feat(hub): Phase-2 deep exemplar — pyaging knowledge graph + gated skill (SHA-stamped)#11
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Proves the JIT deep layer end-to-end on one North-Star repo (pyaging — the clock library that feeds clockbench/Turn 01). All grounded in the real repo/docs at HEAD c2b3000e3c2c: - hub/pyaging/knowledge-graph.json — 20 nodes/edges (modules, real API fns, real clock names, modalities → clockbench) - hub/pyaging/SKILL.md — compute-aging-clocks, using pyaging's REAL API (df_to_adata → predict_age → adata.obs) → hand off to clockbench - hub/pyaging/manifest.json — provenance (source SHA, license, engines, regenerate-on-SHA-change) - scripts/hub_gen.py — offline integrity gate (in make check) + network staleness check (--refresh: currently FRESH) No fabrication (no evidence ⇒ no claim); no stale commits (SHA-gated). E24; audit 100/100. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
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Proves the hub's JIT deep layer end-to-end on one North-Star repo — pyaging (the clock library that feeds
clockbench/Turn 01, so this directly serves the #1 target, G1).Everything is grounded in pyaging's real repo + docs at HEAD
c2b3000e3c2c(fetched, not invented):hub/pyaging/knowledge-graph.json— 20 nodes/edges: modules (pya.pp/pya.pred), real API fns (df_to_adata,predict_age), real clock names (Horvath2013, AltumAge, PCGrimAge, GrimAge2, DunedinPACE), modalities → and an edge toclockbench.hub/pyaging/SKILL.md—compute-aging-clocks, using pyaging's real API (df_to_adata→predict_age→adata.obs), then hands the panel toclockbench.py --inputfor the disagreement number. Closes the loop.hub/pyaging/manifest.json— provenance: source SHA, license (BSD-3), engines, regenerate-on-SHA-change.scripts/hub_gen.py— offline integrity gate (wired intomake check) + network staleness check (--refresh): pinned SHA == live HEAD → FRESH.The two guarantees from
HUB_ARCHITECTURE.md, both demonstrated: no fabrication (grounded + gated) and never stale (SHA-gated freshness). E24; audit 100/100, 13/24 executions.🤖 Generated with Claude Code