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chore: release v6.4.0
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.claude-plugin/plugin.json

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{
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"name": "harness-evolver",
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"description": "LangSmith-native autonomous agent optimization evolves LLM agent code using multi-agent proposers, LangSmith experiments, and git worktrees",
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"version": "6.3.2",
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"description": "LangSmith-native autonomous agent optimization \u2014 evolves LLM agent code using multi-agent proposers, LangSmith experiments, and git worktrees",
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"version": "6.4.0",
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"author": {
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"name": "Raphael Valdetaro"
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},

CHANGELOG.md

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## [6.4.0] - 2026-04-03
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Two competitive-inspired features: compound learning and score certification.
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### Added
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- **Compound Learning** — New `tools/promote_learnings.py` (stdlib-only) extracts proven evolution insights (recurrence >= 5) from `evolution_memory.md` and appends them as permanent rules in the project's CLAUDE.md. Each evolution session permanently improves the project, not just the code. Inspired by [Compound Engineering (EveryInc)](https://github.com/EveryInc/compound-engineering-plugin) and [Self-Improving Agent (pskoett)](https://github.com/pskoett/pskoett-ai-skills).
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- **`/harness:certify` skill** — Runs evaluation 3x and reports mean ± std with STABLE/MARGINAL/UNSTABLE verdict. Verifies LLM-as-judge score consistency before deploying. Inspired by [PluginEval Monte Carlo (wshobson)](https://github.com/wshobson/agents).
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- **Consolidator Phase 5: Promote** — The consolidator agent now flags insights with recurrence >= 5 as promotion candidates. Terminology clarified: "anchored" (rec >= 3, within memory) vs "promoted" (rec >= 5, to CLAUDE.md).
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- **Deploy "Promote learnings" option**`/harness:deploy` offers a 4th option to promote learnings with dry-run preview + user consent before writing.
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## [6.3.2] - 2026-04-03
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### Fixed

package.json

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{
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"name": "harness-evolver",
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"version": "6.3.2",
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"version": "6.4.0",
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"description": "LangSmith-native autonomous agent optimization for Claude Code",
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"author": "Raphael Valdetaro",
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"license": "MIT",

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