Overview
Every Skills instance tracks usage metrics per skill automatically. Metrics help you understand which skills agents use, how often, and how much content they load.
Accessing Metrics
From an Agent
from upsonic import Agent
from upsonic.skills import Skills, BuiltinSkills
agent = Agent(
model="anthropic/claude-sonnet-4-6",
name="Reviewer",
role="Code Reviewer",
goal="Review code quality",
skills=Skills(loaders=[BuiltinSkills(skills=["code-review"])]),
)
result = agent.print_do("Review this function for bugs.")
# Get metrics after execution
metrics = agent.get_skill_metrics()
print(metrics)
# {'code-review': {'load_count': 1, 'reference_access_count': 0,
# 'script_execution_count': 0, 'total_chars_loaded': 1234,
# 'last_used_timestamp': 1710000000.0}}
From a Task
from upsonic import Agent, Task
from upsonic.skills import Skills, BuiltinSkills
task = Task(
description="Summarize this document.",
skills=Skills(loaders=[BuiltinSkills(skills=["summarization"])]),
)
agent = Agent(model="anthropic/claude-sonnet-4-6", name="Writer", role="Writer", goal="Write content")
agent.print_do(task)
metrics = task.get_skill_metrics()
Directly from Skills
from upsonic.skills import Skills, BuiltinSkills
skills = Skills(loaders=[BuiltinSkills()])
metrics = skills.get_metrics()
for skill_name, m in metrics.items():
print(f"{skill_name}: {m.to_dict()}")
SkillMetrics Fields
| Field | Type | Description |
|---|
load_count | int | Times instructions were loaded |
reference_access_count | int | Times references were accessed |
script_execution_count | int | Times scripts were executed |
total_chars_loaded | int | Total characters loaded across all access types |
last_used_timestamp | float | Unix timestamp of last access |
Team Metrics
When skills are attached to a Team, each agent gets an independent copy with its own metrics:
from upsonic import Agent, Task, Team
from upsonic.skills import Skills, BuiltinSkills
agent_a = Agent(model="anthropic/claude-sonnet-4-6", name="Agent A", role="Analyst", goal="Analyze")
agent_b = Agent(model="anthropic/claude-sonnet-4-6", name="Agent B", role="Writer", goal="Write")
team = Team(
agents=[agent_a, agent_b],
skills=Skills(loaders=[BuiltinSkills()]),
mode="coordinate",
model="anthropic/claude-sonnet-4-6",
)
task = Task(description="Analyze data and write a report.")
team.print_do(tasks=task)
# Each agent has independent metrics
print(agent_a.get_skill_metrics())
print(agent_b.get_skill_metrics())
Team skill propagation uses Skills.copy() internally, so each agent’s metrics are tracked independently — one agent’s usage doesn’t affect another’s counts.
Serialization
SkillMetrics supports to_dict() and from_dict() for serialization:
from upsonic.skills import SkillMetrics
m = SkillMetrics(load_count=3, reference_access_count=1)
data = m.to_dict()
restored = SkillMetrics.from_dict(data)
