Service Mesh Observability
wshobson/agents
Set up tracing, metrics and dashboards for Istio, Linkerd and other service meshes, with golden-signal alerts, SLOs and guidance on sampling and cardinality.
Design production observability strategies covering SLI/SLOs, metrics, logs, traces, dashboards, and alert quality.
Install Observability Designer
$ npx skills add aAAaqwq/AGI-Super-Team --skill observability-designer -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install aAAaqwq/AGI-Super-Team observability-designer --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/aAAaqwq/AGI-Super-Team.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/observability-designer .claude/skills/observability-designer && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "observability-designer" agent skill from https://github.com/aAAaqwq/AGI-Super-Team/tree/main/skills/observability-designer into .claude/skills/observability-designer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "observability-designer", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/aAAaqwq/AGI-Super-Team/tree/main/skills/observability-designerType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add aAAaqwq/AGI-Super-Team --skill observability-designer -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install aAAaqwq/AGI-Super-Team observability-designer --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/aAAaqwq/AGI-Super-Team.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/observability-designer .agents/skills/observability-designer && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "observability-designer" agent skill from https://github.com/aAAaqwq/AGI-Super-Team/tree/main/skills/observability-designer into .agents/skills/observability-designer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "observability-designer", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add aAAaqwq/AGI-Super-Team --skill observability-designer -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install aAAaqwq/AGI-Super-Team observability-designer --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/aAAaqwq/AGI-Super-Team.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/observability-designer .cursor/skills/observability-designer && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "observability-designer" agent skill from https://github.com/aAAaqwq/AGI-Super-Team/tree/main/skills/observability-designer into .cursor/skills/observability-designer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "observability-designer", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/aAAaqwq/AGI-Super-Team.git --path skills/observability-designer--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add aAAaqwq/AGI-Super-Team --skill observability-designer -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install aAAaqwq/AGI-Super-Team observability-designer --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/aAAaqwq/AGI-Super-Team.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/observability-designer .gemini/skills/observability-designer && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "observability-designer" agent skill from https://github.com/aAAaqwq/AGI-Super-Team/tree/main/skills/observability-designer into .gemini/skills/observability-designer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "observability-designer", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install aAAaqwq/AGI-Super-Team observability-designerInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add aAAaqwq/AGI-Super-Team --skill observability-designer -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/aAAaqwq/AGI-Super-Team.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/observability-designer .github/skills/observability-designer && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "observability-designer" agent skill from https://github.com/aAAaqwq/AGI-Super-Team/tree/main/skills/observability-designer into .github/skills/observability-designer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "observability-designer", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add aAAaqwq/AGI-Super-Team --skill observability-designer -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install aAAaqwq/AGI-Super-Team observability-designer --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/aAAaqwq/AGI-Super-Team.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/observability-designer .opencode/skills/observability-designer && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "observability-designer" agent skill from https://github.com/aAAaqwq/AGI-Super-Team/tree/main/skills/observability-designer into .opencode/skills/observability-designer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "observability-designer", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
observability-designer- GitHub stars
- 105
- Used in
- 1 other repo
- Token cost
- ~3.3k tokens
- SKILL.md length
- 1,402 words
- Files
- 13 (incl. scripts, references, assets)
- Skills in repo
- 167
- Repo updated
- First seen
- Licence
- MIT
At a glance
Design production observability strategies covering SLI/SLOs, metrics, logs, traces, dashboards, and alert quality.
- Works in 3 steps: SLO Designer (slo_designer.py) → Alert Optimizer (alert_optimizer.py) → Dashboard Generator…
- Tasks that involve Site reliability engineering
- SKILL.md covers Overview, Core Competencies, Scripts Overview and Integration Patterns, plus 3 more sections
- Runs Python scripts from its folder
What it does
Observability Designer is an agent skill from aAAaqwq/AGI-Super-Team. Design production observability strategies covering SLI/SLOs, metrics, logs, traces, dashboards, and alert quality.
Its SKILL.md is about 3.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 16 other files, including scripts, reference files and assets (for example `README.md`, `assets/sample_alerts.json` and `assets/sample_service_api.json`).
It sits in DevOps & Cloud, covering Site reliability engineering and Observability. The repository describes itself as: An installable, cross-framework AI organization: C-suite agents, expert subagents, curated skills, independent review, and one-command setup across 18 AI client/runtime adapters. The licence is MIT.
When your agent uses it
- Tasks that involve Site reliability engineering
- Tasks that involve Observability
Example prompts
- “/observability-designer”
Requirements
- Python 3
Workflow steps
3 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit 331ecd3. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships 3 files in scripts/ (Python), which the agent can run.
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Observability Designer loads about 3.3k tokens when it runs, and up to ~12k if it reads all its reference files. Until then it costs about 35 tokens; SKILL.md has 1,402 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.
SKILL.md
The full file from aAAaqwq/AGI-Super-Team at commit 331ecd3, republished under its MIT licence (© aAAaqwq). 1,402 words, ~3,313 tokens.
Download SKILL.mdSave it as
.claude/skills/observability-designer/SKILL.md (or your agent's skills folder). This skill also uses 12 other files; get the full folder from GitHub.- name
- observability-designer
- description
- Design production observability strategies covering SLI/SLOs, metrics, logs, traces, dashboards, and alert quality.
Observability Designer (POWERFUL)
Category: Engineering
Tier: POWERFUL
Description: Design comprehensive observability strategies for production systems including SLI/SLO frameworks, alerting optimization, and dashboard generation.
Overview
Observability Designer enables you to create production-ready observability strategies that provide deep insights into system behavior, performance, and reliability. This skill combines the three pillars of observability (metrics, logs, traces) with proven frameworks like SLI/SLO design, golden signals monitoring, and alert optimization to create comprehensive observability solutions.
Core Competencies
SLI/SLO/SLA Framework Design
- Service Level Indicators (SLI): Define measurable signals that indicate service health
- Service Level Objectives (SLO): Set reliability targets based on user experience
- Service Level Agreements (SLA): Establish customer-facing commitments with consequences
- Error Budget Management: Calculate and track error budget consumption
- Burn Rate Alerting: Multi-window burn rate alerts for proactive SLO protection
Three Pillars of Observability
Metrics
- Golden Signals: Latency, traffic, errors, and saturation monitoring
- RED Method: Rate, Errors, and Duration for request-driven services
- USE Method: Utilization, Saturation, and Errors for resource monitoring
- Business Metrics: Revenue, user engagement, and feature adoption tracking
- Infrastructure Metrics: CPU, memory, disk, network, and custom resource metrics
Logs
- Structured Logging: JSON-based log formats with consistent fields
- Log Aggregation: Centralized log collection and indexing strategies
- Log Levels: Appropriate use of DEBUG, INFO, WARN, ERROR, FATAL levels
- Correlation IDs: Request tracing through distributed systems
- Log Sampling: Volume management for high-throughput systems
Traces
- Distributed Tracing: End-to-end request flow visualization
- Span Design: Meaningful span boundaries and metadata
- Trace Sampling: Intelligent sampling strategies for performance and cost
- Service Maps: Automatic dependency discovery through traces
- Root Cause Analysis: Trace-driven debugging workflows
Dashboard Design Principles
Information Architecture
- Hierarchy: Overview → Service → Component → Instance drill-down paths
- Golden Ratio: 80% operational metrics, 20% exploratory metrics
- Cognitive Load: Maximum 7±2 panels per dashboard screen
- User Journey: Role-based dashboard personas (SRE, Developer, Executive)
Visualization Best Practices
- Chart Selection: Time series for trends, heatmaps for distributions, gauges for status
- Color Theory: Red for critical, amber for warning, green for healthy states
- Reference Lines: SLO targets, capacity thresholds, and historical baselines
- Time Ranges: Default to meaningful windows (4h for incidents, 7d for trends)
Panel Design
- Metric Queries: Efficient Prometheus/InfluxDB queries with proper aggregation
- Alerting Integration: Visual alert state indicators on relevant panels
- Interactive Elements: Template variables, drill-down links, and annotation overlays
- Performance: Sub-second render times through query optimization
Alert Design and Optimization
Alert Classification
- Severity Levels:
- Critical: Service down, SLO burn rate high
- Warning: Approaching thresholds, non-user-facing issues
- Info: Deployment notifications, capacity planning alerts
- Actionability: Every alert must have a clear response action
- Alert Routing: Escalation policies based on severity and team ownership
Alert Fatigue Prevention
- Signal vs Noise: High precision (few false positives) over high recall
- Hysteresis: Different thresholds for firing and resolving alerts
- Suppression: Dependent alert suppression during known outages
- Grouping: Related alerts grouped into single notifications
Alert Rule Design
- Threshold Selection: Statistical methods for threshold determination
- Window Functions: Appropriate averaging windows and percentile calculations
- Alert Lifecycle: Clear firing conditions and automatic resolution criteria
- Testing: Alert rule validation against historical data
Runbook Generation and Incident Response
Runbook Structure
- Alert Context: What the alert means and why it fired
- Impact Assessment: User-facing vs internal impact evaluation
- Investigation Steps: Ordered troubleshooting procedures with time estimates
- Resolution Actions: Common fixes and escalation procedures
- Post-Incident: Follow-up tasks and prevention measures
Incident Detection Patterns
- Anomaly Detection: Statistical methods for detecting unusual patterns
- Composite Alerts: Multi-signal alerts for complex failure modes
- Predictive Alerts: Capacity and trend-based forward-looking alerts
- Canary Monitoring: Early detection through progressive deployment monitoring
Golden Signals Framework
Latency Monitoring
- Request Latency: P50, P95, P99 response time tracking
- Queue Latency: Time spent waiting in processing queues
- Network Latency: Inter-service communication delays
- Database Latency: Query execution and connection pool metrics
Traffic Monitoring
- Request Rate: Requests per second with burst detection
- Bandwidth Usage: Network throughput and capacity utilization
- User Sessions: Active user tracking and session duration
- Feature Usage: API endpoint and feature adoption metrics
Error Monitoring
- Error Rate: 4xx and 5xx HTTP response code tracking
- Error Budget: SLO-based error rate targets and consumption
- Error Distribution: Error type classification and trending
- Silent Failures: Detection of processing failures without HTTP errors
Saturation Monitoring
- Resource Utilization: CPU, memory, disk, and network usage
- Queue Depth: Processing queue length and wait times
- Connection Pools: Database and service connection saturation
- Rate Limiting: API throttling and quota exhaustion tracking
Distributed Tracing Strategies
Trace Architecture
- Sampling Strategy: Head-based, tail-based, and adaptive sampling
- Trace Propagation: Context propagation across service boundaries
- Span Correlation: Parent-child relationship modeling
- Trace Storage: Retention policies and storage optimization
Service Instrumentation
- Auto-Instrumentation: Framework-based automatic trace generation
- Manual Instrumentation: Custom span creation for business logic
- Baggage Handling: Cross-cutting concern propagation
- Performance Impact: Instrumentation overhead measurement and optimization
Log Aggregation Patterns
Collection Architecture
- Agent Deployment: Log shipping agent strategies (push vs pull)
- Log Routing: Topic-based routing and filtering
- Parsing Strategies: Structured vs unstructured log handling
- Schema Evolution: Log format versioning and migration
Storage and Indexing
- Index Design: Optimized field indexing for common query patterns
- Retention Policies: Time and volume-based log retention
- Compression: Log data compression and archival strategies
- Search Performance: Query optimization and result caching
Cost Optimization for Observability
Show full SKILL.md (570 more words)Show less
Data Management
- Metric Retention: Tiered retention based on metric importance
- Log Sampling: Intelligent sampling to reduce ingestion costs
- Trace Sampling: Cost-effective trace collection strategies
- Data Archival: Cold storage for historical observability data
Resource Optimization
- Query Efficiency: Optimized metric and log queries
- Storage Costs: Appropriate storage tiers for different data types
- Ingestion Rate Limiting: Controlled data ingestion to manage costs
- Cardinality Management: High-cardinality metric detection and mitigation
Scripts Overview
This skill includes three powerful Python scripts for comprehensive observability design:
1. SLO Designer (slo_designer.py)
Generates complete SLI/SLO frameworks based on service characteristics:
- Input: Service description JSON (type, criticality, dependencies)
- Output: SLI definitions, SLO targets, error budgets, burn rate alerts, SLA recommendations
- Features: Multi-window burn rate calculations, error budget policies, alert rule generation
2. Alert Optimizer (alert_optimizer.py)
Analyzes and optimizes existing alert configurations:
- Input: Alert configuration JSON with rules, thresholds, and routing
- Output: Optimization report and improved alert configuration
- Features: Noise detection, coverage gaps, duplicate identification, threshold optimization
3. Dashboard Generator (dashboard_generator.py)
Creates comprehensive dashboard specifications:
- Input: Service/system description JSON
- Output: Grafana-compatible dashboard JSON and documentation
- Features: Golden signals coverage, RED/USE methods, drill-down paths, role-based views
Integration Patterns
Monitoring Stack Integration
- Prometheus: Metric collection and alerting rule generation
- Grafana: Dashboard creation and visualization configuration
- Elasticsearch/Kibana: Log analysis and dashboard integration
- Jaeger/Zipkin: Distributed tracing configuration and analysis
CI/CD Integration
- Pipeline Monitoring: Build, test, and deployment observability
- Deployment Correlation: Release impact tracking and rollback triggers
- Feature Flag Monitoring: A/B test and feature rollout observability
- Performance Regression: Automated performance monitoring in pipelines
Incident Management Integration
- PagerDuty/VictorOps: Alert routing and escalation policies
- Slack/Teams: Notification and collaboration integration
- JIRA/ServiceNow: Incident tracking and resolution workflows
- Post-Mortem: Automated incident analysis and improvement tracking
Advanced Patterns
Multi-Cloud Observability
- Cross-Cloud Metrics: Unified metrics across AWS, GCP, Azure
- Network Observability: Inter-cloud connectivity monitoring
- Cost Attribution: Cloud resource cost tracking and optimization
- Compliance Monitoring: Security and compliance posture tracking
Microservices Observability
- Service Mesh Integration: Istio/Linkerd observability configuration
- API Gateway Monitoring: Request routing and rate limiting observability
- Container Orchestration: Kubernetes cluster and workload monitoring
- Service Discovery: Dynamic service monitoring and health checks
Machine Learning Observability
- Model Performance: Accuracy, drift, and bias monitoring
- Feature Store Monitoring: Feature quality and freshness tracking
- Pipeline Observability: ML pipeline execution and performance monitoring
- A/B Test Analysis: Statistical significance and business impact measurement
Best Practices
Organizational Alignment
- SLO Setting: Collaborative target setting between product and engineering
- Alert Ownership: Clear escalation paths and team responsibilities
- Dashboard Governance: Centralized dashboard management and standards
- Training Programs: Team education on observability tools and practices
Technical Excellence
- Infrastructure as Code: Observability configuration version control
- Testing Strategy: Alert rule testing and dashboard validation
- Performance Monitoring: Observability system performance tracking
- Security Considerations: Access control and data privacy in observability
Continuous Improvement
- Metrics Review: Regular SLI/SLO effectiveness assessment
- Alert Tuning: Ongoing alert threshold and routing optimization
- Dashboard Evolution: User feedback-driven dashboard improvements
- Tool Evaluation: Regular assessment of observability tool effectiveness
Success Metrics
Operational Metrics
- Mean Time to Detection (MTTD): How quickly issues are identified
- Mean Time to Resolution (MTTR): Time from detection to resolution
- Alert Precision: Percentage of actionable alerts
- SLO Achievement: Percentage of SLO targets met consistently
Business Metrics
- System Reliability: Overall uptime and user experience quality
- Engineering Velocity: Development team productivity and deployment frequency
- Cost Efficiency: Observability cost as percentage of infrastructure spend
- Customer Satisfaction: User-reported reliability and performance satisfaction
This comprehensive observability design skill enables organizations to build robust, scalable monitoring and alerting systems that provide actionable insights while maintaining cost efficiency and operational excellence.
© aAAaqwq, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 12 other files (scripts, references, assets) in skills/observability-designer of aAAaqwq/AGI-Super-Team.
- SKILL.md
- README.md
- assets/sample_alerts.json
- assets/sample_service_api.json
- assets/sample_service_web.json
- expected_outputs/sample_dashboard.json
- expected_outputs/sample_slo_framework.json
- references/alert_design_patterns.md
- references/dashboard_best_practices.md
- references/slo_cookbook.md
- scripts/alert_optimizer.py
- scripts/dashboard_generator.py
- scripts/slo_designer.py
Open the folder on GitHubat commit 331ecd3
Used in 1 other repository
We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in aAAaqwq/AGI-Super-Team, which our catalogue first saw on October 7, 2026.
Compare with similar skills
Observability Designer next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Observability Designer this skillaAAaqwq/AGI-Super-Team | 105 | 1 repos | ~3.3k | Automated safety check: Pass | MIT | |
| Service Mesh Observabilitywshobson/agents | 40k | 9 repos | ~607 | Automated safety check: Pass | MIT | |
| Observability2SSK/dot-files | 247 | — | ~676 | Automated safety check: Pass | MIT | |
| Monitoring Observabilityahmedasmar/devops-claude-skills | 203 | — | ~3.9k | Automated safety check: Pass | None | |
| Prometheus Error Rate Investigatorprometheus/prometheus-mcp | 120 | — | ~592 | Automated safety check: Pass | Apache-2.0 | |
| Error HandlerEliasOulkadi/shokunin | 114 | — | ~3.6k | Automated safety check: Notes | MIT |
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Categories
Questions about Observability Designer
What does Observability Designer do?
Design production observability strategies covering SLI/SLOs, metrics, logs, traces, dashboards, and alert quality. Observability Designer is an agent skill from aAAaqwq/AGI-Super-Team. Design production observability strategies covering SLI/SLOs, metrics, logs, traces, dashboards, and alert quality.
When should I use Observability Designer?
Observability Designer fits situations like: tasks that involve Site reliability engineering; tasks that involve Observability.
How do I install Observability Designer in Claude Code?
Run `npx skills add aAAaqwq/AGI-Super-Team --skill observability-designer -a claude-code`. Or copy the skill folder (skills/observability-designer in aAAaqwq/AGI-Super-Team) into .claude/skills/observability-designer in your project. Claude Code loads it when a task matches its description.
How do I install Observability Designer in Codex?
Run `npx skills add aAAaqwq/AGI-Super-Team --skill observability-designer -a codex`. Or copy the skill folder (skills/observability-designer in aAAaqwq/AGI-Super-Team) into .agents/skills/observability-designer in your project. Codex loads it when a task matches its description.
Can I use Observability Designer in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add aAAaqwq/AGI-Super-Team --skill observability-designer -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/observability-designer, .gemini/skills/observability-designer, .github/skills/observability-designer and .opencode/skills/observability-designer in your project.
What does Observability Designer need to run?
Going by SKILL.md and its folder, Observability Designer needs Python for the scripts in its folder. Our summary lists: Python 3.
Does Observability Designer access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Observability Designer safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
What licence does Observability Designer use?
Observability Designer is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Observability Designer use?
About 3.3k tokens (SKILL.md is roughly 13k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 9.2k tokens, read only when the agent opens those files.
What are the alternatives to Observability Designer?
Skills that share tags, products or a category with Observability Designer: Service Mesh Observability (wshobson/agents, 40k stars), Observability (2SSK/dot-files, 247 stars), Monitoring Observability (ahmedasmar/devops-claude-skills, 203 stars) and Prometheus Error Rate Investigator (prometheus/prometheus-mcp, 120 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Observability Designer?
aAAaqwq (a GitHub user) maintains it in aAAaqwq/AGI-Super-Team, which has 105 GitHub stars. The repository holds 167 skills in this directory. The repository was last updated on October 8, 2026.
Source: aAAaqwq/AGI-Super-Team on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.