Best use case
cat is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
cat Skill: Derivational Pipe Chaining
Teams using cat 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
$curl -o ~/.claude/skills/cat/SKILL.md --create-dirs "https://raw.githubusercontent.com/plurigrid/asi/main/plugins/asi/skills/cat/SKILL.md"
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/cat/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How cat Compares
| Feature / Agent | cat | 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?
cat Skill: Derivational Pipe Chaining
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
# cat Skill: Derivational Pipe Chaining
**Trit**: 0 (ERGODIC - coordinator)
**Color**: #26D826 (Green)
**Principle**: Chain operations via derivational succession, not temporal
---
## Overview
The `cat` skill implements the `|>` pipe operator using **derivational chaining** instead of temporal succession. Each pipe stage advances a seed: `seed_{n+1} = f(seed_n, trit_n)`.
## Core Formula
```
pipe_chain: A |> f |> g |> h
seed₀ → f(seed₀, trit_f) → seed₁
seed₁ → g(seed₁, trit_g) → seed₂
seed₂ → h(seed₂, trit_h) → seed₃
GF(3) conservation: Σ(trit_f + trit_g + trit_h) ≡ 0 (mod 3)
```
## Babashka Implementation
```clojure
(ns cat.pipe
(:require [clojure.string :as str]))
(def GAMMA 0x9E3779B97F4A7C15)
(def MIX 0xBF58476D1CE4E5B9)
(def MASK64 0xFFFFFFFFFFFFFFFF)
(defn chain-seed [seed trit]
(bit-and (unchecked-multiply
(bit-xor seed (* trit GAMMA))
MIX)
MASK64))
(defmacro |>
"Derivational pipe with GF(3) tracking"
[seed & forms]
(reduce (fn [acc [f trit]]
`(let [result# (~f ~acc)
new-seed# (chain-seed (:seed ~acc) ~trit)]
(assoc result# :seed new-seed# :trit ~trit)))
`{:value ~seed :seed 0x42D :trit 0}
(partition 2 forms)))
```
## Usage
```bash
# Pipe with GF(3) conservation
bb -e "(require '[cat.pipe :refer [|>]])
(|> 'input'
[read-fn -1] ; MINUS: validate
[transform-fn 0] ; ERGODIC: coordinate
[write-fn +1]) ; PLUS: generate
; => GF(3) sum = 0 ✓"
```
## Commands
```bash
# Run pipe chain
just cat-pipe 'input' -1 0 +1
# Verify GF(3) conservation
just cat-verify-gf3 chain.edn
```
## Integration
Forms triads with temporal-coalgebra (-1) and synthetic-adjunctions (+1):
```
temporal-coalgebra (-1) ⊗ cat (0) ⊗ synthetic-adjunctions (+1) = 0 ✓
```
---
**Skill Name**: cat
**Type**: Pipe Coordinator
**Trit**: 0 (ERGODIC)
**Replaces**: dypler-mcp (not found in npm)
## 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
We are still matching the closest adjacent skills for this page. In the meantime, continue through the full directory.