Results for “structure”

Prompt engineering standards and context engineering principles for AI agents based on Anthropic best practices. Covers clarity, structure, progressive discovery, and optimization for signal-to-noise ratio.

Map a current event below the surface into patterns, structures, and mental models. Use when a class or team needs systemic understanding before action.

Root cause analysis skill — diagnose bugs systematically before fixing. Iron Law: no fix without a confirmed hypothesis. Uses scope lock, 3-strike escalation, and structured debug reports.

Convene a four-voice council for ambiguous decisions, tradeoffs, and go/no-go calls. Use when multiple valid paths exist and you need structured disagreement before choosing.

Design and improve revenue operations, lead lifecycle rules, scoring, routing, handoffs, and CRM process automation. Use when marketing, sales, and customer success workflows need clearer operational structure.

Design and improve revenue operations, lead lifecycle rules, scoring, routing, handoffs, and CRM process automation. Use when marketing, sales, and customer success workflows need clearer operational structure.

Design and improve revenue operations, lead lifecycle rules, scoring, routing, handoffs, and CRM process automation. Use when marketing, sales, and customer success workflows need clearer operational structure.

Convene a four-voice council for ambiguous decisions, tradeoffs, and go/no-go calls. Use when multiple valid paths exist and you need structured disagreement before choosing.

Search Google via Bright Data SERP API. Returns structured JSON results with title, link, and description. Requires BRIGHTDATA_API_KEY and BRIGHTDATA_UNLOCKER_ZONE environment variables.

Design and improve revenue operations, lead lifecycle rules, scoring, routing, handoffs, and CRM process automation. Use when marketing, sales, and customer success workflows need clearer operational structure.

Design and improve revenue operations, lead lifecycle rules, scoring, routing, handoffs, and CRM process automation. Use when marketing, sales, and customer success workflows need clearer operational structure.

Algorithm-first discipline: state Big-O, data structure, and algorithm family BEFORE writing loops, queries, or recursion. Catches O(n^2), N+1, and brute-force defaults.

Safety guardrails for autonomous agents. Intercepts destructive actions and vets skills, requires structured approval before irreversible operations, with CLI-backed cryptographic audit trails and policy enforcement.

Design and improve revenue operations, lead lifecycle rules, scoring, routing, handoffs, and CRM process automation. Use when marketing, sales, and customer success workflows need clearer operational structure.

Algorithm-first discipline: state Big-O, data structure, and algorithm family BEFORE writing loops, queries, or recursion. Catches O(n^2), N+1, and brute-force defaults.

Builds Quarterly Business Review decks for active clients. Pulls metrics, generates structured QBR with wins, challenges, roadmap, and recommendations. Uses crm-writer to read client data.

Create new agent skills with proper structure, progressive disclosure, and bundled resources. Use when user wants to create, write, or build a new skill.

A fast Rust-based headless browser automation CLI with Node.js fallback that enables AI agents to navigate, click, type, and snapshot pages via structured commands.

Design and improve revenue operations, lead lifecycle rules, scoring, routing, handoffs, and CRM process automation. Use when marketing, sales, and customer success workflows need clearer operational structure.

Algorithm-first discipline: state Big-O, data structure, and algorithm family BEFORE writing loops, queries, or recursion. Catches O(n^2), N+1, and brute-force defaults.

Design and improve revenue operations, lead lifecycle rules, scoring, routing, handoffs, and CRM process automation. Use when marketing, sales, and customer success workflows need clearer operational structure.

Algorithm-first discipline: state Big-O, data structure, and algorithm family BEFORE writing loops, queries, or recursion. Catches O(n^2), N+1, and brute-force defaults.

Append structured execution traces across operational, cognitive, and contextual surfaces with minimal overhead. Load when inspecting agent runs, logging tool calls and observations, enabling post-run debugging, or pairing with structured-planning step IDs. Also triggers on "trace this run", "log execution", "agent observability", "run log", or when fault-localize needs evidence. Default-on during multi-step plans. Traces live at .agent-loom/traces/ — git-ignored by default.

Holarchic reasoning framework implementing λ-calculus over simplicial complexes. Entities (ο) transform through operations (λ) toward terminals (τ) via the universal form λο.τ. Persistent homology captures multi-scale structure; sheaf theory ensures local-to-global consistency. Use when knowledge requires: (1) homoiconic self-reference where structure mirrors content, (2) scale-invariant holonic decomposition, (3) topological invariants preserved across transformations, or (4) formal Lex-style axiom systems over property graphs.

Generate operational runbooks from a service name — deployment, incident response, maintenance, and rollback workflows. Templated structure customizable per environment.

Provides a structured pre-launch checklist, staged rollout plan, monitoring setup, and rollback strategy for safe production deployments.

Design pricing, packaging, and monetization strategies that capture value and align with customer willingness to pay.

Guide users through learning new skills or concepts with structured lessons, reference materials, and progress tracking within a dedicated workspace.

Answers questions about a codebase by searching and reading source files, providing answers with inline citations and structured responses.

Search Notion pages, synthesize findings, and publish structured briefs, summaries, comparisons, or reports with citations and source links.

Design content models for AEM Edge Delivery Services blocks that are easy for authors to work with.

Generate an llms.txt file from repository structure, following the llms.txt specification to help LLMs navigate and understand the project.

Build automated evaluation pipelines for Python LLM applications using real LLM calls and structured test datasets.

Analyzes article structure, identifies positions requiring visual aids, and generates illustrations with consistent type, style, and palette.

Organize, query, validate, and convert neuroscience and biomedical data using the Brain Imaging Data Structure (BIDS) standard.

Design the structure, hierarchy, and navigation model for a product's content and features.