recommendations-next-steps
Skill187 starsChanged 6 months agoMITView on GitHub
Use to produce the forward-looking recommendations and next steps for a Zava Learning incident — prioritized preventive, detective, and process actions with owners, target dates, and the risk of inaction. Its output is used in the zava-reporting deliverables.
In this repository28 other files it gives its agents.
- labs/onboardinglab/AGENTS.mdAGENTS.md
- labs/zava-aks-postgres/AGENTS.mdAGENTS.md
- labs/zava-learning/AGENTS.mdAGENTS.md
- deployment-compliance-checkSkill
- onboarding-labSkill
- azure-monitor-rcaSkill
- email-incident-followupSkill
- github-issue-followupSkill
- proactive-health-checkSkill
- ticketing-pr-validationSkill
- compliance-drift-detectionSkill
- vm-performance-diagnosticsSkill
- deploying-demoSkill
- managing-sre-agentSkill
- running-demoSkill
- connectivity-triageSkill
- cost-analysisSkill
- evidence-before-afterSkill
- nsg-auditSkill
- pagerduty-incident-updateSkill
- performance-investigationSkill
- pr-deliverySkill
- rbac-auditSkill
- rca-analysisSkill
- redaction-guardSkill
- servicenow-change-managementSkill
- zava-audit-reportSkill
- zava-reportingSkill
The file
We split this skill into the rules it gives an agent
1 rule. About 0 are common, 1 is distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈1
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 33
Every recommendation is specific and testable — no "improve monitoring".
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Zava Learning — Recommendations & Next Steps
Turn the RCA into a concrete, accountable action plan. Retrieve zava-brand and
zava-report-template with SearchMemory; follow the Recommendations section. Use
SearchIncidentKnowledge for related past incidents and microsoft-learn_microsoft_docs_search
to ground recommendations in Azure best practice.
Derive actions from the RCA
Map each contributing factor and detection gap to an action. Classify every action:
- Preventive — stops recurrence (e.g. an NSG priority guardrail in IaC, a CI check for synchronous work on the request path, an autoscale floor so an API can't reach zero replicas).
- Detective — catches it sooner (a targeted alert/metric, a synthetic probe on
/api/quiz/*, a dashboard). - Process — runbook, ownership, review, or deployment-policy change.
Present as an accountable table
Columns: action · type · owner · priority (P1/P2/P3) · target date · risk if not done · status.
- Priority by likelihood × impact of recurrence.
- Owners are roles/teams, not named individuals.
- Every recommendation is specific and testable — no "improve monitoring".
- Separate immediate next steps (this week) from longer-term hardening.
Rules
- Recommend only what the evidence supports; don't pad the list.
- Note where a recommendation is already partially done (e.g. mitigation applied, guardrail PR open) and what remains.
Verification
A prioritized, owned, dated action table where each row traces to an RCA finding, immediate vs.
long-term separated, ready for zava-reporting.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

