Results for “skill-files”

Audits a project's agent configuration files, establishes a source-of-truth skills directory, normalizes naming, and generates bridge files to make the workspace consistent across Claude Code, Codex, Grok, and generic Agents.

Scan installed skills to extract cross-cutting principles and distill them into rules files.

Bridge any skill folder or directory of skill folders to Claude Code, Codex, generic Agents, and Grok using symlinks plus Grok bridge files.

Operates as an autonomous system that loads context from index files, follows goal-driven principles, and uses available agents and skills to execute decisions.

Creates cross-platform agent skills from workflow descriptions, files, URLs, or screenshots. Supports single skills, multi-agent suites, template-based creation, cross-platform export, spec validation, and security auditing of third-party skills before install.

Guides users through selecting and installing skills and rules from the Everything Claude Code project, verifying paths and optionally optimizing installed files.

Sincroniza el registro central de skills con AGENTS.md y registry/AGENT_REGISTRY.md, detectando skills huérfanas o faltantes y auditando el ecosistema.

Captures quick notes as timestamped markdown files, lists them, and promotes them into todos with zero friction.

Build and maintain a custom project skill repertoire. Always use at new project bootstrap and when users ask for new domain capabilities. Detect active assistant folders first, recommend at least 5 skills per search batch, confirm installs, normalize install paths, and regenerate workflow files.

Create, synthesize, and iteratively improve agent skills following the Agent Skills specification. Handles source capture, precision passes, authoring, registration, and validation.

Browse and search all available skills, grouped by workflow stage or activity type

Tracks the dates of skills in the repository, with details maintained in a separate document.

Provides a structured template for creating new Agent Skills, including sections for description, activation criteria, core concepts, practical guidance, and integration notes.

Migrate Claude Code configuration, skills, agents, and MCP servers to Codex project and global files.

Routes general Convex requests to the appropriate project skill, installs official Convex AI guidance, and provides command preflight instructions for Convex CLI operations.

Coordinates a goal lifecycle: define, decompose, activate, track, and execute goals using a set of system skills that write markdown files.

This skill should be used when working with Obsidian files including .md notes with wikilinks/callouts/properties, .base database views with filters/formulas, or .canvas visual diagrams. Routes to specialized sub-skills based on file type and task context. Features self-iterative learning that improves with use.

Imports external plan files into a GSD-structured project, detecting conflicts against existing roadmap, requirements, and locked decisions before writing a validated PLAN.md.

Periodically scans Claude Code configuration for redundant, stale, or orphaned items and walks the user through a confirm-each-deletion cleanup.

Provides end-to-end job search support through five specialized agents: story mining, job recommendation, resume optimization, mock interviews, and skill gap analysis, with scripts to generate resume files in multiple formats.

Tighten annotations in existing Python code with a focused, low-churn workflow based on Honnibal's tighten-types skill. Use to improve changed files, remove avoidable Any, make container and return types precise, or reduce type-checker errors without broad refactoring.

Builds a configurable scraping agent that collects data from APIs, HTML, or RSS, enriches it with Gemini AI scoring, and stores results in Notion, Google Sheets, Supabase, or local files.

Composite skill — land a safe first PR in an unfamiliar repo without rediscovery loops or guessing at conventions. Chains onboard-new-repo (architecture + scripts + CI scan) → context-pack (relevant files + standards + ADRs for the scoped change) → scope-and-execute (narrow the task) → test-driven-development (write failing test first) → pr-to-release (open the PR). Use the first time you contribute to a repo OR when an unfamiliar contributor needs a guided first change.

Machine learning on genomic interval data (BED files) with the geniml Python package — region embeddings (Region2Vec), joint region+metadata embeddings (BEDspace/StarSpace), single-cell ATAC-seq embeddings (scEmbed), consensus peak sets / universes (build-universe), tokenization, BEDshift randomization, and BBClient/BEDbase caching. Use when training or using region/cell embeddings, clustering scATAC-seq, building a tokenization universe from BED collections, or any ML/feature-learning task over genomic regions. NOT for plain interval arithmetic (overlap/intersect/merge counts) — that is gtars, not geniml. Part of the AlterLab Academic Skills suite.

Score any piece of writing for AI-generation tells and produce a weighted 0-100 scorecard with flagged evidence and ranked fixes. Use this skill whenever the user asks "does this sound AI-written", "run this through the AI detector", "score this writing", "check this for AI tells", "would this pass as human", "humanize check", or wants any article, blog post, email, or copy audited for AI patterns before publishing. Also use it when the user pastes or points to text and asks how it reads, whether it's too "ChatGPT-ish", or wants a QA pass on generated content. Works on pasted text, files, and URLs.