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Documentation AI Skills

Discover Documentation skills, compare source repositories and read the instructions for each workflow.

Categories: Documentation

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GitHub

train-debug

rHedBull

Interactive diagnostic workflow for training problems. Use when training is failing, loss is stuck, gradients explode, NaN occurs, or convergence is poor.

GitHub

train

rHedBull

Execute a neural network training run with mandatory monitoring and best-practice defaults. Use when user wants to train a model, start training, or run a training job.

GitHub

experiment-plan

rHedBull

Design rigorous experiments before running them. Use when planning experiments, testing hypotheses, or designing ablation studies.

GitHub

monitor

rHedBull

View training progress in real-time. Use when checking on running training, viewing live metrics, or opening dashboards.

GitHub

run-validation

rHedBull

Run model validation on a dataset. Use when testing model performance, comparing checkpoints, or running final evaluation.

GitHub

interpret-curves

rHedBull

Analyze training logs and explain what's happening. Use when reviewing training runs, understanding loss curves, or diagnosing completed training.

GitHub

scaling-analysis

rHedBull

Run scaling experiments to understand model/data/compute relationships. Use when investigating scaling laws, compute-optimal training, or model size decisions.

GitHub

typescript

Pallepadehat

TypeScript strict patterns and best practices. Trigger: When implementing or refactoring TypeScript in .ts/.tsx (types, interfaces, generics, const maps, type guards, removing any, tightening unknown).

GitHub

ai-sdk-5

Pallepadehat

Vercel AI SDK 5 patterns. Trigger: When building AI features with AI SDK v5 (chat, streaming, tools/function calling, UIMessage parts), including migration from v4.

GitHub

claude-agent-sdk

tkwn2080

Build production AI agents with the Claude Agent SDK. Use when working with: (1) Creating autonomous agents with query() or ClaudeSDKClient, (2) Defining custom tools via in-process MCP servers, (3) Implementin...

GitHub

gtm-ops

accoladians

Trigger when user mentions GTM, Tag Manager, Enhanced Conversions, or publishing. Guide through GTM operations.

GitHub

pmax-optimization

accoladians

Activate when user asks about Performance Max campaigns, search themes, asset groups, or PMax optimization. Provides guidance on managing PMax campaigns via xwander-ads plugin.

GitHub

gaql-queries

accoladians

Activate when user needs custom Google Ads data analysis, advanced reporting, or specific metric queries. Provides GAQL query building and execution guidance.

GitHub

conversion-tracking

accoladians

Activate when user asks about conversion tracking, HubSpot sync, offline conversions, or attribution. Provides guidance on conversion setup and troubleshooting.

GitHub

obsidian-todorust

mimiqdev

Todoist data retrieval for Obsidian using todorust CLI. Fetch tasks and projects in ready-to-use Markdown formats. Use when skills need to: (1) Get completed tasks for daily/weekly reports, (2) Query by project...

GitHub

ontology-phase-1-ingest

a4b-corporation

Phase 1 of Ontology Builder Pipeline. Ingests and catalogs all input materials from _input/ folder. Use when starting ontology building process or when processing new input documents for domain analysis.

GitHub

ontology-phase-4-generate

a4b-corporation

Phase 4 of Ontology Builder Pipeline. Generates final Ontology artifacts (entity definitions, workflow catalog, concept guides) from DRD. Use after Phase 3 DRD is complete.

GitHub

ontology-phase-2-analyze

a4b-corporation

Phase 2 of Ontology Builder Pipeline. AI acts as domain SME to analyze raw inputs, extract entities/workflows/rules, fill knowledge gaps using market expertise. Use after Phase 1 ingestion is complete.

GitHub

ontology-phase-3-synthesize

a4b-corporation

Phase 3 of Ontology Builder Pipeline. Synthesizes analysis results into complete Domain Requirement Document (DRD). Use after Phase 2 analysis is complete to generate structured DRD.

The default order uses GitHub repository stars from our indexed data. Stars belong to the source repository and may be shared by multiple skills. They indicate repository interest, not skill installs or an independent quality score.

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