# Knowledge Sememolution Skill

> Skill for population-based Knowledge Graph evolution via Sememolution. Use this when the user wants creative cross-domain synthesis, speculative reasoning, or when a single KG search might be too narrow. Sememolution maintains a population of KG "individuals". Each individual has its own graph (different facts, concepts, links) and its own genome controlling how it searches and evolves. Core genome parameters: - `lambda_depth` — Poisson rate for search traversal depth - `lambda_breadth` — Poisson rate for search breadth per step - `sleep_ops` — which refinement ops to apply during sleep - `dream_probability` — chance of speculative synthesis per cycle Workflow: 1. Create a population: `SememolutionPopulation(model, provider, population_size=100, sample_size=10)` 2. Initialize: `pop.initialize()` 3. Assimilate text: `pop.assimilate_text(chunk)` — each individual absorbs it differently 4. Sleep cycle: `pop.sleep_cycle()` — each individual prunes/deepens independently 5. Query and rank: `pop.query_and_rank(quest

- Skill: `npc-worldwide/knowledge-sememolution-skill` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add npc-worldwide/knowledge-sememolution-skill`
- Raw SKILL.md: https://api.skillmd.com/api/skills/npc-worldwide/knowledge-sememolution-skill/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: npc-worldwide (https://skillmd.com/u/npc-worldwide)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/npc-worldwide/knowledge-sememolution-skill

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# knowledge_sememolution_skill

Skill for population-based Knowledge Graph evolution via Sememolution. Use this when the user wants creative cross-domain synthesis, speculative reasoning, or when a single KG search might be too narrow.
Sememolution maintains a population of KG "individuals". Each individual has its own graph (different facts, concepts, links) and its own genome controlling how it searches and evolves.
Core genome parameters: - `lambda_depth` — Poisson rate for search traversal depth - `lambda_breadth` — Poisson rate for search breadth per step - `sleep_ops` — which refinement ops to apply during sleep - `dream_probability` — chance of speculative synthesis per cycle
Workflow: 1. Create a population: `SememolutionPopulation(model, provider, population_size=100, sample_size=10)` 2. Initialize: `pop.initialize()` 3. Assimilate text: `pop.assimilate_text(chunk)` — each individual absorbs it differently 4. Sleep cycle: `pop.sleep_cycle()` — each individual prunes/deepens independently 5. Query and rank: `pop.query_and_rank(question)` — sample individuals, each searches
   its own graph with Poisson-sampled depth/breadth, generates a response,
   and responses are ranked. Winners get fitness bumps.
6. Evolve: `pop.evolve_generation()` — tournament selection, crossover, mutation.
When to use this: - The user asks open-ended "what if" or "how might X relate to Y" questions - You need diverse perspectives on the same knowledge corpus - You want to discover non-obvious connections across domains - Standard KG search returns shallow or overly literal results
Important: this is computationally expensive. Only invoke after checking whether standard keyword/embedding/hybrid search is sufficient.

## Inputs

- `name` (default: `'task'`)
- `description` (default: `'initialize | assimilate | query_rank | evolve | sleep'`)
- `name` (default: `'population_size'`)
- `description` (default: `'Number of individuals (default 100)'`)
- `name` (default: `'query_text'`)
- `description` (default: `'Question to ask the population (for query_rank)'`)

## Steps

- `instruct` → [`instruct.py`](./instruct.py)

## Usage

```
/run_jinx jinx_ref=knowledge_sememolution_skill input_values={"name": "query_text", "description": "Question to ask the population (for query_rank)"}
```