match — draft a REDACTED incident-learnings candidate to
/memory propose:
type: incident-learningssummary: one-line pattern (no customer names, no secrets, no
project-rooted paths)evidence_paths: ≥ 2 file paths (for admission gate)decision_surface: ≥ 3 decisions this pattern changes (if
possible)last_validated: todayreview_after_days: 90applicable_scope: projectNEVER auto-promote. The human drives promotion via /memory promote. If the candidate fails the admission gate
(check_memory_proposal.ts), surface the gap to the user.
If the lookup surfaces stale entries (past review_after_days),
surface them explicitly — never silently use stale data.
The post-mortem produces, in order:
final or draft if root cause is unresolved.incident-learnings JSONL
snippet for /memory propose (or the frequency-update proposal if
a duplicate was found).draft and
continue.status: draft, not a rejection. Draft
post-mortems still produce memory candidates with the open question
noted.near-miss; the extra two questions apply.incident-commander — produces the
skeleton this skill consumes; run first during live incidents.root-cause-frameworks — RCA
engine invoked in step 2 when root cause is unresolved.risk-officer — mitigation framing
reused for corrective actions (owner, size, residual-risk).docs/contracts/analysis-memory-loop.md
— produce → propose → promote → retrieve contract this skill binds to.Frequently asked questions
Facilitate a blame-free post-mortem after an incident or near-miss. Consumes the incident-commander skeleton, derives or fills the root cause, writes the corrective-action plan, and drafts an incident-learnings memory candidate. Systems and processes are examined — never individ…
The source record exposes this install command: npx skills add https://github.com/event4u-app/agent-config --skill "src/skills/blameless-post-mortem". Inspect the command and pinned source before running it.
Alternatives
alirezarezvani/claude-skills
App Store Optimization (ASO) toolkit for researching keywords, analyzing competitor rankings, generating metadata suggestions, and improving app visibility on Apple App Store and Google Play Store. Use when the user asks about ASO, app store rankings, app metadata, app titles and descriptions, app store listings, app visibility, or mobile app marketing on iOS or Android. Supports keyword research and scoring, competitor keyword analysis, metadata optimization, A/B test planning, launch checklist
brucesongs/kali-claw
Insecure Design (OWASP A06:2025) focuses on security flaws in system architecture and design phases, rather than code implementation-level bugs.
NintendaDev/unikit-ai
Generate and maintain the project's TECHNICAL documentation from its codebase — scans the project structure, tech stack, and module boundaries, then writes a lean README landing page plus detailed topic pages (architecture, modules, setup, build, APIs), only the docs that are relevant. Use whenever the user wants to create, update, or validate documentation of the CODE or the project itself, e.g. "generate documentation", "create docs", "write the README", "update the project docs", "document th
K-Dense-AI/scientific-agent-skills
Distributed computing for larger-than-RAM pandas/NumPy workflows. Use when you need to scale existing pandas/NumPy code beyond memory or across clusters. Best for parallel file processing, distributed ML, integration with existing pandas code. For out-of-core analytics on single machine use vaex; for in-memory speed use polars.