Observed Signal · Oct 5, 2026 · Market Signal · Source: Legora · Impact: 2/5
Introducing Skills
Teach the Agent how you work. Skills let legal teams encode their standards, processes and preferences inside the Legora aOS™.
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1 Entity mappedRelated Market Signals & Shifts
Recent verified developments and strategic activity across this market segment.
AgentSkills: Teach AI Agents How to Execute Tasks
The article describes a gap in many LLM-based agent applications: agents often know what to do but not how to do it reliably. It introduces AgentSkills (aka Procedure Skills) — self-contained, structured playbooks (commonly formatted as SKILL.md) that bundle YAML frontmatter, step-by-step execution instructions, small automation scripts, domain resources, and output templates. The author explains why embedding full procedures in large system prompts fails (fragility, token waste, inconsistency) and advocates progressive disclosure: a discovery phase that loads only skill names/descriptions and an activation phase that loads full skill assets when a match occurs. The piece gives design principles for effective skills (imperative language, explicit failure states, small composable units) and explains when skills materially improve agent reliability and cost-efficiency. Published May 6, 2026 by Sreeni Ramadorai on DEV Community.
Open Skills Library: Making Agent Workflows Portable
A Substack essay argues that AI agent 'skills'—the procedural knowledge encoded as prompts, runbooks, SKILL.md files and configs—are becoming trapped inside vendor tools (Claude, Codex, Cursor, ChatGPT), creating repeated rebuild costs when teams switch platforms. The author launches "Open Skills," a public library of agent skills and runbooks designed to be visible, movable, inspectable and installable across tools. The piece explains how skills differ from memory and prompts, lists four failure modes that create long-term debt, provides a "work package" checklist to prove ownership of a skill, and demonstrates rebuilding a support-billing workflow that travels across Claude Code, Codex and Cursor. The author frames skill portability as practical work for 2026 that avoids new subscriptions by making existing workflows portable.
AI Instruction Split: AGENTS.md, SKILL.md, DESIGN.md
The article describes a growing three-layer standard for instructing AI agents: AGENTS.md for overall agent behavior and boundaries, SKILL.md for reusable task procedures (used by Anthropic's Claude Skills and the Agent Skills standard), and DESIGN.md — a Google Labs design-spec format released in April 2026 that combines machine-readable design tokens (YAML) with human-readable intent and ships with a CLI validator (npx @google/design.md lint). The author argues these formats separate verifiable rules (tokens, audits, structural checks) from judgment-based guidance (tone, stance), and situates the split alongside Spec-Driven Development (SDD) workflows (Kiro, GitHub Spec Kit). The three-layer approach is presented as complementary to SDD and intended for incremental adoption where verification adds value.
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