gts

name	gts
description	The fundamental pattern for making cognitive tasks tractable: Generate possibilities, then Search using criteria.
output	{"format":"prose"}

Generate-Then-Search Meta-Pattern

Input: $ARGUMENTS

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Interpretations

Before executing, identify which interpretation matches the user's input:

Interpretation 1 — Solve a specific problem: The user has a concrete problem and wants to generate candidate solutions then systematically select the best one using criteria. Interpretation 2 — Choose among known options: The user already has options in mind and needs help defining selection criteria and ranking them. Interpretation 3 — Explore a possibility space: The user wants to map out what is possible in a domain before committing to any direction, emphasizing the generation phase over selection.

If ambiguous, ask: "I can help with solving a specific problem, choosing among known options, or exploring a possibility space — which fits?" If clear from context, proceed with the matching interpretation.

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Overview

The fundamental pattern for making cognitive tasks tractable:

1. GENERATE a comprehensive space of possibilities
2. SEARCH within that space using well-defined criteria

Key insights:

• The intelligence is in the CRITERIA, not the search
• Generation can be constrained to exclude obviously bad options
• Comparison/selection is easier than generation from scratch
• Matching to criteria requires less intelligence than judgment

This pattern applies to: interpretation, prediction, communication, reasoning, creativity, understanding, and planning.

Steps

Step 1: Identify the Task

What cognitive task needs to be performed?

Task Type	Generate	Search For
Interpretation	Possible meanings	Most consistent with evidence
Prediction	Possible futures	Most likely given data
Communication	Possible expressions	Clearest for audience
Reasoning	Possible inferences	Logically valid ones
Creativity	Novel combinations	Valuable and feasible ones
Understanding	Possible models	Best fit to observations
Planning	Possible plans	Optimal given constraints
Decision	Possible options	Best according to criteria
Diagnosis	Possible causes	Most consistent with symptoms
Design	Possible solutions	Meeting requirements

Step 2: GENERATE — Create the Possibility Space

Rules for generation:

1. Breadth first: Generate MANY options before evaluating ANY

   • Premature evaluation kills the best options
   • Target: 2-3× more options than you think you need

2. Constrained generation: Pre-filter obviously impossible options

   • Physical impossibility → exclude
   • Violates hard constraints → exclude
   • Everything else → include, even if it seems unlikely

3. Generation methods (combine several):

   • Systematic enumeration (→ /se)
   • Morphological analysis (→ /ma)
   • Cross-domain analogy (→ /cda)
   • Inversion (→ /va, /im)
   • Random combination
   • Expert knowledge

4. Quality check: Is the space COMPREHENSIVE?

   • Have you included the obvious AND the non-obvious?
   • Have you included options from outside your domain?
   • Would someone from a different background add options you missed?

GENERATED SPACE:
Options: [N total]
Generation methods used: [which]
Constraint pre-filter: [what was excluded and why]
Completeness: [confident / gaps possible in: areas]

Step 3: DEFINE CRITERIA — Set Search Parameters

Before searching, define what you're looking for:

1. Must-have criteria (non-negotiable):

   • [criterion] — how measured
   • [criterion] — how measured

2. Ranked criteria (in order of importance):

   • [criterion] — weight: [1-10]
   • [criterion] — weight: [1-10]

3. Anti-criteria (things to avoid):

   • [what to avoid] — why

Criteria quality check:

• Are criteria INDEPENDENT of each other? (Not measuring the same thing twice)
• Are criteria MEASURABLE? (Not "good" — specifically what makes it good)
• Are criteria RANKED? (You can't optimize everything equally)
• Do criteria reflect what you ACTUALLY want? (Not what sounds impressive)

Step 4: SEARCH — Apply Criteria to Space

Apply criteria systematically:

Round 1 — Must-have filter: Pass every option through must-have criteria. Eliminate failures. Remaining: [N] of [N original]

Round 2 — Ranked scoring: Score remaining options on ranked criteria:

Option	Criterion 1 (wt: X)	Criterion 2 (wt: Y)	...	Weighted Total
A	[score]	[score]		[total]
B	[score]	[score]		[total]

Round 3 — Top candidates: Take top 3-5 by weighted score for deeper evaluation.

Step 5: EVALUATE — Deep-Dive Top Candidates

For each top candidate:

1. What are the risks specific to this option?
2. What assumptions does it depend on?
3. What would execution look like?
4. What would a critic say about this choice?

Step 6: SELECT

Based on evaluation:

1. Which option has the best combination of score and low risk?
2. Is there a clear winner, or are top options very close?
3. If close: what tiebreaker criterion matters most?
4. → INVOKE: /sel (selection) or /dcp (decision procedure) for formal selection

Step 7: Report

GENERATE-THEN-SEARCH:
Task: [what was being solved]

Generation:
- Options generated: [N]
- Methods: [which generation methods]
- Constraints: [what was pre-filtered]

Search criteria:
- Must-have: [list]
- Ranked: [top 3 criteria with weights]

Results:
- Passed must-have: [N] of [N]
- Top 3:
  1. [option] — score: [N] — key strength: [what]
  2. [option] — score: [N]
  3. [option] — score: [N]

Selected: [option]
Rationale: [why this one]
Key risk: [biggest concern with selected option]

When to Use

• Any task where generating options and selecting among them is more tractable than directly constructing the answer
• When you're not sure where to start (generate broadly, then narrow)
• When quality of selection criteria matters more than quantity of options
• → INVOKE: /se (systematic enumeration) for generation
• → INVOKE: /gen (candidate generation) for diverse candidates
• → INVOKE: /sel (selection) for formal selection from candidates
• → INVOKE: /m (matching criteria) for defining search criteria

Verification

• Generation phase completed BEFORE evaluation phase
• Multiple generation methods used
• Criteria defined BEFORE searching
• Criteria are independent, measurable, and ranked
• Must-have filter applied first, then ranked scoring
• Top candidates evaluated in depth (not just scored)