skill-specification
Agent Skills formal specification for cross-platform compatibility. Ensures skills are evolutionarily robust across Claude, Codex, Cursor, Amp, and future agents.
Best use case
skill-specification is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Agent Skills formal specification for cross-platform compatibility. Ensures skills are evolutionarily robust across Claude, Codex, Cursor, Amp, and future agents.
Teams using skill-specification should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/skill-specification/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How skill-specification Compares
| Feature / Agent | skill-specification | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Agent Skills formal specification for cross-platform compatibility. Ensures skills are evolutionarily robust across Claude, Codex, Cursor, Amp, and future agents.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Skill Specification
Formal specification for evolutionarily robust agent skills.
## Why This Matters
Skills that follow the spec work across:
- **Claude Code** (Anthropic)
- **Codex CLI** (OpenAI)
- **Cursor** (Anysphere)
- **Amp** (Sourcegraph)
- **Letta** (memGPT)
- Future agents
Non-compliant skills break silently or fail validation.
## SKILL.md Schema
```yaml
---
name: skill-name # REQUIRED: lowercase, hyphens, 1-64 chars
description: What and when # REQUIRED: max 1024 chars, no < or >
license: Apache-2.0 # optional
compatibility: Requires git # optional, max 500 chars
metadata: # optional: custom key-value pairs
trit: 0
author: bmorphism
version: "1.0"
allowed-tools: Bash Read # optional, experimental
---
# Body content (Markdown)
```
## Field Constraints
| Field | Required | Rules |
|-------|----------|-------|
| `name` | ✓ | `[a-z0-9-]+`, no `--`, no leading/trailing `-`, max 64 |
| `description` | ✓ | 1-1024 chars, no `<` or `>`, includes WHEN to use |
| `license` | ✗ | Short name or file reference |
| `compatibility` | ✗ | Environment requirements, max 500 |
| `metadata` | ✗ | Arbitrary k:v for custom fields |
| `allowed-tools` | ✗ | Space-delimited tool names |
## Evolutionary Robustness Patterns
### 1. Progressive Disclosure
```
Level 1: name + description (~100 tokens) - loaded at startup
Level 2: SKILL.md body (<5000 tokens) - loaded on activation
Level 3: scripts/, references/, assets/ - loaded on demand
```
Keep SKILL.md under 500 lines. Move details to `references/`.
### 2. Cross-Platform Compatibility
```yaml
# BAD - platform-specific
allowed-tools: claude_desktop_mcp
# GOOD - generic capability
compatibility: Requires MCP server access
```
### 3. Self-Validation Hook
Include validation in your skill:
```bash
# scripts/validate.sh
skills-ref validate "$(dirname "$0")/.."
```
### 4. Semantic Versioning in Metadata
```yaml
metadata:
version: "2.1.0"
breaking-changes: "v2.0 changed API"
```
### 5. Triadic Classification (GF(3) Extension)
For plurigrid/asi skills:
```yaml
metadata:
trit: -1 # MINUS: verification, constraint
trit: 0 # ERGODIC: balance, mediation
trit: +1 # PLUS: generation, exploration
```
Conservation: `Σ trits ≡ 0 (mod 3)` across compositions.
## Directory Structure
```
skill-name/
├── SKILL.md # Required
├── scripts/ # Executable code
│ └── main.py
├── references/ # Additional docs
│ └── REFERENCE.md
└── assets/ # Static resources
└── template.json
```
## Validation Commands
```bash
# Official validator
skills-ref validate ./my-skill
# Codex-rs validator
python3 codex-rs/core/src/skills/assets/samples/skill-creator/scripts/quick_validate.py ./my-skill
# Batch validate
for d in skills/*/; do skills-ref validate "$d"; done
```
## Common Failures
| Error | Fix |
|-------|-----|
| No YAML frontmatter | Add `---` delimiters |
| Unexpected keys | Move to `metadata:` |
| Angle brackets in description | Remove `<` and `>` |
| Name not hyphen-case | Lowercase, hyphens only |
| Description too long | Max 1024 chars |
| YAML colon in value | Quote the string |
## Evolution Strategy
1. **Start minimal** - name + description + one paragraph
2. **Add scripts/** when automation helps
3. **Add references/** when body exceeds 300 lines
4. **Add metadata** for custom classification
5. **Validate on every commit** via CI
## References
- [agentskills.io/specification](https://agentskills.io/specification)
- [github.com/agentskills/agentskills](https://github.com/agentskills/agentskills)
- [OpenAI Codex Skills](https://developers.openai.com/codex/skills/)
- [Claude Code Skills](https://docs.claude.com/en/docs/claude-code/skills)
## Scientific Skill Interleaving
This skill connects to the K-Dense-AI/claude-scientific-skills ecosystem:
### Graph Theory
- **networkx** [○] via bicomodule
- Universal graph hub
### Bibliography References
- `category-theory`: 139 citations in bib.duckdb
## Cat# Integration
This skill maps to **Cat# = Comod(P)** as a bicomodule in the equipment structure:
```
Trit: 0 (ERGODIC)
Home: Prof
Poly Op: ⊗
Kan Role: Adj
Color: #26D826
```
### GF(3) Naturality
The skill participates in triads satisfying:
```
(-1) + (0) + (+1) ≡ 0 (mod 3)
```
This ensures compositional coherence in the Cat# equipment structure.Related Skills
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