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Claude Skills by sharpdeveye

github.com/sharpdeveye
25 skillsA× 251 installs4 views
AccelerateA

Use when the workflow is too slow, too expensive, or both and needs latency, cost, or token usage optimization.

data-aigoperformance
0
204
Adapt WorkflowA

Use when porting a workflow to a different AI provider, deployment environment, model tier, or organizational context.

data-aigorails
0
204
Agent WorkflowA

Use when any Maestro command is invoked — provides foundational workflow design principles across prompt engineering, context management, tool orchestration, agent architecture, feedback loops, knowledge systems, and guardrails.

data-airustgo
0
204
AmplifyA

Use when the workflow works but needs to handle more complex cases or produce higher-quality output through better tools, context, prompts, or models.

data-aigotesting
0
204
CalibrateA

Use when workflow components are inconsistent, naming conventions vary, or a new team member's work needs alignment to project standards.

data-aigorefactoring
0
204
CaptureA

Capture a session summary — what was done, what decisions were made, and what to do next.

data-aigoapi
0
204
ChainA

Use when the workflow needs multi-step processing with sequential, parallel, or conditional tool compositions and proper data flow.

data-aigo
0
204
ComposeA

Use when a single agent demonstrably cannot handle the task and multi-agent coordination is justified.

data-aigoperformance
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204
DiagnoseA

Use when the user wants to find problems, audit workflow quality, or get a comprehensive health check on their AI workflow.

data-aigoapi
0
204
EnrichA

Use when the agent needs access to information beyond its training data — knowledge sources, RAG pipelines, or grounding data.

data-aigorails
0
204
EvaluateA

Use when the user wants a quality review, interaction audit, or to test the workflow against realistic scenarios.

data-aigotesting
0
204
Extract PatternA

Use when the user wants to create templates, extract reusable patterns, document solutions, or build a pattern library from working workflows.

data-aigotesting
0
204
FortifyA

Use when the workflow lacks error handling, has been failing in production, or needs retry logic, fallback strategies, and circuit breakers.

data-aigorails
0
204
GuardA

Use when deploying to production, handling sensitive data, or the workflow needs safety constraints, input validation, and security boundaries.

data-airustgo
0
204
IterateA

Use when the workflow needs to self-correct, improve over time, or establish feedback loops and evaluation cycles.

data-aigotesting
0
204
Onboard AgentA

Use when starting a new project, adding a new agent to an existing system, or setting up workflow infrastructure from scratch.

data-aigogit
0
204
RecapA

Quick summary of the last session — commands run, files changed, and what to do next.

data-aigo
0
204
RefineA

Use when the workflow works but needs polish, or as the final step in a diagnose → fix → refine cycle before shipping.

data-aigo
0
204
ReflectA

Analyze command history to identify which skills work, which fail, and where to improve.

data-aigo
0
204
SpecializeA

Use when the user wants to tailor a workflow for a specific industry, domain, or vertical with specialized expertise, terminology, and guardrails.

data-aigorails
0
204
StreamlineA

Use when the workflow feels too complex, has accumulated cruft, or has redundant steps and overlapping tools that need consolidation.

data-aigorails
0
204
Teach MaestroA

Use when starting a new project with Maestro or when no .maestro.md context file exists yet. Run once per project.

data-aigoapi
0
204
TemperA

Use when the workflow feels over-engineered, has premature optimizations, unnecessary abstraction layers, or complexity beyond actual requirements.

data-aigorails
0
204
TurbochargeA

Use when the user wants to push past conventional workflow limits with advanced performance techniques like parallel orchestration, streaming pipelines, or adaptive routing.

data-aigotesting
0
204
Zero DefectA

Use when you need maximum precision on a critical task — production deployments, security-sensitive code, financial calculations, or any work where mistakes are unacceptable.

data-aigoexpress
0
204