Skill UI

This utility converts a single, high-level objective into a comprehensive, step-by-step build plan suitable for complex, multi-agent, or multi-session engineering projects. It automatically handles dependency mapping, detects parallel steps, and structures each step with self-contained context briefs, ensuring any new agent can execute it without prior knowledge. Ideal for large-scale refactoring or feature development spanning multiple PRs.

A deterministic 'Stop hook' designed to enforce session hygiene for AI agents. It checks machine-verifiable facts such as file modification timestamps of learning logs, disk space, and predefined rationalization patterns. This gate ensures that complex tasks lead to captured knowledge, acting as a mechanical safeguard complementing AI self-auditing.

This tool is designed to analyze source code by extracting core entities (classes, functions, modules, types) and mapping their complex relationships (imports, inheritance, calls, dependencies). It constructs a comprehensive Knowledge Graph (KG) in AgentDB. This is essential for detailed dependency analysis, understanding large codebase structures, and performing accurate impact assessments.

Tree Ring Memory is a local-first, framework-agnostic layer for managing durable knowledge in AI agents. It provides structured methods for recalling past decisions, recording evidence, auditing project state, and deliberately forgetting information, ensuring the core project memory remains clean and actionable without relying on raw conversation transcripts.

This tool performs a comprehensive audit of the project's stored auto-memory. It analyzes learned knowledge to identify actionable patterns for promotion to core rules, flag stale or outdated entries, and suggest opportunities for consolidating redundant knowledge. Use it after major development phases or when the memory knowledge base becomes crowded to maintain system knowledge quality.

This guide helps researchers determine the suitability of a manuscript for the ACM International Conference on Information and Knowledge Management (CIKM). It provides detailed advice on conference positioning, topic fit (IR, KM, DB, KG), evidence standards, and necessary re-framing techniques to maximize acceptance chances in top computer science venues.

This guide helps authors strategically position their academic work within the existing body of sociological literature. It teaches how to frame a paper not merely as filling a knowledge gap, but as resolving a core theoretical tension or participating in a live, generalist debate. It is essential for improving submissions to highly selective, theory-driven journals like AJS.

This guide helps researchers determine if an interesting phenomenon or trend constitutes a genuine theoretical puzzle suitable for the Academy of Management Review (AMR). It guides users to distinguish between a simple topic, an empirical test, and a deep theoretical contradiction that requires building new knowledge-based claims.

This guide provides rigorous standards for crafting academic literature reviews, particularly for specialized journals in economics. It instructs authors to move beyond simple citation listing and instead structure the review around identified research gaps. The required structure emphasizes a clear progression from 'Current Status' to 'Deficiencies' and finally to 'Novel Positioning,' ensuring the paper's marginal contribution is clearly derived from a knowledge gap.

This guide provides expert strategies for crafting the 'Related Work' section in NLP research papers, ensuring it moves beyond a simple literature list. It emphasizes structuring the section as a contrastive argument that clearly positions the contribution, addresses citation specifics (e.g., ACL Anthology vs. arXiv), acknowledges concurrent work, and ensures coverage of diverse and recent venues like LREC-COLING and TACL.

This comprehensive guide details the stringent process of preparing an accepted scholarly paper for publication in LIPIcs proceedings. It covers mandatory steps including converting to the specific `lipics-v2021` class, systematic de-anonymization (restoring authors, ORCIDs, and acknowledgements), completing rich metadata (ACM CCS, keywords, funding), and reconciling the final body text with the public full version. Essential reading for ensuring smooth production.

This guide provides a structured methodology for authors to articulate the core contribution of their Information Systems (IS) research for top-tier academic journals, such as MIS Quarterly. It ensures that the paper explicitly states the new knowledge produced (e.g., behavioral mechanisms, design principles, economic effects), justifies the manuscript's length, and rigorously connects theoretical advances with practical and societal implications, thereby avoiding claims of being merely 'incremental' or 'technical' exercises.