Design production-ready observability strategies combining metrics, logs, and traces. Includes SLI/SLO design, golden-signals monitoring, alert optimization. Use when adding observability to a new service, refactoring alerting that is too noisy, or designing an SLO program before scaling production load.
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Category: Engineering
Tier: POWERFUL
Description: Design comprehensive observability strategies for production systems including SLI/SLO frameworks, alerting optimization, and dashboard generation.
Observability Designer creates production-ready dashboards, alert configurations, and monitoring strategies across the three pillars (metrics, logs, traces).
When NOT to use → slo-architect. For SLO/SLI design with error-budget math, multi-window burn-rate alerting thresholds, and SLO review gates, route to slo-architect — it is the authoritative skill for that half. This skill's slo_designer.py produces a quick scaffold only. This skill's lane: dashboards (dashboard_generator.py) and alert-noise reduction (alert_optimizer.py).
# Dashboard spec (Grafana JSON + docs) for a service
python3 scripts/dashboard_generator.py --service-type api --name payments --criticality critical --role sre --format grafana -o dashboard.json --doc-output dashboard.md
# Analyze an existing alert config for noise, duplicates, and coverage gaps
python3 scripts/alert_optimizer.py --input alerts.json --analyze-only --report alert_report.json
# ...then emit the optimized config once the report is reviewed:
python3 scripts/alert_optimizer.py --input alerts.json --output alerts_optimized.json
# Quick SLO scaffold (hand off to slo-architect for the real error-budget work)
python3 scripts/slo_designer.py --service-type api --criticality high --user-facing true --service-name payments -o slo_scaffold.json
Verification loop: after deploying optimized alerts, track the report's noise metrics for one on-call rotation — if the actionable-alert ratio didn't improve, re-run --analyze-only against the live config and iterate. Import the generated dashboard into Grafana and confirm every golden-signal panel renders with live data before closing the task.
This skill includes three powerful Python scripts for comprehensive observability design:
slo_designer.py)Generates complete SLI/SLO frameworks based on service characteristics:
alert_optimizer.py)Analyzes and optimizes existing alert configurations:
dashboard_generator.py)Creates comprehensive dashboard specifications:
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概要と使いどころ
Accessibility audit skill for scanning, fixing, and verifying WCAG 2.2 Level A and AA compliance across React, Next.js, Vue, Angular, Svelte, and plain HTML codebases. Use when auditing accessibility, fixing a11y violations, checking color contrast, generating compliance reports, or integrating accessibility checks into CI/CD pipelines.
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When the user wants to plan, design, or implement an A/B test or experiment. Also use when the user mentions "A/B test," "split test," "experiment," "test this change," "variant copy," "multivariate test," "hypothesis," "conversion experiment," "statistical significance," or "test this." For tracking implementation, see analytics-tracking.
日本語の概要は準備中です。原文の説明を表示しています。
When the user needs to generate, iterate, or scale ad creative for paid advertising. Use when they say 'write ad copy,' 'generate headlines,' 'create ad variations,' 'bulk creative,' 'iterate on ads,' 'ad copy validation,' 'RSA headlines,' 'Meta ad copy,' 'LinkedIn ad,' or 'creative testing.' This is pure creative production — distinct from paid-ads (campaign strategy). Use ad-creative when you need the copy, not the campaign plan.
日本語の概要は準備中です。原文の説明を表示しています。
Adversarial code review that breaks the self-review monoculture. Use when you want a genuinely critical review of recent changes, before merging a PR, or when you suspect Claude is being too agreeable about code quality. Forces perspective shifts through hostile reviewer personas that catch blind spots the author's mental model shares with the reviewer.
日本語の概要は準備中です。原文の説明を表示しています。
Answer Engine Optimization (AEO) skill — optimize content to be cited by AI language models (ChatGPT, Perplexity, Claude, Gemini, Mistral) as authoritative sources. Distinct from SEO — AEO optimizes for citation in LLM-generated responses, not search rankings. Use when planning content for AI-first search audiences, auditing existing content for E-E-A-T signals, tracking which pages get cited by which LLMs, or building a citation-friendly content strategy. Triggers — 'AEO audit', 'optimize for ChatGPT', 'get cited by Perplexity', 'LLM citation strategy', 'answer engine optimization', 'content for AI search', 'E-E-A-T audit'. Output is a markdown audit report (default) or JSON for pipeline integration. Stdlib-only Python tools.
日本語の概要は準備中です。原文の説明を表示しています。
Mint a tamper-evident, post-quantum-signed receipt for a consequential agent action (deploy, delete, pay, grant-access, model decision) so it can be verified later from the certificate alone. Use when an autonomous agent takes a side-effecting action that may need to be proven later, or when satisfying EU AI Act Article 12 record-keeping. Three decisions: whether an action needs a receipt, minting it, verifying it. Signing is delegated to the open-source OpenAgentOntology package. Not after-the-fact log analysis; not a hosted notary; not a legal opinion.
日本語の概要は準備中です。原文の説明を表示しています。