Results for “metaskill”

Searches and loads runnable skill guidance from two skill corpora via MCP or CLI, with admin commands for feedback, outcomes, and reindexing.

Manages personal local Agent Skill files by detecting reusable material, searching for similar skills, and creating or updating SKILL.md folders after user approval.

Reduce an oversized SKILL.md by checking whether an existing skill already covers the excess sub-workflow before creating a new child skill only when none fits. Load when a skill exceeds 200 lines and compress-skill determines excess content is genuinely CORE, when the same sub-workflow appears in multiple skills, or when universal-skill-creator or improve-skills identifies a coherent sub-capability. Also triggers on "split this skill", "extract a sub-skill", "this skill is doing too much", or "make this skill reusable". Always checks for an existing home before creating a new skill.

Analyze recent screen activity via a local screenpipe daemon, detect repeated research workflows, and draft new skills or composition recipes for uncovered patterns.

Orchestrates VPN switching, geo-block diagnostics, and leak-aware task resumption by coordinating shell, curl, WireGuard, Tailscale, DNS, IPinfo, and MoltGuard skills.

Creates, reads, deletes, and lists memos on a Memos instance via its API, using a personal access token.

Continuously improves Claude's effectiveness by recognizing patterns, saving memory, creating skills, and refining project knowledge. Use when Claude notices repeated workflows, encounters a problem it solved before, wants to save something for future sessions, needs to create a reusable skill, or when the user asks.

Scaffolds new agent skills conforming to the Agent Skills specification and dotnet/skills repository conventions, generating SKILL.md files with proper frontmatter and directory structures.

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.

Installs the oh-my-gods bundle of 80+ curated agent skills onto a machine in one step, targeting Claude Code, Codex CLI, Antigravity/Gemini, and OpenCode runtimes.

Identify skill coverage gaps and improvement opportunities. Use when analyzing missing skills for a task, creating skill gap issues, evaluating skill effectiveness, or refining skill progressive disclosure.