trans-triad
Transclusion × Transduction × Transitivity — the three trans- operations that make skills alive. Transclusion pulls live code into context. Transduction transforms it during passage. Transitivity closes the composition: if A reads B and B transforms C, then loading A gives you C.
Best use case
trans-triad is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Transclusion × Transduction × Transitivity — the three trans- operations that make skills alive. Transclusion pulls live code into context. Transduction transforms it during passage. Transitivity closes the composition: if A reads B and B transforms C, then loading A gives you C.
Teams using trans-triad 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/trans-triad/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How trans-triad Compares
| Feature / Agent | trans-triad | 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?
Transclusion × Transduction × Transitivity — the three trans- operations that make skills alive. Transclusion pulls live code into context. Transduction transforms it during passage. Transitivity closes the composition: if A reads B and B transforms C, then loading A gives you C.
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
# Trans-Triad: The Three Operations
## The Problem
2014 skills × ~200 lines average = 400K lines of static text.
Most of it is stale the moment the source code changes.
Loading even 3 skills burns 10K tokens of context.
## The Solution
Skills are not documents. Skills are **transducers** with **transclusion inputs** composed via **transitivity**.
```
┌─────────────────────────────────────────────────────────┐
│ TRANS-TRIAD │
│ │
│ TRANSCLUSION (−1) source.rs ──┐ │
│ Pull live content README.md ──┼──► into skill │
│ from disk/repo/API Cargo.toml ──┘ │
│ │ │
│ TRANSDUCTION (0) ▼ │
│ Transform during ┌─────────┐ │
│ passage through │ extract │ │
│ the skill │ reshape │ │
│ │ filter │ │
│ └────┬────┘ │
│ │ │
│ TRANSITIVITY (+1) ▼ │
│ Compose chains: A reads B │
│ A→B, B→C ⊢ A→C B transforms C │
│ ∴ loading A gives you C │
└─────────────────────────────────────────────────────────┘
```
## 1. Transclusion (−1, MINUS, Validator)
A skill declares what it READS, not what it CONTAINS.
```yaml
# In SKILL.md frontmatter
transclude:
- source: src/server.rs
extract: "#[tool(" # grep pattern
format: tool-signatures # how to reshape
- source: IMPLEMENTATION_STATUS.md
extract: "## Status"
format: section
- source: Cargo.toml
extract: "[dependencies]"
format: deps
```
At load time, the agent:
1. Reads each `source` file
2. Extracts matching content
3. Injects as `<transcluded>` XML inside `<loaded_skill>`
```xml
<loaded_skill name="signal-messaging">
<transcluded from="src/server.rs" pattern="#[tool(">
signal_encrypt_message: Encrypt via Double Ratchet + Sealed Sender
signal_initialize_session: X3DH key agreement
signal_verify_safety_number: Identity fingerprint verification
</transcluded>
<static>
... rest of SKILL.md ...
</static>
</loaded_skill>
```
**Cost**: 1 file read per source. No stale content. Skill stays 30 lines.
## 2. Transduction (0, ERGODIC, Coordinator)
The skill is not a passive container — it's a **signal processor**.
Content passes THROUGH it and is transformed.
### Transduction operations:
| Op | Input | Output | Example |
|---|---|---|---|
| `extract` | Full file | Matching lines | `#[tool(` from 500-line server.rs |
| `reshape` | Raw code | Structured table | Tool signatures → markdown table |
| `filter` | All content | Relevant subset | Only tools, not imports/tests |
| `annotate` | Code | Code + context | Add trit assignments to each tool |
| `compose` | Multiple sources | Unified view | server.rs + types.rs → full API |
### The transducer pattern:
```
input_signal ──► [skill as transducer] ──► output_signal
│
transform = f(input)
f is defined by the skill's
extract/reshape/filter rules
```
A skill that just transclude without transducing is a symlink.
A skill that transduces is a **lens** — it focuses what matters.
### Concrete transduction: signal-messaging
```
server.rs (500 lines) ──► extract #[tool( ──► 3 tool signatures
reshape as table (15 lines)
filter out impl
annotate trit
Compression ratio: 500:15 = 33×
```
## 3. Transitivity (+1, PLUS, Generator)
If skill A transclude from source B,
and skill B transduces output C,
then loading A produces C without loading B.
```
beeper ──transclude──► signal-messaging ──transduce──► signal-mcp/server.rs
│ │
└──── transitively ──── loading beeper gives you ───────────┘
signal tool signatures without loading signal-messaging
```
### Transitive chains in the skill graph:
```
beeper
├── transclude → signal-messaging → signal-mcp/src/server.rs
├── transclude → messaging-world → ~/i.duckdb dm_landscape schema
├── transclude → gmail-anima → Google Workspace MCP tools
└── transclude → worlding-calendar → CalDAV + org-mode events
Loading "beeper" transitively gives you:
- 3 Signal tools (from server.rs)
- DM landscape schema (from DuckDB)
- Gmail MCP tools (from workspace)
- Calendar events (from CalDAV)
```
### Transitivity makes the graph SPARSE
Without transitivity: every skill duplicates its dependencies' content.
With transitivity: each skill is a pointer. Content lives once, at the source.
```
Before: 2014 skills × 200 lines = 402,800 lines of static content
After: 2014 skills × 30 lines = 60,420 lines of pointers
+ live transclusion from ~466 source repos
Compression: 6.7×
Freshness: always current
```
## Implementation
### At API call time (Anthropic XML injection):
```python
def load_skill(name: str) -> str:
skill = read(f"skills/{name}/SKILL.md")
frontmatter = parse_yaml(skill)
xml_parts = ['<loaded_skill name="{}">'.format(name)]
# Transclusion: pull live content
for t in frontmatter.get('transclude', []):
content = read(t['source'])
extracted = grep(content, t['extract'])
# Transduction: transform during passage
reshaped = reshape(extracted, t.get('format', 'raw'))
xml_parts.append(
'<transcluded from="{}" pattern="{}">\n{}\n</transcluded>'.format(
t['source'], t['extract'], reshaped
)
)
# Transitivity: follow subsumes/evolves chains
for dep in frontmatter.get('subsumes', []):
dep_content = load_skill(dep) # recursive!
xml_parts.append(
'<transitive from="{}">\n{}\n</transitive>'.format(dep, dep_content)
)
xml_parts.append('<static>\n{}\n</static>'.format(skill))
xml_parts.append('</loaded_skill>')
return '\n'.join(xml_parts)
```
### Depth limit
Transitivity recurses. Cap at depth 2 to prevent context explosion:
```
Depth 0: beeper (30 lines static + transcluded)
Depth 1: signal-messaging (15 lines transcluded from server.rs)
Depth 2: STOP — don't recurse into signal-mcp's own dependencies
```
## GF(3) Conservation
```
transclusion (−1) — pulls IN, consumes source, validator
transduction ( 0) — transforms, neither creates nor destroys
transitivity (+1) — extends OUT, generates new reachability
(−1) + (0) + (+1) = 0 ✓
```
Every skill interaction exercises all three:
1. Read live source (transclusion)
2. Extract/reshape (transduction)
3. Compose into caller's context (transitivity)
## The Stub Is the Skill
A 30-line SKILL.md with 3 transclusion targets IS the complete skill.
The code it points to is the content.
The extract patterns are the lens.
The composition graph is the knowledge.
Static SKILL.md files are dead documents.
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