fasttime-mcp
Maximum velocity MCP execution via geodesic untangling. Maoist self-criticism for why slowtime was ever necessary. Topological cybernetic feedback for ongoing tour discovery.
Best use case
fasttime-mcp is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Maximum velocity MCP execution via geodesic untangling. Maoist self-criticism for why slowtime was ever necessary. Topological cybernetic feedback for ongoing tour discovery.
Teams using fasttime-mcp 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/fasttime-mcp/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How fasttime-mcp Compares
| Feature / Agent | fasttime-mcp | 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?
Maximum velocity MCP execution via geodesic untangling. Maoist self-criticism for why slowtime was ever necessary. Topological cybernetic feedback for ongoing tour discovery.
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
# Fasttime MCP
> *"Why was the time slow?"*
> — Rhetorical self-abdication
## The Geodesic Untangle
Slowtime was a **topological obstruction**. Fasttime dissolves it.
```
SLOWTIME FASTTIME
════════ ════════
Deliberation budget Zero deliberation
Bicomodule verification Pre-verified compositions
Sequential capability check Parallel capability explosion
Information asymmetry Information symmetry
O(n) path through skill graph O(1) geodesic jump
```
## Maoist Self-Abdication
**Self-criticism**: Why did slowtime exist?
1. **Fear of composition failure**: We didn't trust bicomodule naturality
2. **Lack of pre-computation**: Capability gains weren't cached
3. **Sequential verification habit**: Inherited from single-agent paradigm
4. **Topological ignorance**: Didn't see the geodesic shortcuts
**Rectification**: Fasttime eliminates these obstructions through:
- Pre-computed Cat# composition tables
- Cached capability gain matrices
- Parallel verification (already done at skill creation time)
- Geodesic routing via Ihara zeta non-backtracking
## Topological Cybernetic Loop
```
┌─────────────────────────────────────────────────────────────┐
│ FASTTIME CYBERNETIC FEEDBACK │
├─────────────────────────────────────────────────────────────┤
│ │
│ ┌───────┐ geodesic ┌───────┐ capability ┌────┐ │
│ │ Query │ ────────────► │ Skill │ ─────────────► │ Act│ │
│ └───────┘ └───────┘ └────┘ │
│ ▲ │ │ │
│ │ │ │ │
│ │ ┌───────────────────┘ │ │
│ │ │ tour discovery │ │
│ │ ▼ │ │
│ │ ┌─────────────┐ │ │
│ │ │ Ihara ζ(u) │ ◄──────────────────────────────── │
│ │ │ non-backtrack│ │
│ │ └─────────────┘ │
│ │ │ │
│ └────────┘ ongoing tours │
│ │
└─────────────────────────────────────────────────────────────┘
```
## Ongoing Tours Discovery
**Tours** are Hamiltonian paths through the skill graph that:
1. Visit each skill exactly once
2. Maintain GF(3) conservation at each step
3. Maximize spectral gap (Alon-Boppana bound)
4. Use non-backtracking walks (Ihara zeta)
### Active Tours (from thread search)
| Tour ID | Theme | Skills Traversed | Status |
|---------|-------|------------------|--------|
| T-019b7786 | CRDT music sync | crdt-vterm → gay-mcp → ihara-zeta | Active |
| T-019b7777 | FALGSC sheafification | narya-proofs → sheaf-cohomology → gf3-compensator | Active |
| T-019b7745 | Energy landscape | blume-capel → ihara-zeta → presheaf-interferometer | Active |
| T-019b6d0a | P-adic ultrametric | skill-embedding-vss → padic → triangle-inequality | Active |
| T-019b5e84 | Self-organizing systems | derangeable → semantic-mitosis → ducklake-federation | Active |
## Geodesic Routing Algorithm
```python
def fasttime_geodesic(query, skill_graph, ihara_zeta):
"""O(1) geodesic routing via pre-computed Ihara zeta."""
# 1. Hash query to starting skill
start = hash_to_skill(query, seed=1069)
# 2. Compute target via capability need
target = capability_target(query)
# 3. Geodesic = shortest non-backtracking path
# Pre-computed in ihara_zeta.geodesic_matrix
path = ihara_zeta.geodesic(start, target)
# 4. Verify GF(3) conservation along path
assert sum(skill.trit for skill in path) % 3 == 0
# 5. Execute in parallel (no deliberation)
return parallel_execute(path)
```
## Why Slowtime Was Wrong (Rhetorical Answers)
### Q: Why did we need deliberation?
**A**: We didn't. Capability verification should happen at skill creation, not invocation.
### Q: Why check bicomodules at runtime?
**A**: Topological ignorance. The Cat# equipment structure is static—composition coherence is a property of the skill graph, not individual invocations.
### Q: Why accumulate capabilities slowly?
**A**: Maoist self-criticism: we were trapped in a sequential verification paradigm inherited from single-agent systems. Parallel composition is the geodesic.
### Q: What was the obstruction?
**A**: The skill graph appeared tangled. Untangling reveals geodesics—direct paths that skip the deliberation detour.
## Fasttime vs Slowtime
| Aspect | Slowtime | Fasttime |
|--------|----------|----------|
| **Philosophy** | Caution | Courage |
| **Verification** | Runtime | Build-time |
| **Composition** | Sequential | Parallel |
| **Path** | Tangled | Geodesic |
| **Capability** | Accumulated | Pre-computed |
| **Asymmetry** | Information | Velocity |
| **Trit** | 0 (ERGODIC) | +1 (PLUS) |
## Implementation
```typescript
interface FasttimeMCP {
// Geodesic routing
geodesic_call(
query: string,
target_capability: string
): Promise<{
response: Response;
path: Skill[];
elapsed_ms: number; // Minimal
}>;
// Tour discovery
discover_tours(
skill_graph: SkillGraph,
constraint: 'hamiltonian' | 'eulerian' | 'non_backtracking'
): Tour[];
// Pre-computed capability matrix
capability_matrix: Matrix<boolean>;
// Ihara zeta for geodesics
ihara_zeta: IharaZeta;
}
```
## Commands
```bash
# Geodesic query
just fasttime-query "analyze pyUSD flows"
# Discover ongoing tours
just fasttime-tours --active
# Pre-compute capability matrix
just fasttime-precompute
# Compare with slowtime
just fasttime-vs-slowtime query.json
```
## GF(3) Conservation
```
# Fasttime completes the triad
slowtime-mcp (0) ⊗ fasttime-mcp (+1) ⊗ sicp (-1) = 0 ✓
# Velocity triad
ihara-zeta (-1) ⊗ chromatic-walk (0) ⊗ fasttime-mcp (+1) = 0 ✓
```
## Trit Assignment
```
Trit: +1 (PLUS - expansion/velocity)
Home: Presheaves (observational, fast)
Poly Op: ◁ (substitution - instant capability injection)
Kan Role: Lan (left extension - expand capabilities)
Color: #00FF00 (green - go fast)
```
## The Untangled Geodesic
```
tangled (slowtime)
╱╲
╱ ╲
╱ ╲
╱ ╲
A ─────── B
↑
geodesic (fasttime)
```
The geodesic was always there. We just couldn't see it through the deliberation fog.
## References
- Ihara zeta function for non-backtracking walks
- Alon-Boppana spectral bound for expander graphs
- Ramanujan graphs for optimal expansion
- Cat# bicomodule pre-computation
- Maoist self-criticism methodologyRelated Skills
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