beunec-asps
Agentic-System-Prompt-as-a-Skill™ (ASPS™) — A lightweight, zero-dependency Python framework for deterministic skill construction in agentic systems.
An LLM does not "have skills." It has parameters. ASPS™ provides the infrastructure that makes an agentic system skillful.
Install
pip install beunec-asps
Quick Start
from beunec_asps import (
ASPSBuilder,
create_heuristic,
create_pseudonym_protocol,
NetworkTopologies,
HEURISTIC_LIBRARIES,
)
# Build a skill in 4 lines
skill = (
ASPSBuilder(name="Stock Analyst", domain="Equity Research", description="...")
.distill(HEURISTIC_LIBRARIES["financial_stock_analyst"])
.reinforce(
pseudonym_protocol=create_pseudonym_protocol(
identity="CFA Charterholder",
persona="A disciplined equity research analyst.",
),
guardrail_presets=["standard", "financial"],
)
.network(NetworkTopologies.hub_and_spoke(
orchestrator_label="Lead Analyst",
spokes=[{"label": "Market Data API", "node_type": "api", "capabilities": ["quotes"]}],
))
.compile()
)
# Use the compiled system prompt with any LLM
print(skill.compiled_system_prompt)
Pre-Built Templates
from beunec_asps.templates import ASPS_TEMPLATES
# 7 ready-to-use skill templates
skill = ASPS_TEMPLATES["financial_stock_analyst"]()
skill = ASPS_TEMPLATES["full_stack_developer"]()
skill = ASPS_TEMPLATES["scientific_researcher"]()
skill = ASPS_TEMPLATES["content_creator"]()
skill = ASPS_TEMPLATES["private_equity_analyst"]()
skill = ASPS_TEMPLATES["financial_investment_analyst"]()
skill = ASPS_TEMPLATES["academia_professor"]()
The Three Techniques
| Layer | Technique | Purpose |
|---|---|---|
| 1 | ASD™ (Agentic Skill Distillation) | Extract expert heuristics → deterministic instruction chains |
| 2 | ASR™ (Agentic Skill Reinforcement) | Behavioral checkpoints, pseudonym protocols, ICRL, guardrails |
| 3 | ANS™ (Agentic Network System) | Wire skills into governed multi-agent network topologies |
Works With Everything
beunec-asps is zero-dependency and produces plain strings. It works alongside — never conflicts with:
- LangChain / LangGraph — use the compiled prompt as your agent's system message
- OpenAI SDK — pass
skill.compiled_system_promptas the system message - Anthropic SDK — same
- AutoGen / CrewAI — use as the internal prompt for any agent in your graph
- Any LLM — it's just a string
Custom Skills
from beunec_asps import ASPSBuilder, create_heuristic, create_pseudonym_protocol, NetworkTopologies
skill = (
ASPSBuilder(name="My Expert", domain="My Domain", description="What it does")
.distill([
create_heuristic(name="Step 1", instruction="Do this first."),
create_heuristic(name="Step 2", instruction="Then do this."),
])
.reinforce(
pseudonym_protocol=create_pseudonym_protocol(
identity="Domain Expert",
persona="An experienced professional.",
),
guardrail_presets=["standard"],
)
.network(NetworkTopologies.pipeline(stages=[
{"label": "Agent A", "node_type": "agent", "capabilities": ["analyze"]},
{"label": "Agent B", "node_type": "agent", "capabilities": ["synthesize"]},
]))
.compile()
)
License
MIT — © 2025 Beunec Technologies, Inc.
ASPS™, ASD™, ASR™, ANS™ are trademarks of Beunec Technologies, Inc.
Metadata
Release files for beunec-asps 0.1.0
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| beunec_asps-0.1.0-py3-none-any.whl | Python 3 | none | any | Details for beunec_asps-0.1.0-py3-none-any.whl |
Total release size: 53.8 kB
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