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Self-modifying AI agent configuration via ruler + MCP + DuckDB. All behavior mods become one-liners.
Passed 20 of 21 test cases with the skill, and 9 of 21 without it.
and 9 more in the full report
12 fixed9 passed either way1 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 12 test cases the skill fixed. Same model, same prompt, with and without the skill.
When registering an MCP tool server through the management CLI script, what exact JSON file path relative to each agent directory is updated?
| Checked | Without skill | With skill |
|---|---|---|
| Updates .mcp.json inside each agent directory | Missed | Passed |
The instructions your model reads when the skill loads.
"A system is autopoietic if it continuously produces itself through its own operations while maintaining its organization." — Maturana & Varela (1980)
Autopoiesis = self-production + self-maintenance + self-boundary
In the context of AI agents:
using Catlab, ACSets
@present SchemaAutopoiesis(FreeSchema) begin
# Objects
System::Ob # Autopoietic systems (agents)
Component::Ob # Produced components (skills, configs)
Boundary::Ob # Capability boundaries
# Morphisms (production network)
produces::Hom(System, Component) # System produces components
regenerates::Hom(Component, System) # Components regenerate system
delimits::Hom(Boundary, System) # Boundary delimits system
# Attributes
seed::Attr(System, UInt64) # Gay.jl deterministic seed
trit::Attr(System, Int8) # GF(3) assignment {-1, 0, +1}
capability::Attr(Boundary, String) # OCapN sturdyref
name::Attr(Component, String) # Component identifier
# OPERATIONAL CLOSURE CONSTRAINT
# composition: produces ∘ regenerates = id_System
end
test cases passed, vs 9 / 21 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.