Skills de Claude Code · página 82

Research a company before applying or interviewing. Get an intelligence brief with culture, financials, recent news, team structure, key contacts, and smart interview questions. Use when someone says 'research this company', 'tell me about', 'what do you know about', or 'prep me for my interview at'.

Scan company career pages for job openings that match your profile. Uses web search with site-scoped queries to find listings on Greenhouse, Lever, Ashby, SmartRecruiters, and other ATS platforms. Use when someone says 'scan for jobs', 'check careers page', 'find openings at', or 'search for roles'.

Generate an ATS-optimized resume tailored to a specific job posting. Creates clean HTML you can print to PDF. Works for any industry. Use when someone says 'tailor my resume', 'make me a resume', 'create a resume for', or 'update my resume for'.

View and update your job application tracker. See all applications, filter by status, update progress, and get statistics on your search. Use when someone says 'show my applications', 'how is my job search going', 'update status', or 'tracker'.

Quick-score your pipeline of scan results. Ranks every role in your pipeline by fit and recommends which ones deserve a full evaluation. Use when someone says 'triage my pipeline', 'which scanned jobs should I apply to', 'rank my pipeline', or 'process my scan results'.

Investigate GitHub Actions and SHAFT Allure failures from a run, PR, or job; separate code defects from infrastructure and provider failures.

Diagnose and stabilize intermittent SHAFT tests, especially TestNG parallelism, shared state, files, drivers, timing, and external dependencies.

Apply SHAFT production Java compatibility, API, properties, logging, Allure, and WebDriver rules when editing src/main Java files.

Apply SHAFT Java test lifecycle, parallelism, isolation, Allure, browser, and regression rules when editing src/test Java files.

Prepare or review a SHAFT release by checking reactor versions, metadata, examples, consumer fixtures, stable dependency updates, and Maven Central publication safeguards.

Archived historical SHAFT_ENGINE Gemma integration retained outside active skill discovery.

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Migrate prompts and code from Claude Sonnet 4.0, Sonnet 4.5, or Opus 4.1 to Opus 4.5. Use when the user wants to update their codebase, prompts, or API calls to use Opus 4.5. Handles model string updates and prompt adjustments for known Opus 4.5 behavioral differences. Does NOT migrate Haiku 4.5.

Evaluates agent skills against Anthropic's best practices. Use when asked to review, evaluate, assess, or audit a skill for quality. Analyzes SKILL.md structure, naming conventions, description quality, content organization, and identifies anti-patterns. Produces actionable improvement recommendations.

Airflow adapter pattern for v2/v3 API compatibility. Use when working with adapters, version detection, or adding new API methods that need to work across Airflow 2.x and 3.x.

Use when the user needs human-in-the-loop workflows in Airflow (approval/reject, form input, or human-driven branching). Covers ApprovalOperator, HITLOperator, HITLBranchOperator, HITLEntryOperator, HITLTrigger. Requires Airflow 3.1+. Does not cover AI/LLM calls (see airflow-ai).

Build Airflow 3.1+ plugins that embed FastAPI apps, custom UI pages, React components, middleware, macros, and operator links directly into the Airflow UI. Use this skill whenever the user wants to create an Airflow plugin, add a custom UI page or nav entry to Airflow, build FastAPI-backed endpoints inside Airflow, serve static assets from a plugin, embed a React app in the Airflow UI, add middleware to the Airflow API server, create custom operator extra links, or call the Airflow REST API from inside a plugin. Also trigger when the user mentions AirflowPlugin, fastapi_apps, external_views, react_apps, plugin registration, or embedding a web app in Airflow 3.1+. If someone is building anything custom inside Airflow 3.1+ that involves Python and a browser-facing interface, this skill almost certainly applies.

Queries, manages, and troubleshoots Apache Airflow using the af CLI. Covers listing DAGs, triggering runs, reading task logs, diagnosing failures, debugging DAG import errors, checking connections, variables, pools, and monitoring health. Also routes to sub-skills for writing DAGs, debugging, deploying, and migrating Airflow 2 to 3. Use when user mentions "Airflow", "DAG", "DAG run", "task log", "import error", "parse error", "broken DAG", or asks to "trigger a pipeline", "debug import errors", "check Airflow health", "list connections", "retry a run", or any Airflow operation. Do NOT use for warehouse/SQL analytics on Airflow metadata tables — use analyzing-data instead.

Queries data warehouse and answers business questions about data. Handles questions requiring database/warehouse queries including "who uses X", "how many Y", "show me Z", "find customers", "what is the count", data lookups, metrics, trends, or SQL analysis.

Annotate Airflow tasks with data lineage using inlets and outlets. Use when the user wants to add lineage metadata to tasks, specify input/output datasets, or enable lineage tracking for operators without built-in OpenLineage extraction.

Workflow and best practices for writing Apache Airflow DAGs. Use when the user wants to create a new DAG, write pipeline code, or asks about DAG patterns and conventions. For testing and debugging DAGs, see the testing-dags skill.

Define reusable Airflow task group templates with Pydantic validation and compose DAGs from YAML. Use when creating blueprint templates, composing DAGs from YAML, validating configurations, or enabling no-code DAG authoring for non-engineers.

Quick data freshness check. Use when the user asks if data is up to date, when a table was last updated, if data is stale, or needs to verify data currency before using it.

Use when turning a dbt Core project into an Airflow DAG/TaskGroup using Astronomer Cosmos. Does not cover dbt Fusion. Before implementing, verify dbt engine, warehouse, Airflow version, execution environment, DAG vs TaskGroup, and manifest availability.

Use when running a dbt Fusion project with Astronomer Cosmos. Covers Cosmos 1.11+ configuration for Fusion on Snowflake/Databricks with ExecutionMode.LOCAL. Before implementing, verify dbt engine is Fusion (not Core), warehouse is supported, and local execution is acceptable. Does not cover dbt Core.

Create custom OpenLineage extractors for Airflow operators. Use when the user needs lineage from unsupported or third-party operators, wants column-level lineage, or needs complex extraction logic beyond what inlets/outlets provide.

Author Apache Airflow DAGs declaratively with dag-factory YAML configs. Use when creating dag-factory templates, composing DAGs from YAML for dag-factory, configuring defaults/dynamic tasks/datasets/callbacks for dag-factory, or validating dag-factory configurations.

Comprehensive DAG failure diagnosis and root cause analysis. Use for complex debugging requests requiring deep investigation like "diagnose and fix the pipeline", "full root cause analysis", "why is this failing and how to prevent it". For simple debugging ("why did dag fail", "show logs"), the airflow entrypoint skill handles it directly. This skill provides structured investigation and prevention recommendations.

Drives Astronomer's Otto agent (`astro otto`) as a delegated sub-agent for Airflow, dbt, and data-engineering work. Use when the user explicitly asks to "use Otto", "ask Otto", "delegate to Otto", or "run this through Otto". Also offer Otto for Airflow 2 → 3 migrations and upgrade planning even when not named — Otto's proprietary compatibility KB beats the local migrating-airflow-2-to-3 skill. Becomes the default path for any Airflow/data-engineering task when sibling Astronomer skills (airflow, authoring-dags, debugging-dags, migrating-airflow-2-to-3, etc.) are NOT loaded in the current session. Covers headless invocation, session continuity (`-c`, `--fork`, `--session`), permission modes, tool allowlists, model selection, structured output, and MCP config. **Do not load this skill if you are Otto** — Otto must not delegate to itself.

Deploy Airflow DAGs and projects. Use when the user wants to deploy code, push DAGs, set up CI/CD, deploy to production, or asks about deployment strategies for Airflow.

Manage Astronomer production deployments with Astro CLI. Use when the user wants to authenticate, switch workspaces, create/update/delete deployments, or deploy code to production.

Manage local Airflow environment with Astro CLI (Docker and standalone modes). Use when the user wants to start, stop, or restart Airflow, view logs, query the Airflow API, troubleshoot, or fix environment issues. For project setup, see setting-up-astro-project.

Migrates Airflow projects from airflow-ai-sdk to apache-airflow-providers-common-ai 0.1.0+. Use this skill when the user wants to replace airflow-ai-sdk with the official Airflow AI provider, migrate LLM decorators (@task.llm, @task.agent, @task.llm_branch, @task.embed), switch from model strings/objects to connection-based LLM configuration, or update imports from airflow_ai_sdk to the new provider. Also trigger when the user mentions common-ai provider, AIP-99, pydanticai connection, or migrating away from airflow-ai-sdk.

Guide for migrating Apache Airflow 2.x projects to Airflow 3.x. Use when the user mentions Airflow 3 migration, upgrade, compatibility issues, breaking changes, or wants to modernize their Airflow codebase. If you detect Airflow 2.x code that needs migration, prompt the user and ask if they want you to help upgrade. Always load this skill as the first step for any migration-related request.

Deep-dive data profiling for a specific table. Use when the user asks to profile a table, wants statistics about a dataset, asks about data quality, or needs to understand a table's structure and content. Requires a table name.

Initialize and configure Astro/Airflow projects. Use when the user wants to create a new project, set up dependencies, configure connections/variables, or understand project structure. For running the local environment, see managing-astro-local-env.

Complex DAG testing workflows with debugging and fixing cycles. Use for multi-step testing requests like "test this dag and fix it if it fails", "test and debug", "run the pipeline and troubleshoot issues". For simple test requests ("test dag", "run dag"), the airflow entrypoint skill handles it directly. This skill is for iterative test-debug-fix cycles.

Trace downstream data lineage and impact analysis. Use when the user asks what depends on this data, what breaks if something changes, downstream dependencies, or needs to assess change risk before modifying a table or DAG.

Trace upstream data lineage. Use when the user asks where data comes from, what feeds a table, upstream dependencies, data sources, or needs to understand data origins.

Troubleshoot Astronomer production deployments with Astro CLI. Use when investigating deployment issues, viewing production logs, analyzing failures, or managing deployment environment variables.

Initialize warehouse schema discovery. Generates .astro/warehouse.md with all table metadata for instant lookups. Run once per project, refresh when schema changes. Use when user says "/astronomer-data:warehouse-init" or asks to set up data discovery.

Use Agent Mail from Codex for file leases, notifications, inboxes, and conflict prevention.

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Use when converting markdown plans into br beads with dependencies for implementation or swarm execution.

Use when switching AI coding CLI accounts quickly to recover from subscription rate limits or OAuth friction.

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Use when starting non-trivial work, mining lessons, or preventing repeated mistakes with cm procedural memory.

Mine past agent sessions for working prompts, decisions, and patterns. Use when "what did I ask?", "find that prompt", session archaeology, or agent history.

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Use when creating GitHub Actions workflows, release automation, checksums, signing, or CI/CD.

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Use when diagnosing runaway processes with the pt wrapper and choosing safe remediation.

Use when offloading slow builds to remote workers or recovering RCH worker, hook, SSH, sync, or disk issues.

Use when applying a skill repeatedly with progressive deepening for iterative improvement.

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Use when using ru for multi-repo commits, sync, GitHub review, or maintenance automation.

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Use when configuring SSH access, keys, tunnels, host diagnostics, or safe remote command workflows.

Use when restoring machine responsiveness from high CPU, memory, IO, cache, or runaway process pressure.

Use when reviewing code with UBS for bugs, security issues, AI-generated quality, or pre-commit checks.

Use when tending NTM agent swarms, unsticking panes, handling rate limits, or coordinating convergence.

Explain AgentOps workflows.

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Manage the PROGRAM.md/AUTODEV.md contract consumed by evolve/factory ticks. Use for loop rules, boundaries, or PROGRAM.md repair.

Track br issues and dependencies for Codex agents. Triggers: "beads", "br", "track issue", "create task", "find work", "ready issues".

Initialize AgentOps project files.

Separate goals from implementation.

Compile .agents knowledge wiki.

Multi-perspective review for Codex using the current sub-agent runtime. Triggers: "council", "get consensus", "multi-model review", "multi-perspective review", "council validate", "council brainstorm", "council research".

Hands-free epic execution for Codex using wave-based sub-agents and lead-side validation. Triggers: "crank", "run epic", "execute epic", "run all tasks", "hands-free execution", "crank it".

Validate product fit before discovery. Use when: framing a problem, checking product/market fit, or pressure-testing user value before writing a discovery packet or any code.

Create dense execution packets.

Run autonomous improvement loops.

Check knowledge flywheel health.

Mine transcripts into learnings.

Maintain AgentOps goals.

Write compact session handoffs.

Implement one tracked issue.

Load relevant .agents context.

Install or run the seven-move operating-loop Workflow for AgentOps plugin users and multi-agent orchestration.

Decompose goals into issue plans.

Review completed work and learn. Use when: a task, PR arc, or session is finished and you want to extract learnings, or after ≥5 PRs (the scope checkpoint).

Stress-test plans before work. Use when: a plan is drafted but not yet executed and you want to surface failure modes, risks, and what would prove it wrong before committing.

Validate, commit, and push.

Record Brownian Ratchet gates.

Recover session context.

Probe docs and skills. Use when: adversarially probing a doc, skill, plan, or claim for weaknesses, gaps, or unstated assumptions before it ships.

Deep codebase exploration with multi-angle analysis for Codex agents. Output: .agents/research/*.md. Triggers: "research", "investigate", "explore codebase", "how does X work".