skills/paved-road-registry/SKILL.md
Discovers and records repo-local approved build paths so agents reuse proven patterns instead of inventing parallel architectures.
npx skillsauth add bigeasyfreeman/adlc paved-road-registryInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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AI-generated code scales when agents build with the repo's proven patterns. This skill discovers those patterns, names where they apply, and records when no paved road exists.
Use this skill to keep monoliths and large codebases consistent as agent throughput increases. It complements reuse-analysis: reuse analysis finds concrete things not to reimplement; paved-road registry captures the broader build contract agents should follow.
approved, preferred, deprecated, or no_paved_road_found.implementation_interface_contract, map the paved road into the contract's reuse, integration points, invariants, and validation gates.{
"id": "string",
"capability": "string",
"status": "approved | preferred | deprecated | no_paved_road_found",
"reference_impl": "path:line | path | null",
"applies_when": ["string"],
"do_not_reimplement": ["string"],
"required_invariants": ["string"],
"verifiers": ["path | command | schema | fixture"],
"allowed_departure": "string",
"evidence": ["path:line | command output | graph query"]
}
{
"paved_road_candidates": [
{
"id": "string",
"capability": "string",
"status": "approved | preferred | deprecated | no_paved_road_found",
"reference_impl": "string | null",
"applies_when": [],
"do_not_reimplement": [],
"required_invariants": [],
"verifiers": [],
"allowed_departure": "string",
"evidence": []
}
],
"paved_road_gaps": [
{
"capability": "string",
"gap": "string",
"risk": "string",
"evidence": []
}
],
"recommended_task_refs": [
{
"task_id": "string",
"paved_road_refs": [],
"implementation_interface_contract": {},
"departure_requires_review": true
}
]
}
not_applicable when no code path or build pattern changes.no_paved_road_found result explains the search path and closest convention.Remove this discovery skill when models consistently infer repo-local build paths and reuse them without explicit guidance. Keep a smaller verification gate that detects duplicated or inconsistent patterns.
development
Scoped maintenance for docs/solutions entries when stale signals, refactors, or explicit user scope require refresh.
documentation
Conditionally captures verified reusable ADLC learnings into docs/solutions after successful closeout.
development
Uses Graphify as ADLC's graph-backed research layer and Beads as an optional dependency-aware task memory layer. Produces evidence for compatibility, reuse, accuracy, dark-code hotspots, and long-horizon handoff.
tools
Assesses structural and velocity dark-code risk from architecture, AI tool usage, ownership, and deployment practices. Produces a direct risk assessment without inventing missing facts.