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Found 27 Skills
Observe user interaction patterns, extract per-session facets, update a dual-matrix soul state, and periodically synthesize a personalized Soul profile for better collaboration.
Dynamic orchestration engine that plans multi-step agent work as DAGs with Mermaid visualization.
Persistent shared memory for AI agents backed by PostgreSQL (fts + pg_trgm, optional pgvector). Includes compaction logging and maintenance scripts.
Canonical skill graph navigation skill for the Skill System.
Unified CLI entry point for the entire Skill System. One command (sk) to operate all skills, run configurable gate validation, and execute discoverable project scripts via CLI-Anything style scan/run workflows.
Generate standalone interactive dashboard HTML from skill graph, TKT, memory, and roadmap data.
Configurable gate validation skill. Use when running repository-local gate rules from gate_rules.yaml (command/structural/eda-contract), checking legacy experiment registry safety, or enforcing close-gate fail-closed validation.
Canonical ticket lifecycle engine for multi-agent orchestration. Two backends: (1) filesystem YAML bundles for project-level work management (roadmap → bundle → tickets → review), (2) DB-backed durable tickets for session-level claim/block/close lifecycle. This skill is the single source of truth for all ticket operations.
Exploratory Data Analysis skill for CSV and parquet datasets with deterministic profiling, drift/anomaly scans, contract generation and validation, and optional memory writeback into skill-system-memory. The implementation is Polars-first (lazy scan for large files and early `--sample` head), includes high-cardinality guards for profile/importance/contract flows, and supports categorical correlation with Cramer's V. Use when building or reviewing tabular fraud/risk/data-quality workflows, profiling new datasets, checking leakage or drift, or saving/validating data contracts.
BDD-style behavior specification engine for the skill system. Use when: (1) defining a new skill's behavior before implementation, (2) validating a spec against the schema, (3) generating a behavior contract (Mermaid DAG) from a spec, (4) running structural acceptance tests against a built skill. Workflow: Spec → Test → Develop → Contract.
Postgres-backed observability and policy store for the skill system. Provides tables for policy profiles (effect allowlists), skill execution runs, and step-level events. Use when setting up the skill system database or querying execution history.
Agent behavioral profiles that standardize how different LLMs behave. Load this skill when you need to: (1) adopt a specific behavioral mode for a task, (2) switch between creative/strict/talkative modes, (3) ensure consistent behavior across different models. Profiles define personality, decision heuristics, communication style, and quality standards.