Best use case
planar-isotopy-screen is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Planar Isotopy Screen Mapping
Teams using planar-isotopy-screen 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/planar-isotopy-screen/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How planar-isotopy-screen Compares
| Feature / Agent | planar-isotopy-screen | 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?
Planar Isotopy Screen Mapping
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
# Planar Isotopy Screen Mapping
Maps thread states and observations to screen positions using planar isotopy principles.
## Trit Value
**0 (ERGODIC)** - Coordinate between spatial regions
## Purpose
Transform abstract thread relationships into concrete screen positions while preserving topological invariants:
- **Adjacency**: Neighboring trits occupy adjacent screen regions
- **Handedness**: MINUS→left, ERGODIC→center, PLUS→right
- **Conservation**: Screen area sum is invariant under isotopy
## Screen Region Mapping
```
┌─────────────────┬─────────────────┬─────────────────┐
│ │ │ │
│ MINUS │ ERGODIC │ PLUS │
│ (left) │ (center) │ (right) │
│ │ │ │
│ Cold hues │ Neutral hues │ Warm hues │
│ 180-300° │ 60-180° │ 0-60°,300-360° │
│ │ │ │
│ Validator │ Coordinator │ Generator │
│ │ │ │
└─────────────────┴─────────────────┴─────────────────┘
```
## Position Computation
```clojure
(defn seed->screen-position [seed trit screen-width screen-height]
"Map seed deterministically to (x, y) within trit's region.
Uses SplitMix64 decomposition:
- High bits → x offset
- Low bits → y offset"
(let [region (trit->region trit screen-width)
[rand1 seed'] (splitmix64 seed)
[rand2 _] (splitmix64 seed')
x (+ (:x region) (* (:width region) (/ rand1 MASK64)))
y (* screen-height (/ rand2 MASK64))]
{:x x :y y :region trit}))
```
## Observation Lines
When thread A observes thread B, draw a line between their screen positions:
```clojure
(defn observation-line [observer observed]
{:from (seed->screen-position (:seed observer) (:trit observer))
:to (seed->screen-position (:seed observed) (:trit observed))
:gf3-sum (mod (+ (:trit observer) (:trit observed)) 3)})
```
## Planar Isotopy Invariants
1. **No crossings for conserved observations**: Lines between threads with GF(3) sum = 0 should not cross
2. **Triadic bundling**: Triad members form non-crossing triangles
3. **Seed progression**: Moving a thread moves its position deterministically
## Integration with Mutual Thread Observation
```python
from mutual_thread_observation import MutualThreadObservationSystem
system = MutualThreadObservationSystem()
# ... register threads ...
# Get screen positions
for tid, state in system.thread_states.items():
pos = seed_to_screen_position(state.seed, state.trit)
print(f"{tid}: ({pos['x']:.0f}, {pos['y']:.0f}) in {pos['region_label']}")
```
## macOS Integration
Use with macos-use MCP for actual screen interaction:
```bash
# Get element at thread's screen position
bb -e '(require \'[mutual-thread-demo :as mtd])
(let [pos (mtd/seed->screen-position seed trit)]
(println (:x pos) (:y pos)))'
```
Then use `mcp__macos-use__macos-use_click_and_traverse` at those coordinates.
## Related Skills
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- `gay-mcp` - Deterministic color from seed
- `acsets-algebraic-databases` - Schema modeling
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