agentic-metacognition

Create, load, merge, and distribute persistent artifact plugin bundles for activation steering. Use this skill when the user wants to manage steering artifacts, create distributable plugin packs, load model bundles, merge multiple plugins, write new artifact directories, or work with the plugin directory tree. Also trigger when the user mentions artifact plugins, plugin bundles, plugin manifests, controllers.json, activations.json, or the artifacts/ directory layout.

Define, discover, and manage cognitive feature specifications and steering vectors for LLM activation steering. Use this skill when the user wants to create feature specs, define extraction examples with positive/negative labels, set evaluation criteria, discover feature vectors from specs, persist discovered vectors, work with the standard feature catalog, or manage the feature lifecycle. Also trigger when the user mentions feature extraction, feature catalog, feature specs, cognitive features, or interaction features.

Use when creating, compiling, validating, running, or debugging GitHub Agentic Workflows with the gh-aw CLI.

Build and query Neo4j-backed reasoning trajectory graphs for the hybrid meta-cognition agent. Use this skill when the user wants to persist task plans, subgoals, constraints, run states, and verifier outcomes in a Neo4j graph, retrieve evidence paths with PathRAG, or store drift-correction records. Also trigger when the user mentions Neo4j, GraphRAG, PathRAG, reasoning trajectories, graph store, evidence paths, or task plan graphs — even if they don't say "graphrag" explicitly.

Build and run a hybrid planner/retriever/steered-executor/verifier meta-cognition agent loop with persistent steering memory. Use this skill when the user wants to create an agent that plans tasks, retrieves context, applies activation steering, verifies results, and writes back to memory. Also use when the user mentions HybridMetaCognitionAgent, SteeredExecutor, planner/verifier loops, steering controllers, agent runs, or activation traces — even if they don't say "hybrid agent" explicitly.

Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.

Run activation-steering feature discovery for a Hugging Face model. Use this skill whenever the user says /steering, wants to steer a model, extract a cognitive feature, generate steering vectors, run feature discovery, or produce steering artifacts. Specify a model (default: gpt2) and an optional feature name. If a feature is supplied, generate inputs, expected outputs, then run an extraction pass and output artifacts. If no feature is supplied, auto-pick one that hasn't already been extracted. If no test data is supplied, generate synthetic examples; otherwise use the user's data and fill in whatever's missing.