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PythonReactSEOPDF

antoniocascais AI Skills

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Explorez les skills IA publiés de antoniocascais et consultez leurs instructions source.

Statistiques de l’auteur

Skills indexés

7

Étoiles des dépôts des skills indexés

35

Forks du dépôt

0

Moyenne des étoiles par skill indexé

5

Les étoiles d’un dépôt peuvent se répéter entre ses skills.

7 skills

Explorer

GitHub

c7

antoniocascais

Fetches up-to-date library documentation from Context7 and saves to /tmp/context7/. Use when needing current API docs, code examples, library references, SDK documentation, or checking latest library versions....

GitHub

codemap

antoniocascais

Generate navigational codebase maps with architecture diagrams. Use when mapping a codebase, creating architecture docs, visualizing project structure, generating infrastructure diagrams, understanding repo lay...

GitHub

git-commit

antoniocascais

Plans and executes git commits with optional TICKET_ID prefix. Analyzes staged changes, proposes optimal commit structure (single or multiple), generates descriptive messages with technical context, and execute...

GitHub

workflow-review

antoniocascais

Reviews Claude Code sessions and proposes workflow improvements. Use when: (1) /workflow-review command, (2) "review my workflow", "how can I improve", (3) after long sessions when nudged, (4) start of session...

GitHub

skill-creator

antoniocascais

Creates new Claude Code skills with proper structure and best practices. Use when user wants to create a skill, add a new command, scaffold a workflow, or asks "how do I make a skill".

GitHub

pr-review

antoniocascais

Reviews code changes before merging. Use when reviewing PRs, checking staged changes, reviewing diffs, code review, merge readiness check, or validating changes before commit/push.

GitHub

codex

antoniocascais

AI peer review via OpenAI Codex CLI. Use when reviewing code changes, validating technical decisions, comparing implementation approaches, or getting a second opinion on architecture choices. Triggers on /codex...

Le classement utilise les étoiles du dépôt GitHub indexé. Plusieurs skills peuvent partager ce nombre : ce n’est ni un nombre d’installations ni une note de qualité.