Rework evalbuff: commit learning mode, parallel agents, trace compression

Two-mode architecture: learn mode walks git history commit-by-commit,
prompt mode runs a specific task. Both use iterative doc improvement
with parallel agent execution, judging, and keep/reject loop.

Key changes:
- Add commit-task-generator: extracts tasks from git history via LLM
- Add trace-compressor: hybrid compression stores large tool results
  in files with inline pointers so doc writer can see agent reasoning
- Rewrite run-evalbuff with runLearnMode/runPromptMode, parallel
  agent execution (N runs per task), and iterative doc improvement
- Fix cli-runner timeout: kill entire process group via detached spawn
- Update judge with judgeTaskResult for prompt mode (no ground truth)
- Update docs-optimizer: always analyze, agent trace support, revert
  logic that preserves previously-accepted doc edits
- Rewrite tests for new architecture

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

Author: jahooma

AGENTS.md

@@ -43,4 +43,3 @@ Make an efficient learning agent that can do anything.
 - [`docs/agents-and-tools.md`](docs/agents-and-tools.md) — Agent system, shell shims, tool definitions
 - [`docs/patterns/handle-steps-generators.md`](docs/patterns/handle-steps-generators.md) — handleSteps generator patterns and spawn_agents tool calls
 - [docs/evalbuff/interpreting-task-prompts.md](docs/evalbuff/interpreting-task-prompts.md)
-- [docs/conventions/simplifying-documentation.md](docs/conventions/simplifying-documentation.md)