skillinfer-mcp
MCP server for skillinfer — Bayesian skill inference for AI agents and humans.
Lets any MCP-compatible AI agent (Claude, Cursor, VS Code Copilot, etc.) build and query skill profiles via tool calls.
Install
pip install skillinfer-mcp
Or run directly without installing:
uvx skillinfer-mcp
Configure
Add to your Claude Desktop / Claude Code config:
{
"mcpServers": {
"skillinfer": {
"command": "uvx",
"args": ["skillinfer-mcp"]
}
}
}
Tools
Populations
| Tool | Description |
|---|---|
load_dataset |
Load built-in dataset (O*NET or ESCO) |
load_population_from_csv |
Load population from CSV file |
load_population_from_parquet |
Load population from Parquet file |
list_populations |
List loaded populations |
population_summary |
Summary statistics for a population |
list_features |
List feature names in a population |
Profiles
| Tool | Description |
|---|---|
create_profile |
Create a new skill profile |
observe |
Observe a single skill score |
observe_many |
Observe multiple skills at once |
predict |
Predict all skills with uncertainty |
most_uncertain |
Find the most uncertain skills (for active learning) |
profile_summary |
Summary statistics for a profile |
list_profiles |
List active profiles |
Task matching
| Tool | Description |
|---|---|
match_task |
Score a profile against a weighted task |
rank_agents |
Rank multiple profiles against a task |
Persistence
| Tool | Description |
|---|---|
save_profile |
Save profile to JSON |
load_profile |
Load profile from JSON (requires population) |
Example session
Agent: load_dataset(name="onet", dataset="onet")
→ Population 'onet' loaded: 894 entities x 120 features.
Agent: create_profile(name="alice", population="onet")
→ Profile 'alice' created (120 features, prior=population mean).
Agent: observe(profile="alice", skill="Skill:Programming", score=0.92)
→ Observed Skill:Programming=0.920 on 'alice'. Observations: 1.
Top impacted features:
Skill:Programming: +0.3891
Knowledge:Computers and Electronics: +0.1842
...
Agent: most_uncertain(profile="alice", k=3)
→ [{"feature": "Skill:X", "mean": 0.51, "std": 0.12}, ...]
Agent: predict(profile="alice", skill="Knowledge:Mathematics")
→ {"mean": 0.68, "std": 0.09, "ci_lower": 0.50, "ci_upper": 0.86, ...}
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