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Claude Skills by nimadorostkar
github.com/nimadorostkar138 skills5 installs181 views
- Agent InstructionsUse when writing project instructions for a coding agent (CLAUDE.md, AGENTS.md, or equivalent). Covers what belongs in them, what does not, structure, and keeping them accurate as the project changes.Votes: 0GitHub stars: 26
- Agent MemoryUse when an agent needs state that survives a session or a context compaction. Covers what to persist, file-based memory, structuring notes for retrieval, and preventing memory from becoming stale.Votes: 0GitHub stars: 26
- HooksUse when automating agent behavior with lifecycle hooks. Covers hook events, deterministic enforcement of rules the model should not be trusted to remember, and avoiding hooks that make an agent unusable.Votes: 0GitHub stars: 26
- Plugin DevelopmentUse when packaging skills, commands, hooks, and MCP servers into a distributable plugin. Covers manifest structure, bundling, versioning, testing, and distribution through a marketplace.Votes: 0GitHub stars: 26
- Skill AuthoringUse when writing a new skill for an AI agent. Covers scoping, description writing for reliable triggering, progressive disclosure, and the difference between a skill and a prompt.Votes: 0GitHub stars: 26
- Skill ReviewUse when reviewing or improving an existing agent skill. Covers triggering accuracy, content quality, redundancy with the base model, and measuring whether the skill actually changes behavior.Votes: 0GitHub stars: 26
- Slash CommandsUse when creating reusable slash commands for an agent. Covers when a command beats a skill, argument handling, composing tool calls, and writing commands that are worth typing.Votes: 0GitHub stars: 26
- SubagentsUse when delegating work to a separate agent with its own context. Covers when delegation pays off, designing a subagent's scope and prompt, parallel fan-out, and the cost of a cold start.Votes: 0GitHub stars: 26
- Agent DesignUse when building an LLM agent that uses tools over multiple steps. Covers tool design, the agent loop, error recovery, termination, human checkpoints, and knowing when an agent is the wrong architecture.Votes: 0GitHub stars: 26
- Context EngineeringUse when managing what an LLM sees. Covers context-window budgeting, retrieval and compaction, memory across turns, tool-result pruning, and the failure modes that come from too much context rather than too little.Votes: 0GitHub stars: 26
- Fine TuningUse when considering fine-tuning a model. Covers when fine-tuning beats prompting or RAG, dataset construction, LoRA and full fine-tuning, evaluation, and the failure modes that waste the effort.Votes: 0GitHub stars: 26
- Llm Cost OptimizationUse when an LLM feature costs too much. Covers prompt caching, context reduction, model routing, batching, output limits, and finding where the tokens actually go.Votes: 0GitHub stars: 26
- Llm EvaluationUse when measuring the quality of an LLM feature. Covers building an evaluation set, choosing metrics, LLM-as-judge and its pitfalls, regression testing prompts, and evaluating in production.Votes: 0GitHub stars: 26
- Llm IntegrationUse when integrating an LLM API into an application. Covers streaming, retries and rate limits, timeouts, caching, fallback across providers, and the production concerns that a tutorial integration ignores.Votes: 0GitHub stars: 26
- Mcp ServerUse when building a Model Context Protocol server. Covers tool, resource, and prompt design, transport choice, authentication, error handling, and testing an MCP server against a real client.Votes: 0GitHub stars: 26
- Ml PipelineUse when building or operating a machine learning pipeline. Covers feature engineering, training reproducibility, train/serve skew, deployment, monitoring for drift, and retraining.Votes: 0GitHub stars: 26
- Model SelectionUse when choosing which LLM to use for a task. Covers matching capability to task, cost and latency trade-offs, routing between models, benchmark skepticism, and evaluating on your own data.Votes: 0GitHub stars: 26
- Prompt EngineeringUse when writing or improving prompts for a language model. Covers instruction structure, examples, reasoning elicitation, output formatting, and systematically diagnosing why a prompt fails.Votes: 0GitHub stars: 26
- RagUse when building retrieval-augmented generation. Covers chunking, embedding and hybrid search, reranking, grounding and citation, and diagnosing whether a bad answer is a retrieval failure or a generation failure.Votes: 0GitHub stars: 26
- Structured OutputUse when an LLM must return machine-readable data. Covers schema design for models, native structured-output modes, validation and repair, and extraction that survives contact with messy input.Votes: 0GitHub stars: 26
- Api DesignUse when designing or reviewing an HTTP API. Covers resource modeling, status codes, pagination, idempotency, versioning, error formats, and the contract details that break clients when you get them wrong.Votes: 0GitHub stars: 26
- Background JobsUse when designing asynchronous job processing. Covers queue selection, idempotency, retry and backoff policy, scheduling, poison messages, and observability for work that happens outside the request.Votes: 0GitHub stars: 26
- CachingUse when adding or debugging a cache. Covers cache placement, invalidation strategies, stampede protection, TTL selection, and the consistency you are trading away.Votes: 0GitHub stars: 26
- DjangoUse when building Django applications. Covers ORM query performance, model design, migrations, Django REST Framework, security defaults, and testing.Votes: 0GitHub stars: 26
- Event Driven ArchitectureUse when designing systems around events and message queues. Covers event schema design, delivery guarantees, idempotent consumers, ordering, dead-letter handling, and event sourcing.Votes: 0GitHub stars: 26
- FastapiUse when building APIs with FastAPI. Covers dependency injection, Pydantic v2 validation, async database access, authentication, background tasks, and testing.Votes: 0GitHub stars: 26
- GraphqlUse when designing or operating a GraphQL API. Covers schema design, resolver performance and DataLoader batching, query cost limiting, error handling, and federation.Votes: 0GitHub stars: 26
- MicroservicesUse when decomposing a system into services, or deciding whether to. Covers service boundaries, inter-service communication, distributed data, saga patterns, and the operational cost of the network.Votes: 0GitHub stars: 26
- NestjsUse when building NestJS services. Covers module structure, providers and scopes, validation pipes, guards and interceptors, TypeORM/Prisma integration, and testing.Votes: 0GitHub stars: 26
- Realtime WebsocketsUse when building realtime features with WebSockets or Server-Sent Events. Covers protocol choice, connection lifecycle, reconnection and backfill, scaling across instances, and backpressure.Votes: 0GitHub stars: 26
- Spring BootUse when building Spring Boot services. Covers dependency injection, transaction boundaries, JPA performance, configuration, validation, and testing slices.Votes: 0GitHub stars: 26
- Competitive AnalysisUse when analyzing competitors. Covers gathering evidence rather than opinion, positioning and pricing analysis, identifying real differentiation, and producing a conclusion that changes a decision.Votes: 0GitHub stars: 26
- Lead ResearchUse when researching prospects or accounts before outreach. Covers qualification, finding the trigger event, identifying the right contact, and producing a brief that makes outreach specific rather than generic.Votes: 0GitHub stars: 26
- Product AnalysisUse when analyzing a product's performance or deciding what to build. Covers metric selection, funnel and retention analysis, distinguishing signal from noise, and prioritizing on evidence.Votes: 0GitHub stars: 26
- ResumeUse when writing or tailoring a CV or resume. Covers evidence over adjectives, tailoring to a specific role without lying, structure, and the specific patterns that get a resume discarded.Votes: 0GitHub stars: 26
- User ResearchUse when planning, running, or synthesizing user research. Covers interview questions that produce evidence rather than opinions, usability testing, and turning transcripts into findings.Votes: 0GitHub stars: 26
- Data ModelingUse when designing a schema. Covers normalization and when to break it, choosing keys, modeling time and history, soft deletes, multi-tenancy, and schema decisions that are expensive to reverse.Votes: 0GitHub stars: 26
- Data QualityUse when validating a dataset or building quality checks into a pipeline. Covers profiling, schema and constraint validation, freshness and completeness checks, anomaly detection, and failing a pipeline correctly.Votes: 0GitHub stars: 26
- Database PerformanceUse when a database is the bottleneck. Covers finding the expensive queries, index strategy, lock contention, connection saturation, and the schema decisions that make queries fast or impossible.Votes: 0GitHub stars: 26
- PandasUse when analyzing or transforming tabular data in Python. Covers vectorized operations, memory-efficient dtypes, correct joins, groupby patterns, and avoiding the silent errors pandas makes easy.Votes: 0GitHub stars: 26
- PostgresUse when working with PostgreSQL specifically. Covers indexing, MVCC and vacuum, connection pooling, partitioning, JSONB, replication, and the operational realities that separate Postgres from generic SQL.Votes: 0GitHub stars: 26
- SparkUse when building distributed data pipelines with Apache Spark. Covers partitioning, shuffles, skew, joins, caching, and reading the Spark UI to find why a job is slow.Votes: 0GitHub stars: 26
- Brand GuidelinesUse when applying or documenting a brand identity. Covers extracting a system from existing assets, documenting colour, type, and voice, and applying a brand consistently across new work.Votes: 0GitHub stars: 26
- Generative ArtUse when creating algorithmic or generative visual art. Covers composition through code, controlled randomness, colour systems, and building work that is varied without being arbitrary.Votes: 0GitHub stars: 26
- ThemingUse when building a theme or theming system for an interface. Covers token architecture, dark mode, contrast preservation across themes, and generating a coherent palette from a seed colour.Votes: 0GitHub stars: 26
- Ui ReviewUse when critiquing an interface design. Covers hierarchy, affordance, consistency, states, and delivering feedback that changes the design rather than describing taste.Votes: 0GitHub stars: 26
- Visual DesignUse when creating posters, covers, social graphics, or other static visual designs. Covers composition, typographic hierarchy, colour, and producing original work rather than pastiche.Votes: 0GitHub stars: 26
- Architecture DecisionsUse when a significant technical decision needs to be recorded. Produces Architecture Decision Records that capture context, options, the decision, and its consequences — so the reasoning survives the people who made it.Votes: 0GitHub stars: 26
- Bug Fix ProtocolUse when fixing a reported bug end to end. Enforces a disciplined sequence — reproduce, write a failing test, fix minimally, verify, prevent recurrence — and blocks the common failure mode of patching symptoms.Votes: 0GitHub stars: 26
- Cli DevelopmentUse when building command-line tools. Covers argument design, exit codes, streams and piping, progress output, configuration precedence, and behavior that respects the shell.Votes: 0GitHub stars: 26