temporal-sequencing
$
npx mdskill add yogsoth-ai/de-anthropocentric-research-engine/temporal-sequencingDetermine the optimal order, timing, and phasing of portfolio elements when dependencies, learning effects, and option value make sequencing matter as much as selection.
SKILL.md
.github/skills/temporal-sequencingView on GitHub ↗
---
name: temporal-sequencing
description: Determine optimal ordering and phasing of portfolio investments using Real Options, Critical path, Dependency graph, and Staged investment methods.
used-by: portfolio-optimization
---
# Temporal Sequencing
## Purpose
Determine the optimal order, timing, and phasing of portfolio elements when dependencies, learning effects, and option value make sequencing matter as much as selection.
## When to use
- Candidates have dependencies (A must precede B)
- Early investments create options for later ones
- Information gained from early bets informs later decisions
- Budget is released in phases over time
- Timing affects value (first-mover advantage, market windows)
## Budget
| Dimension | Target |
|-----------|--------|
| Candidates sequenced | 8-20 |
| Time periods modeled | 3-6 phases |
| Dependencies mapped | all critical |
| Decision points identified | >=2 stage-gates |
## State Ledger
| Field | Type | Description |
|-------|------|-------------|
| candidates | list | Candidates with timing attributes |
| dependencies | graph | Precedence relationships between candidates |
| phases | list | Time periods with budget allocations |
| option_values | list | Value of information/flexibility from early bets |
| sequence | list | Ordered plan with stage-gates |
## Available Tactics
| Tactic | When |
|--------|------|
| pareto-frontier-construction | Trading off speed vs cost vs risk in sequencing |
| scenario-stress-testing | Testing sequence robustness under timeline uncertainty |
## Available SOPs
| SOP | Purpose |
|-----|---------|
| objective-definition | Define sequencing objectives and constraints |
| optimization-run | Find optimal sequences |
| scenario-construction | Model timeline uncertainties |
| portfolio-evaluation-per-scenario | Test sequence under delays/accelerations |
| portfolio-synthesis | Synthesize robust sequence recommendation |
## Execution Guidance
1. Map dependencies and precedence constraints
2. Identify option value — which early investments create future flexibility
3. Define phase budgets and stage-gate criteria
4. Optimize sequence considering dependencies, option value, and constraints
5. Stress-test sequence against timeline uncertainties
6. Build staged investment plan with decision points
## Output Format
```yaml
strategy: temporal-sequencing
sequence:
- phase: 1
candidates: [<name1>, <name2>]
budget: <amount>
stage_gate: <criteria for proceeding>
- phase: 2
candidates: [<name3>]
budget: <amount>
depends_on: [<phase 1 outcomes>]
critical_path: [<ordered candidates>]
option_values:
- candidate: <name>
options_created: [<future possibilities>]
method_used: <real-options|critical-path|staged-investment>
```
More from yogsoth-ai/de-anthropocentric-research-engine
- abductive-hypothesis-generationStrategy: 面对异常的最佳解释推理
- ablation-brainstormRemove components one by one, observe system changes to reveal hidden dependencies and generate ideas from structural gaps.
- ablation-component-mappingMap system architecture to ablatable units for ablation studies
- ablation-designDesign ablation studies to isolate component contributions in ML systems
- ablation-executionRemove components one by one from a system, record the response/impact of each removal.
- abp-vulnerability-classificationClassify assumptions on 2 axes — load-bearing (how much conclusion depends on it) × vulnerable (how likely to be false). Focuses attention on High-Load × High-Vulnerable quadrant.
- abstraction-extractionExtract abstract principles from concrete domain cases. Strips domain-specific details to reveal transferable mechanisms.
- abstraction-ladderPerform bisociation at multiple abstraction levels
- abstraction-ladderingMove between concrete and abstract framings — 3 levels up (Why?) and 3 levels down (How?) to find the most productive research level.
- abstraction-to-designAbstract biological principle to design principle. Bridge from biology to engineering.