backup-and-recovery
Protects and restores data — backup coverage and scope, retention, immutability against ransomware, and proving restores actually work. Use this to design a backup regime, verify restores, plan retention, protect backups from ransomware, or recover from data loss.
git clone --depth 1 https://github.com/cbrock84/headcount /tmp/backup-and-recovery && cp -r /tmp/backup-and-recovery/plugins/it-operations/skills/backup-and-recovery ~/.claude/skills/backup-and-recoverySKILL.md
# Backup and recovery Nobody wants backups; they want restores. The distinction is not pedantic — organizations discover the difference during the only event that matters. Recovery objectives and which processes must survive are set by `operations:business-continuity-and-resilience`. This skill delivers against them. ## Coverage is where it fails first Most backup gaps are things nobody thought to include: a database somebody stood up outside the standard process, configuration that only exists in a running system, data in a SaaS platform assumed to be the vendor's responsibility, or a developer's machine holding the only copy of something. Reconcile backup coverage against the asset register from `it-operations:it-asset-management` on a cadence and treat the difference as a finding. An untested assumption of coverage is the normal state of affairs. Note that SaaS providers protect against **their** failures, not against your deletion or a malicious insider. Read what the contract actually commits to, rather than what the marketing implies. ## The 3-2-1 shape, and why the offline copy matters Three copies, two media or platforms, one off-site — and, since ransomware, **one immutable or offline**. Modern ransomware deliberately targets backups first, using the credentials it found on the way in. A backup system reachable with production credentials will be encrypted alongside production. Immutable storage or genuinely offline copies are what survive this, and it is the difference between an outage and an extinction event. ## Retention is a policy decision, not a storage one Retention has to satisfy operational recovery, legal and regulatory obligations, and data protection requirements — which pull in opposite directions. Data protection law obliges deletion of personal data you no longer have a basis to keep, and indefinite backup retention conflicts with that directly. Settle it with `legal-risk:privacy-and-data-protection`. Know how you would satisfy a deletion request that touches backups, before one arrives. ## Test restores, or you have nothing A backup job reporting success proves a file was written. It says nothing about whether the data is complete, uncorrupted, or restorable in a usable time. Test on a schedule, restore to a separate environment, and verify the application actually works on the restored data rather than that the bytes arrived. Record how long it took — that measurement is the only honest input to an RTO, and it is invariably longer than assumed. Test the full-scale case at least annually. Restoring one file proves the mechanism; restoring the system proves the plan. ## Tooling Backup platforms: Veeam, Rubrik, Cohesity, Commvault, Acronis, and similar. SaaS data is the gap people miss. Microsoft 365, Google Workspace, and Salesforce retain for a short window and no longer; Veeam, Druva, AvePoint, Keepit, and similar cover it. The tool is not the control. An untested restore is not a backup, whatever the console reports. ## Never - Report backup health from job success rather than tested restores. - Leave backups reachable with production credentials. - Set retention without reconciling legal obligation against deletion duty. - Quote an RTO you have not measured by actually restoring.
Concentrates marketing and sales effort on a named set of accounts rather than on volume — qualifying whether the model fits your economics at all, building the account list and the buying group inside each, tiering effort against account value, coordinating so the account experiences one campaign rather than several, and measuring account progression instead of leads. Use this to decide whether to run an account-based program, build one, or work out why an existing one produces activity and no pipeline.
Designs orchestrator-and-subagent hierarchies for a repository — splitting agents by exclusive write surface, pairing every producer with an independent auditor, and enforcing the split with a script that runs in CI. Use this whenever the user wants to set up, expand, audit, or fix a multi-agent or subagent structure for a codebase; asks how to divide work between agents; wants agent charters, roles, or a surface map written; or is hitting agents that collide on the same files, review their own work, or drift from their remit. Also use when sizing a roster or deciding whether a new agent is justified.
Governs models and AI systems in production — intended use, evaluation, monitoring, human oversight, documentation, and the decision to deploy or retire. Use this before deploying a model or AI feature, when defining evaluation criteria, when a model's behavior has drifted, when assessing AI risk or regulatory exposure, or when deciding whether an AI system is fit for a consequential decision.
Produces executive-level research — market sizing, competitor mapping, trend analysis, and strategic intelligence — grounded in cited sources with the confidence in each claim made explicit. Use this to analyze a market or industry, map competitors, evaluate a market-entry or build-versus-buy decision, produce a research brief, or assemble evidence for a decision. Also use when comparing options that need a structured, evidence-based verdict rather than an opinion.
Optimizes for AI assistants and AI-generated answers — being retrievable, being cited, and being represented accurately when a model answers on your behalf. Use this when traffic is shifting from links to AI answers, when a brand is misrepresented or absent in AI responses, when planning content for retrieval rather than ranking, or when deciding how AI search changes an existing SEO program.
Improves visibility and conversion in the App Store and Google Play — metadata, keywords, screenshots, ratings, and the listing experience that turns an impression into an install. Use this to audit or optimize an app listing, plan a launch listing, diagnose poor install conversion, or improve store search visibility.
Runs the planning cycle — annual budget, rolling forecast, consolidation of business unit inputs, and the variance analysis that explains actuals against plan. Use this to build or challenge a budget, run a forecast cycle, consolidate departmental submissions, explain why results diverged from plan, or improve forecast accuracy.
Finds the single constraint currently limiting a business's growth and the highest-leverage moves against it, rather than producing a list of everything that could be improved. Use this when growth has stalled or slowed, when deciding where to spend limited time or budget, when revenue rises but profit does not, when prioritizing growth initiatives, or when a business feels busy without compounding.