skillinfer 0.1.1

Infer a full skill profile from a few observations. Few-shot capability estimation for AI agents and humans.

skillinfer

Infer a full skill profile from a few observations.

Observe a few skills, predict the rest — with calibrated uncertainty. skillinfer learns how capabilities co-vary across a population and uses that structure to infer a full profile from partial observations.

A closed-form Bayesian update — no training loop, no GPU. One matrix operation gives you the exact posterior. Under 1ms per update, scales to 1000+ skills.

Install

pip install skillinfer

Quick start

import skillinfer

pop = skillinfer.datasets.onet()          # 894 occupations x 120 skills
profile = pop.profile()                   # new entity, unknown
profile.observe("Skill:Programming", 0.92)
print(profile.predict())                  # predict all 120 skills

                           feature   mean    std  ci_lower  ci_upper
    Skill:Complex Problem Solving   0.81   0.17      0.47      1.00
          Skill:Critical Thinking   0.73   0.15      0.43      1.00
              Skill:Programming     0.92   0.01      0.90      0.93  ← observed
                Skill:Mathematics   0.67   0.12      0.43      0.91
         Ability:Static Strength   0.10   0.23      0.00      0.55  ← anti-correlated
...
[120 rows x 5 columns]

How it works

When you observe one skill, the Kalman update propagates to every other skill via the learned covariance:

• Skills with positive covariance move in the same direction (observe high Programming → predict high Analytical Reasoning)
• Skills with negative covariance move opposite (observe high Programming → predict low Static Strength)
• Independent skills are unaffected

The update is the standard closed-form Gaussian conditioning rule, and reported predictions are clipped to [0, 1] to match the population's natural scale.

Each observe() call is O(K²) — one matrix-vector product. No iteration, no convergence.

Core API

import skillinfer
from skillinfer import Skill, Task

# Build a population from any entity-feature matrix
pop = skillinfer.Population.from_dataframe(df)

# Create a profile and observe
profile = pop.profile()
profile.observe("math", 0.95)
profile.observe_many({"code": 0.89, "writing": 0.70})

# Predict with uncertainty
profile.predict()                          # all skills, with CIs
profile.predict("reasoning")               # single skill
profile.most_uncertain(k=3)                # what to assess next

# Match agents to tasks
task = Task({"math": 1.0, "reasoning": 0.5})
result = profile.match_score(task, threshold=0.8)

# Rank a pool of agents
ranking = skillinfer.rank_agents(task, profiles, threshold=0.8)

# Summary statistics
profile.summary(true_vector=ground_truth)  # MAE, RMSE, coverage, etc.
pop.summary()                              # condition number, sparsity, etc.

Built-in datasets

Two preprocessed datasets ship with the package (~440 KB total):

# O*NET 30.2 — U.S. Department of Labor
# 894 occupations x 120 features (skills, knowledge, abilities)
pop = skillinfer.datasets.onet()

# ESCO v1.2.1 — European Commission
# 2,999 occupations x 134 skill groups (binary)
pop = skillinfer.datasets.esco()

Dataset	Entities	Features	Scale	Source
O*NET	894 occupations	120 (35 skills, 33 knowledge, 52 abilities)	Continuous [0, 1]	O*NET 30.2, CC BY 4.0
ESCO	2,999 occupations	134 Level-2 skill groups	Binary {0, 1}	ESCO v1.2.1

Use cases

Domain	Observe	Predict
AI model selection	1-2 benchmark scores	All benchmarks + best model for a task
Human skill profiling	A few task observations	Full occupational profile (120 skills)
Human-AI orchestration	Partial evals for both	Who handles which subtask
Worker-task matching	Known competencies	Fit for new roles and tasks

LLM orchestration

skillinfer profiles are structured context you feed to an LLM orchestrator alongside cost, latency, and business constraints. The LLM reasons about observed vs. inferred skills and applies natural language constraints that no scoring function could replicate:

from openai import OpenAI

# Build profiles from partial evaluations
agents = {
    "gpt-4o":     {"reasoning": 0.92, "code": 0.89},
    "claude-3.5": {"reasoning": 0.90, "writing": 0.95},
    "gemini-pro": {"math": 0.88, "code": 0.82},
}
profiles = {
    name: pop.profile().observe_many(obs)
    for name, obs in agents.items()
}

# Format as context for the orchestrator
agent_context = ""
for name, profile in profiles.items():
    agent_context += f"\n{name}:\n"
    for skill in ["math", "reasoning", "code"]:
        pred = profile.predict(skill)
        source = "observed" if pred["std"] < 0.01 else "inferred"
        agent_context += f"  {skill}: {pred['mean']:.2f} ± {pred['std']:.2f} ({source})\n"

# The LLM decides — not a scoring function
client = OpenAI()
response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": f"Pick an agent for this math task.\n{agent_context}"}],
)

Export / import

# Population
pop.to_csv("population.csv")
pop.to_parquet("population.parquet")
pop = skillinfer.Population.from_csv("population.csv")
pop = skillinfer.Population.from_parquet("population.parquet")

# Profile
profile.to_json("profile.json")
restored = skillinfer.Profile.from_json("profile.json")

d = profile.to_dict()   # plain dict, JSON-serialisable
restored = skillinfer.Profile.from_dict(d)

Visualization

Requires pip install skillinfer[viz].

import skillinfer

pop = skillinfer.datasets.onet()
profile = pop.profile()
profile.observe("Skill:Programming", 0.92)

# Population charts
skillinfer.visualization.correlation_heatmap(pop)     # clustered correlation matrix
skillinfer.visualization.scree_plot(pop)               # PCA variance explained
skillinfer.visualization.feature_distributions(pop)    # box plots by variance
skillinfer.visualization.skill_embedding(pop)          # 2D PCA feature map
skillinfer.visualization.convergence_curve(pop)        # MAE vs. observations

# Profile charts
skillinfer.visualization.posterior_profile(profile)    # predicted skills + uncertainty
skillinfer.visualization.prediction_scatter(profile, true_vec)  # predicted vs. true
skillinfer.visualization.uncertainty_waterfall(pop, observations)  # uncertainty per observation
skillinfer.visualization.compare_profiles({"dev": dev, "nurse": nurse})  # side-by-side

Documentation

Full documentation at kostadindev.github.io/skillinfer:

• Quickstart
• Tutorials — LLM benchmarks, human skills, ESCO, agent orchestration
• How It Works — Kalman update, covariance estimation, computational cost
• API Reference — Population, Profile, Datasets, Visualization

License

MIT