Explorar skills

Patterns for building resilient API clients and real-time connections with retry logic, circuit breakers, and graceful degradation. Use when building production systems that need to handle failures. Triggers on retry logic, circuit breaker, connection resilience, exponential backoff, API client, fault tolerance.

Produces a structured monitoring brief that tells a reporter or editor exactly what to watch, where to watch it, and how often — so a developing story never slips through unnoticed.

Produces a clear, specific description of who a piece of content is written for — including what that audience already knows, what they care about, and what they will do with the information — so editors, writers, and distributors can make better decisions at every stage of production.

Meta-skill that orchestrates comprehensive testing across a project by coordinating testing-patterns, e2e-testing, and testing agents. Use when setting up testing for a new project, improving coverage for an existing project, establishing a testing strategy, or verifying quality before a release.

Execute implementation plans by dispatching a fresh subagent per task with two-stage review (spec compliance then code quality). Use when you have an implementation plan with mostly independent tasks and want high-quality, fast iteration within a single session.

Create a detailed refactor plan with tiny commits via user interview, then file it as a GitHub issue. Use when user wants to plan a refactor, create a refactoring RFC, or break a refactor into safe incremental steps.

Takes a specific claim and a list of sources you have gathered, then maps which sources support, contradict, or are silent on the claim — so you can see at a glance whether your sourcing is solid before publication.

Writes precise, publication-standard footnotes for data claims in a finished article — citing sources accurately, explaining how figures were derived where necessary, and flagging any caveats that qualified readers need to know.

This skill should be used when the user asks to "plan a feature", "what goes in the phase file", "which file owns this", "what evidence class is this", "how do I verify a claim no test can settle", "where should this doc go", or when writing, reading or updating any zforge feature document under docs/{feature}/. Provides the template structure, file ownership rules, the two-axis evidence scale for

Writes a professional LinkedIn post that frames a published article, investigation, or media project as a thought-leadership moment — driving clicks while positioning the author as a credible voice in their field.

Writes a set of Midjourney prompts designed to maintain consistent character appearance — face, build, clothing, and presence — across multiple generated images for use in editorial series, podcast artwork, YouTube thumbnails, or visual narratives.

lm-eval-harness: benchmark LLMs (MMLU, GSM8K, etc.).