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create-jinx

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Mis à jour19 avril 2026 à 14:48

Write a new .jinx file from inputs (name, description, inputs spec as JSON list of strings, python body). Agents use this mid-task to crystallize a repeated block of code into a reusable jinx. The jinx is written to the team's jinxes/lib dir by default and registered with the running team so it is immediately callable without a restart.

Installation

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NPC-Worldwide

NPC-Worldwide/npcpy

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Métiers associésSOC

Basé sur la classification professionnelle SOC

Développeurs de logicielsProfessions informatiques et mathématiques·SOC 15-1252

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SKILL.md

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name	create_jinx
description	Write a new .jinx file from inputs (name, description, inputs spec as JSON list of strings, python body). Agents use this mid-task to crystallize a repeated block of code into a reusable jinx. The jinx is written to the team's jinxes/lib dir by default and registered with the running team so it is immediately callable without a restart.

create_jinx

Inputs

jinx_name
description
inputs_spec (default: '[]')
python_code
target_subdir (default: 'lib')

Steps

write_jinx → write_jinx.py

Usage

/run_jinx jinx_ref=create_jinx input_values={"jinx_name": "<value>", "description": "<value>", "inputs_spec": "[]", "python_code": "<value>", "target_subdir": "lib"}

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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

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Skill for working with local .knowledge.yaml files via KnowledgeStore. Use this when you need to recall, search, or manage directory-local memories and knowledge links stored in plain YAML alongside the user's project files. KnowledgeStore is directory-scoped. Each directory that contains a `.knowledge.yaml` file maintains its own append-only memory graph. There is no global database — the YAML IS the source of truth. Key operations: - Load a directory's store: `npcpy.memory.knowledge_store.get_store_for_path(path)` - Append a memory: `store.append_memory(initial_memory="...", status="pending_approval", ...)` - Update a memory (approve/reject/edit): `store.update_memory(mem_id, status, final_memory)` - Search memories (keyword substring): `store.search_memories("query", limit=20)` - Get approved context for LLM prompts: `store.build_context(max_memories=10)` - Get links for a memory: `store.get_links_for_memory(mem_id)` - Create a link between memories: `store.append_link(from_mem, to_mem, relation="refines",

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NPC-Worldwide/npcpy

Execute a jinx by name (already loaded on the team) or by filesystem path (e.g. a freshly created_jinx that isn't yet registered), passing input values as a JSON object. Returns the jinx's output field or the full context dict. Use this to run a jinx you just wrote without exiting the session.

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name	create_jinx
description	Write a new .jinx file from inputs (name, description, inputs spec as JSON list of strings, python body). Agents use this mid-task to crystallize a repeated block of code into a reusable jinx. The jinx is written to the team's jinxes/lib dir by default and registered with the running team so it is immediately callable without a restart.

create_jinx

Inputs

jinx_name
description
inputs_spec (default: '[]')
python_code
target_subdir (default: 'lib')

Steps

write_jinx → write_jinx.py

Usage

/run_jinx jinx_ref=create_jinx input_values={"jinx_name": "<value>", "description": "<value>", "inputs_spec": "[]", "python_code": "<value>", "target_subdir": "lib"}