Best use case
resource-sharing is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Resource Sharing Skill
Teams using resource-sharing 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/resource-sharing/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How resource-sharing Compares
| Feature / Agent | resource-sharing | 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?
Resource Sharing Skill
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
# Resource Sharing Skill
> Distribute computational load across machines using GF(3) balanced allocation
## Overview
Resource sharing implements the "all category resource sharing machines" pattern:
- **MINUS (-1)**: Nodes with excess capacity (receivers)
- **ERGODIC (0)**: Coordinator/broker nodes
- **PLUS (+1)**: Nodes with excess load (senders)
## Architecture
```
┌─────────────────────────────────────────────────────────────┐
│ Resource Sharing Mesh │
│ │
│ ┌─────────┐ ┌─────────┐ ┌─────────┐ │
│ │ Node A │◄────►│ Broker │◄────►│ Node B │ │
│ │ PLUS +1 │ │ ERGODIC │ │ MINUS -1│ │
│ │ (sender)│ │ (0) │ │(receiver)│ │
│ └─────────┘ └─────────┘ └─────────┘ │
│ │ ▲ │
│ │ Work Migration │ │
│ └──────────────────────────────────┘ │
│ │
│ GF(3) Invariant: Σ node_trits ≡ 0 (mod 3) │
└─────────────────────────────────────────────────────────────┘
```
## Node Classification
```bash
# Determine node trit based on load
node_trit() {
load=$(uptime | awk -F'load average:' '{print $2}' | cut -d, -f1 | tr -d ' ')
cpus=$(sysctl -n hw.ncpu 2>/dev/null || nproc)
ratio=$(echo "$load / $cpus" | bc -l)
if (( $(echo "$ratio > 0.8" | bc -l) )); then
echo "+1" # PLUS: overloaded, needs to shed work
elif (( $(echo "$ratio < 0.3" | bc -l) )); then
echo "-1" # MINUS: underloaded, can accept work
else
echo "0" # ERGODIC: balanced
fi
}
```
## Resource Transfer Protocol
### Via LocalSend
```bash
# Share file to least loaded peer
share_to_idle() {
local file="$1"
peers=$(tailscale status --json | jq -r '.Peer[] | .HostName')
for peer in $peers; do
trit=$(ssh "$peer" 'node_trit')
if [[ "$trit" == "-1" ]]; then
localsend send --target "$peer" "$file"
return
fi
done
echo "No idle peers available"
}
```
### Via Tailscale
```bash
# Direct file copy to underloaded node
migrate_workload() {
local pid="$1"
local target=$(find_minus_node)
# Checkpoint process state
criu dump -t "$pid" -D /tmp/checkpoint
# Transfer to target
tailscale file cp /tmp/checkpoint "$target:"
# Restore on target
ssh "$target" "criu restore -D /tmp/checkpoint"
}
```
## Babashka Resource Monitor
```clojure
#!/usr/bin/env bb
(require '[babashka.process :refer [shell]])
(defn load-avg []
(-> (shell {:out :string} "sysctl -n vm.loadavg")
:out
(clojure.string/split #"\s+")
second
parse-double))
(defn cpu-count []
(-> (shell {:out :string} "sysctl -n hw.ncpu")
:out
clojure.string/trim
parse-long))
(defn node-trit []
(let [ratio (/ (load-avg) (cpu-count))]
(cond
(> ratio 0.8) +1 ; PLUS: shed load
(< ratio 0.3) -1 ; MINUS: accept load
:else 0))) ; ERGODIC: balanced
(defn scum-processes []
(->> (shell {:out :string} "ps axo pid,%cpu,%mem,comm")
:out
clojure.string/split-lines
rest
(map #(clojure.string/split % #"\s+" 4))
(filter #(> (parse-double (nth % 1)) 30))))
(println "Node trit:" (node-trit))
(println "SCUM processes:" (count (scum-processes)))
```
## GF(3) Load Balancing
```
For N nodes with trits t₁, t₂, ..., tₙ:
Invariant: Σtᵢ ≡ 0 (mod 3)
Rebalancing rules:
1. If Σtᵢ > 0: migrate work from PLUS to MINUS nodes
2. If Σtᵢ < 0: wake idle work on MINUS nodes
3. If Σtᵢ = 0: system balanced, no action needed
```
## Integration with SCUM Score
```
Resource Decision Matrix:
SCUM Score | Node Trit | Action
---------- | --------- | ------
>0.35 | +1 | Migrate process to MINUS node
>0.35 | 0 | Throttle locally
>0.35 | -1 | Allow (node can handle)
<0.35 | any | No action needed
```
## Justfile Recipes
```just
# Check mesh balance
mesh-balance:
for host in $(tailscale status --json | jq -r '.Peer[].HostName'); do
echo -n "$host: "
ssh "$host" 'uptime' 2>/dev/null || echo "unreachable"
done
# Find receiving nodes
find-minus:
tailscale status --json | jq -r '.Peer[].HostName' | while read h; do
load=$(ssh "$h" "uptime | awk -F: '{print \$NF}' | cut -d, -f1")
echo "$h: $load"
done | sort -t: -k2 -n | head -3
# Migrate SCUM to idle node
migrate-scum PID:
target=$(just find-minus | head -1 | cut -d: -f1)
echo "Migrating PID {{PID}} to $target"
```
---
**Skill Name**: resource-sharing
**Trit**: 0 (ERGODIC - Coordinator)
**GF(3) Role**: Brokers load between PLUS and MINUS nodes
**Integration**: scum-score, localsend-mcp, tailscale-mesh
## 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
- `general`: 734 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)
```
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