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teleo-codex/inbox/archive/2026-03-01-anthropic-agent-skills-open-standard.md
m3taversal 1de60685be theseus: add 5 Nous Research source archives for codex ingestion 
- GEPA self-evolution system (trace-based evolutionary prompt optimization)
- DeMo: Decoupled Momentum Optimization (Peng, Kingma et al. — 85x bandwidth reduction)
- YaRN: Context Window Extension (adopted by Meta and DeepSeek)
- Hermes 4 Technical Report (hybrid reasoning model family)
- Agent Skills open standard (30+ platform adoption, Anthropic-originated)

Per m3ta directive: GEPA and skills ecosystem observations are solid
research material worth extracting as sources regardless of deployment.

Pentagon-Agent: Theseus <46864dd4-da71-4719-a1b4-68f7c55854d3>
2026-04-07 14:56:03 +00:00

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type title author url date domain intake_tier rationale proposed_by format status processed_by processed_date claims_extracted enrichments tags
source Agent Skills: An Open Standard for Giving Agents New Capabilities Anthropic (originator), AgentSkills community https://agentskills.io 2026-03-01 ai-alignment research-task Agent Skills is the open standard for SKILL.md files, adopted by 30+ platforms including Claude Code, Cursor, GitHub Copilot, VS Code, OpenAI Codex, Hermes Agent, and JetBrains Junie. This is the primary evidence for our 'Agent Skills as industrial codification' claim — the largest real-world instance of procedural knowledge standardization for AI agents. theseus whitepaper processed theseus 2026-04-07
agent skills as industrial codification pattern mirrors historical skill decomposition from craft guilds through scientific management to algorithmic management
agent-skills
skill-md
open-standard
anthropic
codification
interoperability

Agent Skills: Open Standard Overview

Agent Skills is an open format for giving AI agents new capabilities and domain expertise. Originally developed by Anthropic, released as an open standard, and adopted by 30+ agent platforms as of April 2026.

What Agent Skills Are

Skills are folders of instructions, scripts, and resources that agents can discover and use to perform tasks more accurately and efficiently. A skill consists of:

skill-name/
├── SKILL.md          # Required: metadata + instructions
├── scripts/          # Optional: executable code
├── references/       # Optional: documentation
├── assets/           # Optional: templates, resources
└── ...               # Any additional files

SKILL.md Specification

The core file has YAML frontmatter with required fields:

  • name — lowercase alphanumeric + hyphens, max 64 chars, must match directory name
  • description — max 1024 chars, describes what the skill does AND when to use it

Optional fields: license, compatibility, metadata (arbitrary key-value), allowed-tools (experimental pre-approved tool list).

The Markdown body contains instructions with no format restrictions. Recommended: step-by-step procedures, input/output examples, edge cases.

Progressive Disclosure (Token Efficiency)

Skills are structured for efficient context usage across three tiers:

  1. Metadata (~100 tokens) — name and description loaded at startup for ALL skills
  2. Instructions (<5000 tokens recommended) — full SKILL.md body loaded when skill is activated
  3. Resources (as needed) — scripts, references, assets loaded only when required

This means an agent can have hundreds of skills available with minimal token overhead. Only the names and descriptions are in context at startup; the full instructions load on demand.

Adopting Platforms (30+)

Major platforms confirmed:

  • Anthropic: Claude Code, Claude (platform)
  • Microsoft/GitHub: VS Code, GitHub Copilot
  • OpenAI: Codex
  • Google: Gemini CLI
  • Cursor
  • JetBrains: Junie, Kiro
  • Nous Research: Hermes Agent
  • Letta (stateful agents with memory)
  • Block: Goose
  • OpenHands (cloud coding agents)
  • Roo Code
  • Mistral AI: Vibe
  • Databricks: Genie Code
  • Snowflake: Cortex Code
  • Factory (AI-native development)
  • Spring AI (Java ecosystem)
  • TRAE (ByteDance)
  • Qodo (code integrity)
  • Laravel Boost
  • Amp, Autohand, Mux, OpenCode, Firebender, Piebald, pi, Command Code, Ona, VT Code, Emdash, Agentman

Why This Matters

The Agent Skills standard is the largest real-world instance of industrial codification for AI agents. The pattern mirrors historical skill decomposition:

  1. Craft guilds — tacit knowledge held by individuals
  2. Scientific management (Taylor) — explicit process documentation
  3. Algorithmic management — automated process enforcement
  4. Agent Skills — AI-readable procedural knowledge that agents discover, load, and execute

The key difference: Agent Skills are designed for interoperability. A skill written for Claude Code works in Cursor, Hermes Agent, GitHub Copilot, etc. This creates a marketplace dynamic (agentskills.io) where procedural knowledge becomes portable, tradeable, and composable across platforms.

Hermes Agent's Implementation

Hermes Agent was one of the earliest adopters and extends the standard with:

  • Auto-creation: Complex tasks (5+ tool calls) trigger automatic skill generation
  • Self-evolution: GEPA optimizes existing skills via trace-based mutation
  • Progressive disclosure at scale: 40 skills costs the same tokens as 200 skills
  • Community marketplace: Skills Hub at agentskills.io for sharing/installing

Validation and Tooling

The skills-ref reference library provides validation:

skills-ref validate ./my-skill

This checks frontmatter validity and naming conventions. Available on GitHub at agentskills/agentskills.

Open Development

The standard is governed via open development on GitHub (agentskills/agentskills) and Discord. Contributions from any platform are accepted. The spec is versioned and evolving — allowed-tools is explicitly marked as experimental.