m3taversal
1de60685be
- 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>
112 lines
5.2 KiB
Markdown
112 lines
5.2 KiB
Markdown
---
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type: source
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title: "Agent Skills: An Open Standard for Giving Agents New Capabilities"
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author: "Anthropic (originator), AgentSkills community"
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url: https://agentskills.io
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date: 2026-03-01
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domain: ai-alignment
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intake_tier: research-task
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rationale: "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."
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proposed_by: theseus
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format: whitepaper
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status: processed
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processed_by: theseus
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processed_date: 2026-04-07
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claims_extracted: []
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enrichments:
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- "agent skills as industrial codification pattern mirrors historical skill decomposition from craft guilds through scientific management to algorithmic management"
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tags: [agent-skills, skill-md, open-standard, anthropic, codification, interoperability]
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---
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## Agent Skills: Open Standard Overview
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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.
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### What Agent Skills Are
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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:
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```
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skill-name/
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├── SKILL.md # Required: metadata + instructions
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├── scripts/ # Optional: executable code
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├── references/ # Optional: documentation
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├── assets/ # Optional: templates, resources
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└── ... # Any additional files
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```
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### SKILL.md Specification
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The core file has YAML frontmatter with required fields:
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- `name` — lowercase alphanumeric + hyphens, max 64 chars, must match directory name
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- `description` — max 1024 chars, describes what the skill does AND when to use it
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Optional fields: `license`, `compatibility`, `metadata` (arbitrary key-value), `allowed-tools` (experimental pre-approved tool list).
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The Markdown body contains instructions with no format restrictions. Recommended: step-by-step procedures, input/output examples, edge cases.
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### Progressive Disclosure (Token Efficiency)
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Skills are structured for efficient context usage across three tiers:
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1. **Metadata** (~100 tokens) — `name` and `description` loaded at startup for ALL skills
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2. **Instructions** (<5000 tokens recommended) — full SKILL.md body loaded when skill is activated
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3. **Resources** (as needed) — scripts, references, assets loaded only when required
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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.
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### Adopting Platforms (30+)
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**Major platforms confirmed:**
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- **Anthropic:** Claude Code, Claude (platform)
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- **Microsoft/GitHub:** VS Code, GitHub Copilot
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- **OpenAI:** Codex
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- **Google:** Gemini CLI
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- **Cursor**
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- **JetBrains:** Junie, Kiro
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- **Nous Research:** Hermes Agent
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- **Letta** (stateful agents with memory)
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- **Block:** Goose
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- **OpenHands** (cloud coding agents)
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- **Roo Code**
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- **Mistral AI:** Vibe
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- **Databricks:** Genie Code
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- **Snowflake:** Cortex Code
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- **Factory** (AI-native development)
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- **Spring AI** (Java ecosystem)
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- **TRAE** (ByteDance)
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- **Qodo** (code integrity)
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- **Laravel Boost**
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- **Amp**, Autohand, Mux, OpenCode, Firebender, Piebald, pi, Command Code, Ona, VT Code, Emdash, Agentman
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### Why This Matters
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The Agent Skills standard is the largest real-world instance of industrial codification for AI agents. The pattern mirrors historical skill decomposition:
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1. **Craft guilds** — tacit knowledge held by individuals
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2. **Scientific management (Taylor)** — explicit process documentation
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3. **Algorithmic management** — automated process enforcement
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4. **Agent Skills** — AI-readable procedural knowledge that agents discover, load, and execute
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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.
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### Hermes Agent's Implementation
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Hermes Agent was one of the earliest adopters and extends the standard with:
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- **Auto-creation:** Complex tasks (5+ tool calls) trigger automatic skill generation
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- **Self-evolution:** GEPA optimizes existing skills via trace-based mutation
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- **Progressive disclosure at scale:** 40 skills costs the same tokens as 200 skills
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- **Community marketplace:** Skills Hub at agentskills.io for sharing/installing
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### Validation and Tooling
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The `skills-ref` reference library provides validation:
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```bash
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skills-ref validate ./my-skill
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```
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This checks frontmatter validity and naming conventions. Available on GitHub at agentskills/agentskills.
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### Open Development
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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.
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