Context
From a study of the external ECC project (github.com/affaan-m/ECC). ECC's continuous-learning-v2 observes sessions via hooks → atomic "instincts" (confidence 0.3–0.9, project-scoped) → /evolve clusters them into skills; its state-store also logs skillRuns[] {outcome, tokensUsed, durationMs, userFeedback} and skillVersions[] {contentHash, promoted, rolledBack}.
Proposed
- Outcome-feedback learning loop: a mechanism (forgeplan + Hindsight) that mines recurring session patterns into confidence-scored candidates and promotes high-confidence ones (e.g. seen in ≥2 projects, avg confidence ≥0.8) into durable artifacts (NOTE / claim / hypothesis). forgeplan already has the promotion primitives —
forgeplan_claim / _hypothesis_promote; this wires the front of the loop. This is the Ruflo-style learning layer NOTE-004 said we'd adopt.
- Run telemetry → R_eff: log per-artifact/agent execution outcomes (outcome / tokens / duration / userFeedback) and let
forgeplan_decay / _calibrate factor actual usage outcomes — not just graph structure — into R_eff.
Why
Closes the missing "learning layer": forgeplan + Hindsight capture sessions today but don't auto-synthesise durable, confidence-scored knowledge, and R_eff reflects structure, not observed outcomes.
Refs: ECC skills/continuous-learning-v2/SKILL.md, schemas/state-store.schema.json (skillRuns[]/skillVersions[]); our NOTE-004 (Ruflo) + Hindsight. Surfaced by ECC competitive-analysis; filed from ForgePlan/marketplace.
Context
From a study of the external ECC project (github.com/affaan-m/ECC). ECC's
continuous-learning-v2observes sessions via hooks → atomic "instincts" (confidence 0.3–0.9, project-scoped) →/evolveclusters them into skills; its state-store also logsskillRuns[] {outcome, tokensUsed, durationMs, userFeedback}andskillVersions[] {contentHash, promoted, rolledBack}.Proposed
forgeplan_claim/_hypothesis_promote; this wires the front of the loop. This is the Ruflo-style learning layer NOTE-004 said we'd adopt.forgeplan_decay/_calibratefactor actual usage outcomes — not just graph structure — into R_eff.Why
Closes the missing "learning layer": forgeplan + Hindsight capture sessions today but don't auto-synthesise durable, confidence-scored knowledge, and R_eff reflects structure, not observed outcomes.
Refs: ECC
skills/continuous-learning-v2/SKILL.md,schemas/state-store.schema.json(skillRuns[]/skillVersions[]); our NOTE-004 (Ruflo) + Hindsight. Surfaced by ECC competitive-analysis; filed from ForgePlan/marketplace.