data-layer
This skill provides patterns for working with the data-layer module. Use when creating/editing files in src/data-layer/, src/lib/data/, or adding new data sources.
This skill provides patterns for working with the data-layer module. Use when creating/editing files in src/data-layer/, src/lib/data/, or adding new data sources.
Break up a large PR into vertical feature slices delivered incrementally via Graphite stacked PRs. All verticals share a single feature flag so the entire feature ships atomically. Use when: splitting a large PR, breaking up a diff, vertical slicing, incremental delivery, phased rollout, or when a PR is too large to review.
Guide for AI agents running in the isolated agent-harness environment. Use when you need to discover your agent ID, find your ports, manage your stack with agent-cli.sh, run verification, or understand the multi-agent development setup.
Keep anomalib model READMEs, docs pages, image assets, and benchmark/result references in sync
Scale Daft workflows to distributed Ray clusters. Invoke when optimizing performance or handling large data.
Batch-generate images via OpenAI Images API. Random prompt sampler + `index.html` gallery.
Transcribe audio via OpenAI Audio Transcriptions API (Whisper).
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Use CodexBar CLI local cost usage to summarize per-model usage for Codex or Claude, including the current (most recent) model or a full model breakdown. Trigger when asked for model-level usage/cost data from codexbar, or when you need a scriptable per-model summary from codexbar cost JSON.
Annual security report aggregation and analysis. USE WHEN annual reports, security reports, threat reports, industry reports, update reports, analyze reports, vendor reports, threat landscape.
Annual security report aggregation and analysis. USE WHEN annual reports, security reports, threat reports, industry reports, update reports, analyze reports, vendor reports, threat landscape.
Generate a professional PDF report from GEO audit data using ReportLab. Creates a polished, client-ready PDF with score gauges, bar charts, platform readiness visualizations, color-coded tables, and prioritized action plans.
Schema.org structured data audit and generation optimized for AI discoverability — detect, validate, and generate JSON-LD markup
Platform-specific AI search optimization — audit and optimize for Google AI Overviews, ChatGPT, Perplexity, Gemini, and Bing Copilot individually
One-time setup that gathers design context for your project and saves it to your AI config file. Run once to establish persistent design guidelines.
Multi-agent pipeline orchestrator that plans and dispatches parallel development tasks to worktree agents. Reads project context, configures task directories with PRDs and jsonl context files, and launches isolated coding agents. Use when multiple independent features need parallel development, orchestrating worktree agents, or managing multi-agent coding pipelines.
Post-implementation verification across multiple code dimensions: cross-layer data flow, code reuse analysis, import path validation, and same-layer consistency checks. Identifies missed update sites, type mismatches, and duplicated constants. Use when changes span 3+ architectural layers, after modifying shared constants or configs, after batch file modifications, or when creating new utility functions.
Guide for adding a new model to the Archon engine. Use when user wants to add support for a new HuggingFace model architecture in ArchonEngine.
Guide for adding a new model to the Archon engine. Use when user wants to add support for a new HuggingFace model architecture in ArchonEngine.