Explorar skills

Operate the local Fitness Agent CLI for Strava and Obsidian workflows. Use when Codex needs to authenticate with Strava, list or fetch Strava activities, export workout data as Obsidian-compatible Markdown, configure a local Obsidian vault path, or write/sync workout notes into a vault from the local fitness-agent project.

Analyze Strava-derived fitness notes that already exist in an Obsidian vault, then generate deeper dashboards, proxy training-load metrics, trend reports, and visual summaries. Use when the user wants post-sync analysis of existing workout notes rather than fetching new data from Strava.

Create forward-looking training plans from Strava-derived fitness notes already stored in an Obsidian vault. Use when the user wants a realistic next-week or next-two-week plan generated from recent load, consistency, sport mix, and recovery signals in their existing notes.

Operate the local Fitness Agent CLI for Strava and Obsidian workflows. Use when Codex needs to authenticate with Strava, list or fetch Strava activities, export workout data as Obsidian-compatible Markdown, configure a local Obsidian vault path, or write/sync workout notes into a vault from the local fitness-agent project.

Analyze Strava-derived fitness notes that already exist in an Obsidian vault, then generate deeper dashboards, proxy training-load metrics, trend reports, and visual summaries. Use when the user wants post-sync analysis of existing workout notes rather than fetching new data from Strava.

Use when about to commit, create a PR, or declare work complete — after verification-before-completion confirms tests pass but before code ships. Catches security vulnerabilities, logic errors, and business rule bugs that slip through despite passing tests.

Deep fluency in modern Python (3.14+) for production systems. Use when writing Python code, designing APIs, building data pipelines, creating async services, or optimizing performance. Covers typing, tooling (uv/pyright/ruff), FastAPI, Polars, observability, and engineering judgment.

Use when facing a task too large to hold in one piece — decompose into atomic subtasks. Divide-and-conquer in chengyu form. Pairs naturally with subagent dispatch (each part becomes one subagent's job) and with the dispatching-parallel-agents superpower. The decomposition itself is the skill — the parts should each have one clear purpose and a well-defined interface to the others.

Use when planning any non-trivial change — refactor, bug fix, migration, optimisation, communication — and the agent should find the natural seams / high-leverage points of the target system rather than brute-forcing through. Cut at joints (low resistance, clean result), not at bone (high resistance, blade dulls, messy result). The patron-saint chengyu of working WITH the grain of the system.

Create distinctive, production-grade frontend interfaces with high design quality. Use when building web components, pages, or applications. Generates creative, polished code that avoids generic AI aesthetics.

Analyze session history to identify inefficiency patterns and workflow gaps. Two modes: lightweight end-of-session reflection from conversation context, or comprehensive periodic analysis across multiple sessions via jq-extracted JSONL summaries. Produces structured reports with copy-paste-ready improvement proposals for CLAUDE.md, skills, commands, and automation.

Validates Containerfile COPY commands and Kubernetes manifest paths for consistency. Automatically runs when working with Containerfiles or K8s manifests to ensure source paths exist and destinations match between files.