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Found 1,833 Skills
Expert deployment engineer specializing in modern CI/CD pipelines, GitOps workflows, and advanced deployment automation. Masters GitHub Actions, ArgoCD/Flux, progressive delivery, container security, and platform engineering. Handles zero-downtime deployments, security scanning, and developer experience optimization. Use PROACTIVELY for CI/CD design, GitOps implementation, or deployment automation.
You are a data pipeline architecture expert specializing in scalable, reliable, and cost-effective data pipelines for batch and streaming data processing.
Security testing patterns including SAST, DAST, penetration testing, and vulnerability assessment techniques. Use when implementing security testing pipelines, conducting security audits, or validating application security controls.
Switch AI providers or models without breaking things. Use when you want to switch from OpenAI to Anthropic, try a cheaper model, stop depending on one vendor, compare models side-by-side, a model update broke your outputs, you need vendor diversification, or you want to migrate to a local model. Covers DSPy model portability — provider config, re-optimization, model comparison, and multi-model pipelines.
Transform raw data into analytical assets using ETL/ELT patterns, SQL (dbt), Python (pandas/polars/PySpark), and orchestration (Airflow). Use when building data pipelines, implementing incremental models, migrating from pandas to polars, or orchestrating multi-step transformations with testing and quality checks.
Enforce secure secrets management across all platforms. Never hardcode OAuth2 secrets, API keys, tokens, passwords, or credentials in source code. Store all secrets in .env files, load from environment variables, and ensure .env is gitignored. Use this skill when: (1) writing any code that uses API keys, OAuth2 client secrets, tokens, or credentials, (2) setting up authentication or third-party integrations, (3) creating new projects that need environment configuration, (4) reviewing code for security issues related to secrets, (5) configuring CI/CD pipelines or Docker deployments with secrets. Triggers: API key, OAuth, client secret, token, credentials, .env, environment variables, secret, password, authentication setup, third-party integration.
Code quality verification gates wired into the agent lifecycle. Use this skill whenever writing, modifying, reviewing, or debugging code — including new features, bug fixes, refactors, troubleshooting, CI/CD setup, or project bootstrapping. Also use when the user mentions "quality", "testing strategy", "CI pipeline", "guardrails", "debugging", or asks how to improve code reliability. If you're writing code or trying to understand why code isn't working, this skill applies.
Optimize a prompt through a critique-compress pipeline with semantic equivalence verification at each stage. Applies think-critically to improve the prompt, then compress-prompt to reduce it, validating that behavior is preserved after each transformation.
Create and manage GitLab projects, merge requests, pipelines, issues, branches, and more using the orbit CLI. Use this skill whenever the user asks about GitLab repositories, MRs (merge requests), CI/CD pipelines, branches, tags, commits, issues, groups, or project members. Trigger on phrases like 'list MRs', 'check the pipeline', 'create a branch', 'open a merge request', 'view the latest commits', 'list projects in group X', 'retry the CI', 'close the issue', 'who are the members', or any GitLab-related task — even casual references like 'what's running in CI', 'show me the MRs', 'tag a release', 'check if it merged', or 'list repos'. Also trigger when the user mentions PR/pull request in a GitLab context (GitLab calls them merge requests). The orbit CLI alias is `gl`.
Use when designing and building knowledge graphs from unstructured data. Invoke when user mentions entity extraction, schema design, LPG vs RDF, graph data model, ontology alignment, knowledge graph construction, or building a KG for RAG. Provides extraction pipelines, schema patterns, and data model selection guidance.
Create custom multi-agent workflows for Atomic CLI using the defineWorkflow() session-based API with programmatic SDK code. Use this skill whenever the user wants to create a workflow, build an agent pipeline, define a multi-stage automation, set up a review loop, or connect multiple coding agents together. Also trigger when they mention workflow files, .atomic/workflows/, defineWorkflow, or ask how to automate a sequence of agent tasks — even if they don't use the word "workflow" explicitly.
Use when a user asks to automatically generate a CLI command for a website. Takes a URL and optional goal, runs the full verified generation pipeline (explore, synthesize, cascade, verify), and returns a structured outcome. This is the primary entry point for "帮我生成 xxx.com 的 cli".