Break the monolithic forge-interaction skill into focused, single-responsibility skills for better predictability and maintainability: - Add forge-github skill for GitHub-specific interactions using `gh` CLI - Add forge-gitea skill for Gitea-specific interactions using `tea` CLI - Convert forge-interaction into a router that delegates to specialized skills - Add forge-router skill with model invocation disabled for guidance only - Move all forge-related skills to common/deprecated directory Each specialized skill now contains its own complete decision tree, CLI usage patterns, and authentication checks for its respective forge platform.
Common Skills
Skills that work in all CLI agents.
User-invoked
- conversation-summary — Summarize the current AI conversation into a new Obsidian markdown note and matching transcript file.
- crit — Brainstorm with AI using the CRIT framework to generate and evaluate ideas.
- grill-me — A relentless interview to sharpen a plan or design.
- handoff — Compact the current conversation into a handoff document for another agent to pick up.
- knowledge-gardener — Run vault-aware semantic search, synthesis, note creation, linking, and Zettelkasten workflows for this Obsidian vault.
- pkm-curation — Curate an Obsidian-style personal knowledge vault.
- research-vault — Research a topic through a one-question-at-a-time learning conversation and save a linked Obsidian research packet.
- writing-great-skills — Reference for writing and editing skills well — the vocabulary and principles that make a skill predictable.
Model-invoked
- audio-product-dsp — Dispatch audio product DSP hardware/software engineering requests to the best specialist workflow with measurable product-focused outputs.
- forge-interaction — Work safely with GitHub and Gitea repositories, issues, pull requests, releases, and remote forge state.
- forge-preferences — Apply Steve's personal or project-specific GitHub/Gitea forge preferences.
- grilling — Interview the user relentlessly about a plan or design.
- project-context-pack — Build and refresh a bounded repo context memory file so agents use disciplined search instead of repeated browsing.
- research-engineering — Route DSP hardware and software research-engineering requests to the best specialist workflow and return a unified, decision-ready output.