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capabilities

Skill2 starsChanged 3 months agoMITView on GitHub

The system's self-model — a capability registry (scans every SKILL.md into a layered tree SENSE→DECIDE→ACT→REMEMBER→GOVERN→DEPLOY) plus a "curator" that picks the right capabilities for a goal locally. Use to see the whole toolkit as a system/arborescence, to let an agent discover what botte-secrète can do, or as the data the Conductor reads to compose a plan.

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SKILL.md

capabilities — the system's map of itself (registry + curator)

Turns the 26 modules from a collection into a system: one self-describing tree the rest can reason over.

python -m skills.capabilities.cli map               # ASCII layered system tree
python -m skills.capabilities.cli list --json       # the registry
python -m skills.capabilities.cli curate "test my desktop app"

Layers (the arborescence)

LayerRole
SENSEunderstand project / cluster / task
DECIDEroute the work, cheapest capable
ACTdo the work, local-first
REMEMBERcapitalise (compounding)
GOVERNconsistency & cost control
DEPLOYwire into projects & measure

Each skill's layer comes from a layer: field in its SKILL.md frontmatter, else a built-in map, else ACT. Generated snapshot: docs/system-map.txt.

The curator

curate(goal) ranks the capabilities most relevant to a goal (local lexical match, 0 tokens) — the librarian that hands the Conductor (and you) the right branches of the tree to use. Built on [[skill_finder]].

This registry is the foundation for the Conductor (goal → decision tree → ordered plan of capabilities, executed local-first) and the control loop (measure → adapt). Related: [[skill_finder]], [[auto_router]], [[bootstrap]].

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