Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add sethdford/claude-skills --skill disaster-recovery-plangit clone --depth 1 https://github.com/sethdford/claude-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/sethdford/claude-skills/disaster-recovery-plan)<a href="https://agentmods.dev/skills/sethdford/claude-skills/disaster-recovery-plan"><img src="https://agentmods.dev/badge/skills/sethdford/claude-skills/disaster-recovery-plan/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/sethdford/claude-skills/disaster-recovery-plan"><img src="https://agentmods.dev/badge/skills/sethdford/claude-skills/disaster-recovery-plan.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00036 | $0.00654 |
| Opus 5 | $0.00018 | $0.00327 |
| Sonnet 5 | $0.00007 | $0.00131 |
| Haiku 4.5 | $0.00004 | $0.00065 |
Grade A, and why
disaster-recovery-plan scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 48 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Disaster Recovery Plan
Design recovery strategies with defined objectives, tested procedures, and regular validation.
Context
You are planning disaster recovery. Define RTO/RPO requirements, design backup and failover strategies, plan testing. Read business impact analysis, current backups, and regulatory requirements.
Domain Context
Based on IT disaster recovery best practices (NIST, ISO 27031):
- RTO (Recovery Time Objective): How long can system be down? 1 hour? 1 day? Determines failover strategy.
- RPO (Recovery Point Objective): How much data loss acceptable? 1 hour? 1 day? Determines backup frequency.
- Backup Strategies: Full (complete copy), incremental (only changes since last backup), continuous replication
- Failover: Automatic (heartbeat-driven) vs manual (operations-triggered). Planned vs unplanned.
- Testing: Regular drills validate procedures; practice before disaster strikes
Instructions
-
Define Business Requirements: For each critical system, what's RTO (max downtime) and RPO (max data loss)? Business impact: lost revenue, SLA violations, customer trust?
-
Design Backup Strategy: Full daily backup + hourly incremental. Or continuous replication for stricter RPO. Test recovery from backups monthly; document recovery steps.
-
Plan Failover: For RTO < 1 hour, set up active-passive (standby system). For RTO < 5 minutes, active-active (both systems live). Implement health checks and automatic failover.
-
Document Procedures: Who decides to failover? What are manual steps? How do you know failover succeeded? Test documentation with dry runs; update after each test.
-
Schedule Regular Testing: Monthly failover drills for critical systems. Test both planned (maintenance window) and unplanned (kill production server) scenarios. Document findings and improvements.
Anti-Patterns
- RTO/RPO Undefined: Assume everything needs sub-minute RTO. Result: over-engineering cost. Guard: Quantify business impact; set RTO/RPO accordingly per system.
- Backups Never Tested: Assume they work. Result: discover failures during actual disaster. Guard: Regular restore drills; track recovery metrics.
- Manual Failover for Low RTO: Plan manual process for 5-minute RTO. Result: missed SLA. Guard: Automate failover if RTO tight; test automation regularly.
- Ignoring Data Consistency in Failover: Assume data identical between systems. Result: data loss or corruption. Guard: Validate data integrity post-failover; have reconciliation procedure.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 48 lines · 36 tokens per session scan A 7830fcfbb710
disaster-recovery-plan is a skill published in the GitHub repository sethdford/claude-skills (41 stars, last pushed 6mo ago), licensed MIT. It adds 36 tokens to every session and 654 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
videodb
See, Understand, Act on video and audio. See- ingest from local files, URLs, RTSP/live feeds, or live record desktop; return realtime context and playable stream links. Understand- extract frames, build visual/semantic/temporal indexes, and search moments with timestamps and auto-clips. Act- transcode and normalize…
lead-intelligence
AI-native lead intelligence and outreach pipeline. Replaces Apollo, Clay, and ZoomInfo with agent-powered signal scoring, mutual ranking, warm path discovery, and personalized outreach. Use when the user wants to find, qualify, and reach high-value contacts.
springboot-verification
Verification loop for Spring Boot projects: build, static analysis, tests with coverage, security scans, and diff review before release or PR.
team-builder
Interactive agent picker for composing and dispatching parallel teams.
cmd_aside
Answer a quick side question without interrupting or losing context from the current task. Resume work automatically after answering.
seo
Audit, plan, and implement SEO improvements across technical SEO, on-page optimization, structured data, Core Web Vitals, and content strategy. Use when the user wants better search visibility, SEO remediation, schema markup, sitemap/robots work, or keyword mapping.