X Builder Lab
Official@xbuilderlab
a lab for builders who like turning ideas into repos
Agent Skills by X Builder Lab
Showing 16 vetted skills indexed across 1 GitHub repositories.
XBuilderLABcheat-on-content
Calibrates content performance predictions through blind scoring, retrospectives, and evolving rubrics.
XBuilderLABcheat-shoot
Track video shooting progress and compare scripts for consistency.
XBuilderLABcheat-persona
Generate audience personas by analyzing review data in prediction files.
XBuilderLABcheat-recommend
Sort and recommend content topics from a candidates.md file by rubric.
XBuilderLABcheat-predict
Generate immutable prediction logs for content success using AI analytics.
XBuilderLABcheat-migrate
Migrate .cheat-state.json schema versions with dry-run previews.
XBuilderLABcheat-publish
Update metadata and state files with published content details.
XBuilderLABcheat-seed
Analyze post performance and predict virality from historical data.
XBuilderLABcheat-score
Score written content against predefined rubric dimensions and calculate a composite score.
XBuilderLABcheat-trends
Fetch and score trending topics from specified sources into candidates.md.
XBuilderLABcheat-init
Guide content creators through a structured onboarding process with framework setup.
XBuilderLABcheat-retro
Analyze content performance against predictions and generate strategy feedback.
XBuilderLABcheat-status
Read .cheat-state.json to display progress metrics and calibration actions.
XBuilderLABcheat-score-blind
Score scripts blindly using rubric notes without user context or past data.
XBuilderLABcheat-learn-from
Import and analyze social media content to extract patterns and generate a content creation rubric.
XBuilderLABcheat-bump
Propose and execute rubric or bucket upgrades using Python scripts.
Frequently Asked Questions About X Builder Lab
FAQPage SchemaWhat specific tasks can I perform using X Builder Lab?βΌ
You can track video production progress, score scripts against custom rubrics, predict content virality from historical data, and manage metadata state files. These capabilities enable creators to maintain consistency across content lifecycles while utilizing data-driven insights to refine editorial strategies and audience targeting.
Who is the target persona for these content management capabilities?βΌ
These capabilities are designed for content creators, editorial leads, and digital media strategists who require structured, rubric-based evaluation of their output. It serves professionals who need to bridge the gap between raw social media performance data and actionable, repeatable content creation frameworks.
What are the prerequisites for implementing these content tracking functions?βΌ
Implementation requires a local environment capable of executing the provided repository logic and maintaining .cheat-state.json files. Users must organize content sources into candidates.md files and define rubric dimensions to enable the scoring, migration, and performance analysis functions provided by the lab.