refactor: cut training_scan and training_validate, simplify docs

Research from 8 independent evaluations + SkillsBench (7,308 test runs)
found that compact focused context beats comprehensive docs by 20pp.
The training system's value is in correction capture (2-sec saves) and
team propagation (git sync) — not in regex scanning or keyword grep.

Removed:
- training_scan (255 lines) — regex pattern counting, not discovery
- training_validate (315 lines) — keyword grep, not validation

Simplified:
- trainer.txt: removed scan/validate workflows, focused on guided
  teaching and curation
- agent-modes.md: updated trainer section with correction-focused example
- training docs: complete rewrite with new pitch:
  "Correct the agent once. It remembers forever. Your team inherits it."
  Backed by SkillsBench research showing compact > comprehensive.

Net: -753 lines. 152 tests pass.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

Author: anandgupta42

.github/meta/commit.txt

@@ -0,0 +1,24 @@
+feat: merge training into memory with context-aware relevance scoring
+
+Replace two parallel injection systems (memory 8KB + training 16KB)
+with a single unified injection that scores blocks by relevance to
+the current agent.
+
+How it works:
+- All blocks (memory + training) loaded in one pass
+- Each block scored: agent tag match (+10), training kind relevance
+  per agent (+1-5), applied count bonus (+0-3), recency (+0-2),
+  non-training base (+5)
+- Builder sees rules/patterns first; analyst sees glossary/context first
+- Budget is 20KB unified, filled greedily by score
+- Training blocks still tracked with applied counts (fire-and-forget)
+
+Architecture:
+- memory/prompt.ts: new scoreBlock(), unified inject() with InjectionContext
+- memory/types.ts: UNIFIED_INJECTION_BUDGET, AGENT_TRAINING_RELEVANCE weights
+- session/prompt.ts: single inject call with agent context (was 2 separate)
+- training/prompt.ts: deprecated, delegates to MemoryPrompt (backward compat)
+
+No changes to: MemoryStore, TrainingStore, training tools, memory tools.
+
+Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>