Explorar skills

Background knowledge about Loops, the federated short-form video sharing platform (TikTok/Vine alternative for the Fediverse). Covers the key federation design decision of using Note objects with video attachments instead of Video objects for maximum cross-platform compatibility, the instance actor and shared inbox implementation, HTTP Signatures with SHA-256 digests, WebFinger discovery, the grap

Background knowledge about NodeBB, the modern open-source forum software built on Node.js with native ActivityPub federation (core feature since v4.0.0, January 2025). Covers the three actor types (Person user at /u/username, Group category at /category/<cid> implementing FEP-1b12 Group Federation with Announce relay pattern, Application instance actor), the two-level content hierarchy (category a

Background knowledge about Mbin, the federated content aggregator and microblogging platform (active community fork of Kbin). Covers the hybrid dual content model (threads as Page + microblogs as Note in one platform), magazines as Group actors with Announce relay, the three actor types (Application instance actor at /i/actor, Person user at /u/username, Group magazine at /m/name), the full activi

Background knowledge about how the Like activity works on the Fediverse and in ActivityPub, including the Like/Undo{Like} flow, the `likes` and `liked` collections, delivery model, the Dislike type (exists in ActivityStreams but is unused in ActivityPub), emoji reactions (EmojiReact / FEP-c0e0 vs the Misskey _misskey_reaction approach), stale like counts on remote posts, and implementation differe

Auto-activate for Google ADK, LlmAgent, Runner, SQLSpecSessionService, Vertex AI, agent chat endpoints, streaming AI responses, tool-calling endpoints, or Litestar-hosted model workflows. Use when serving AI agents or model-backed workflows from Litestar. Not for offline ML training or non-Litestar agent servers.

Background knowledge about Kbin and its active community fork Mbin, the decentralized content aggregator and microblogging platform for the Fediverse (Reddit + Mastodon hybrid). Covers the dual content model (threads/link aggregation via Page objects + microblogging via Note objects in a single platform), magazines as Group actors with Announce relay (same pattern as Lemmy communities), the three

Background knowledge about PeerTube, the decentralized video streaming platform. Load when the user asks about implementing a PeerTube- compatible ActivityPub server, federating with PeerTube instances, PeerTube's two-tier actor model (Person actors own Group channel actors, Application instance actor), PeerTube's attributedTo requirement (videos must reference a Group actor which references an ow

Background knowledge about how relays work on the Fediverse, including at the ActivityPub wire-format level. Load when the user asks about implementing a relay, subscribing to a relay, the relay subscription handshake (Follow/Accept to the Public collection), the two relay protocols (Mastodon relay protocol vs LitePub/Pleroma relay protocol), the /inbox vs /actor subscription endpoints, how relays

Evaluates LLMs across 60+ academic benchmarks (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag). Use when benchmarking model quality, comparing models, reporting academic results, or tracking training progress. Industry standard used by EleutherAI, HuggingFace, and major labs. Supports HuggingFace, vLLM, APIs.

Provides guidance for LLM post-training with RL using slime, a Megatron+SGLang framework. Use when training GLM models, implementing custom data generation workflows, or needing tight Megatron-LM integration for RL scaling.

Read-only health check for an existing Peec AI project. In one pass produces (1) a setup-quality audit (red flags from the structural setup — wrong competitors, funnel gaps, taxonomy issues), (2) a brand-performance snapshot (visibility per stage / engine, hero prompts winning vs losing, source diversity, competitor delta), and (3) a priority-ranked list of 5–8 concrete improvements drawn from Pee

Process new Obsidian Web Clipper notes into the vault's AI-maintained Wiki. Use this skill whenever the user asks to process notes, process clippings, ingest clippings, build the wiki, update the wiki from new notes, run obsidian-process-notes, run obsidian-build-wiki, or maintain the AI wiki layer. It reads new files from `03 Resources/Clippings`, creates or updates German LLM-generated wiki page