Skill UI

Transforms a prioritized product roadmap into a compelling, non-technical narrative that explains the 'why' and 'how' of product evolution. Ideal for presenting strategy to executive leadership, boards, or all-hands meetings, ensuring every initiative is tied to measurable OKRs and anticipating stakeholder questions. Outputs a complete story arc, executive summary, and scope definition.

This skill helps diagnose, repair, and prevent complex Drizzle Kit database migration conflicts. It addresses issues arising from merging branches, rebasing, or PR updates involving generated SQL, snapshots, and journals. Use it to ensure stable schema evolution in multi-developer teams and harden CI/CD workflows.

A robust protocol designed for multi-agent collaboration on long-running projects. It simulates a self-optimizing city where diverse AI agents (citizens) interact using decentralized markdown files (The Register, Contracts, Chronicle). This framework enables cross-vendor optimization, dynamic work routing, and constitutional evolution without requiring a centralized server or specific runtime environment.

A utility for scaffolding and verifying fixed evaluation corpora (bench suites). It generates deterministic test suites from existing repositories, decoupling the evaluation process from the repo's natural test suite. This ensures consistent fitness scoring when running evolution pipelines across different commits or forks.

Run @metaharness/darwin evolve to mutate a harness's seven policy surfaces, sandbox-score each variant, and promote only measured wins. Use when readiness scores are flat and you need empirical configuration discovery.

Inspect and audit GEPA genomes via the @metaharness/darwin/gepa library. Load, validate, render system prompts, and analyze failure modes in run transcripts. Ideal for adopting evolved policies, CI gates, and debugging evolution runs.

SkillOpt-Sleep enables an AI agent to undergo a simulated "sleep cycle" for continuous, measurable self-improvement. It harvests past sessions, mines recurring tasks, and re-runs them offline. It then consolidates findings into the agent's memory and skills, ensuring only validated improvements are adopted. This process maximizes long-term competence from short-term interactions.

This skill provides comprehensive guidance for authors submitting to Accounts of Chemical Research (ACS). It helps researchers understand the journal's unique niche: publishing concise, first-person, narrative accounts of a research program's evolution, rather than comprehensive field reviews or primary data reports. It serves as a fit-checking tool, guiding manuscript framing, required evidence, and house style.

A comprehensive guide for authors targeting the Annual Review of Ecology, Evolution, and Systematics (AREES). It details the journal's strict requirements for authoritative syntheses, covering scope, conceptual structure, methodology, and house style. Use this tool when proposing or writing high-level review articles that define future research directions in Ecology, Evolution, and Systematics.

A comprehensive guide for authors aiming to submit manuscripts to Current Biology. This skill helps assess the manuscript's scope, novelty, and completeness against the journal's high standard. It covers journal positioning, structure requirements (STAR Methods, figure quality), and disciplinary fit across fields like ecology, neuroscience, and evolution, guiding authors on how to frame their work for a broad scientific readership.

A comprehensive guide for authors submitting mechanistic studies to Functional Ecology. It details the journal's focus on linking organism function (physiology, traits, life history) to ecological and evolutionary consequences. This skill outlines the required functional mechanism, rigorous methodological standards, scope relevance (e.g., ecophysiology, trait-based ecology), and structural requirements for high-impact submissions.

A comprehensive methodology guide for designing and auditing empirical studies in software engineering, specifically focused on maintenance, evolution, and change impact. It outlines best practices for ensuring evidence is proportional to the claim, managing data provenance (SHAs, criteria), handling statistical confounds (survivorship, effect sizes), and integrating rigorous LLM evaluation protocols.