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Use when the user is done evolving and wants to finalize, clean up, tag the result, or push the optimized agent.

From plugin
harness-evolver
579 skills6 agents1 hook

Claude Code

$ npx -y skills add raphaelchristi/harness-evolver --skill deploy --agent claude-code

SKILL.md

deploy.SKILL.md
name: harness:deploy
description: "Use when the user is done evolving and wants to finalize, clean up, tag the result, or push the optimized agent."
allowed-tools: [Read, Write, Bash, Glob, AskUserQuestion]

/harness:deploy

Finalize the evolution results. In v3, the best code is already in the main branch (auto-merged during evolve). Deploy is about cleanup, tagging, and pushing.

What To Do

TOOLS="${EVOLVER_TOOLS:-$([ -d ".evolver/tools" ] && echo ".evolver/tools" || echo "$HOME/.evolver/tools")}"
EVOLVER_PY="${EVOLVER_PY:-$([ -f "$HOME/.evolver/venv/bin/python" ] && echo "$HOME/.evolver/venv/bin/python" || echo "python3")}"

1. Show Results

python3 -c "
import json
c = json.load(open('.evolver.json'))
baseline = c['history'][0]['score'] if c['history'] else 0
best = c['best_score']
improvement = best - baseline
print(f'Baseline: {baseline:.3f}')
print(f'Best: {best:.3f} (+{improvement:.3f}, {improvement/max(baseline,0.001)*100:.0f}% improvement)')
print(f'Iterations: {c[\"iterations\"]}')
print(f'Experiment: {c[\"best_experiment\"]}')
"

Show git diff from before evolution started:

git log --oneline --since="$(python3 -c "import json; print(json.load(open('.evolver.json'))['created_at'][:10])")" | head -20

2. Ask What To Do (interactive)

{
  "questions": [{
    "question": "Evolution complete. What would you like to do?",
    "header": "Deploy",
    "multiSelect": false,
    "options": [
      {"label": "Tag and push", "description": "Create a git tag with the score and push to remote"},
      {"label": "Just review", "description": "Show the full diff of all changes made during evolution"},
      {"label": "Clean up only", "description": "Remove temporary files (trace_insights.json, etc.) but don't push"},
      {"label": "Promote learnings", "description": "Add proven evolution insights to CLAUDE.md (permanent knowledge)"}
    ]
  }]
}

3. Execute

**If "Tag and push"**:

VERSION=$(python3 -c "import json; c=json.load(open('.evolver.json')); print(f'evolver-v{c[\"iterations\"]}')")
SCORE=$(python3 -c "import json; print(f'{json.load(open(\".evolver.json\"))[\"best_score\"]:.3f}')")
git tag -a "$VERSION" -m "Evolver: score $SCORE"
git push origin main --tags

**If "Just review"**:

git diff HEAD~{iterations} HEAD

**If "Clean up only"**:

rm -f trace_insights.json best_results.json comparison.json production_seed.md production_seed.json

**If "Promote learnings"**:

$EVOLVER_PY $TOOLS/promote_learnings.py --memory evolution_memory.md --target CLAUDE.md --threshold 5 --dry-run

Show the dry-run output. If the user approves, run without `--dry-run`.

4. Report

  • What was done
  • LangSmith experiment URL for the best result
  • Suggest reviewing the changes before deploying to production
Read more
@raphaelchristi
Ships withharness-evolver

Point at any LLM agent codebase. Harness Evolver will autonomously improve it — prompts, routing, tools, architecture — using multi-agent evolution with LangSmith as the evaluation backend.

579 skills6 agents
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Questions about deploy.

Which coding agents can use deploy?
deploy installs in Claude Code, Codex, Cursor, Gemini CLI, GitHub Copilot, OpenCode and Windsurf with the skills CLI, using each agent's flag shown on this page. Flowy's auto-invocation, which fires a skill when your prompt matches, runs in Claude Code only.