Skill UI

The Recruitment Pipeline skill helps businesses track the full hiring process: candidates, positions, stages, sources, applied and interview dates, interview scores, notice periods, and offers. It provides minimal recommendations first, then builds artifacts like CSV, SQL, JSON, or Notion mappings on request, ideal for SME hiring tracking.

Provides an auditable framework for conditional asset locking, timelock boundary verifications, and multi-signature release triggers on Stellar, with standardized data models and verification schemas for integration into CI pipelines.

Define and audit Stellar multi-signature signer weights, master key locks, and quorum thresholds. Exports CSV, SQL DDL, and JSON Schema registers for validation pipelines.

Registers zero-knowledge SNARK proof verification keys, circuit complexity constraints, and nullifier tracking for double-spending prevention in privacy systems. Provides standardized field schemas, SQL DDL, JSON validation, and best practices for auditable cryptographic verification across distributed applications and smart contracts.

Runs ECC's engineering process retroactively on existing projects. Audits current state, reconstructs past decisions, and produces a four-axis gap register (thinking, coding practice, missing features, technology selection). Executes fixes forward through the plan-TDD-review pipeline. Ideal for mid-project adoption, inheriting codebases, or identifying what was missed.

Prove data/AI workflows end-to-end on tiny samples, find cost-saving wins, then scale within budget using cost-gated steps. Ideal for multi-stage pipelines involving paid compute like LLM tokens or warehouse credits.

This skill enables splitting extracted text into optimized chunks for LLM context windows or RAG ingestion pipelines. It supports configurable chunk sizes, overlap settings, and various chunker types including text, markdown, YAML, and semantic splitting. Users can utilize token-based sizing for specific models or standalone commands for pre-existing text. Ideal for preparing documents for vector stores or summarization tasks.

Learn how to configure xberg's --format and --content-format flags to optimize document extraction for different consumers. Covers LLM context, RAG indexing, JSON parsing, and token reduction strategies for efficient data pipelines.

Manage multi-agent pipelines on Grok Build using Ruflo-style team bus and MCP tools. Ideal for coordinating feature development workflows like architect-to-reviewer chains or replacing Claude SendMessage. Includes CLI fallback for team creation, planning, and subagent spawning with isolation controls.

Runs reproducible CellProfiler pipelines for nuclear segmentation, cell counting, and per-object fluorescence measurements on 2D microscopy TIFF images, including batch execution, overlays, and QC.

Answer factual and how-to questions about Nightingale (n9e) platform including UI navigation, business groups, subscription rules, notification pipeline, categraf plugin fields, metric names, config syntax, and source code implementation details. Uses official documentation and embedded source code corpus as evidence sources. Every concrete fact must be verified from docs or code.