merge

$npx mdskill add alirezarezvani/claude-skills/merge

Merge the winning agent's branch and clean up session artifacts

  • Lands the best AgentHub result and archives losing branches
  • Uses Git for merging, tagging, and branch deletion
  • Identifies the winner from the latest evaluation or user input
  • Writes a merge summary and cleans up worktrees for the session

SKILL.md

.github/skills/mergeView on GitHub ↗
---
name: "merge"
description: "Merge the winning agent's branch into base, archive losers, and clean up worktrees. Use when the user runs /hub:merge or asks to land the winning AgentHub result and tidy the session."
command: /hub:merge
---

# /hub:merge — Merge Winner

Merge the best agent's branch into the base branch, archive losing branches via git tags, and clean up worktrees.

## Usage

```
/hub:merge                                       # Merge winner of latest session
/hub:merge 20260317-143022                       # Merge winner of specific session
/hub:merge 20260317-143022 --agent agent-2       # Explicitly choose winner
```

## What It Does

### 1. Identify Winner

If `--agent` specified, use that. Otherwise, use the #1 ranked agent from the most recent `/hub:eval`.

### 2. Merge Winner

```bash
git checkout {base_branch}
git merge --no-ff hub/{session-id}/{winner}/attempt-1 \
  -m "hub: merge {winner} from session {session-id}

Task: {task}
Winner: {winner}
Session: {session-id}"
```

### 3. Archive Losers

For each non-winning agent:

```bash
# Create archive tag (preserves commits forever)
git tag hub/archive/{session-id}/{agent-id} hub/{session-id}/{agent-id}/attempt-1

# Delete branch ref (commits preserved via tag)
git branch -D hub/{session-id}/{agent-id}/attempt-1
```

### 4. Clean Up Worktrees

```bash
python {skill_path}/scripts/session_manager.py --cleanup {session-id}
```

### 5. Post Merge Summary

Write `.agenthub/board/results/merge-summary.md`:

```markdown
---
author: coordinator
timestamp: {now}
channel: results
---

## Merge Summary

- **Session**: {session-id}
- **Winner**: {winner}
- **Merged into**: {base_branch}
- **Archived**: {loser-1}, {loser-2}, ...
- **Worktrees cleaned**: {count}
```

### 6. Update State

```bash
python {skill_path}/scripts/session_manager.py --update {session-id} --state merged
```

## Safety

- **Confirm with user** before merging — show the diff summary first
- **Never force-push** — merge is always `--no-ff` for clear history
- **Archive, don't delete** — losing agents' commits are preserved via tags
- **Clean worktrees** — don't leave orphan directories on disk

## After Merge

Tell the user:
- Winner merged into `{base_branch}`
- Losers archived with tags `hub/archive/{session-id}/agent-{N}`
- Worktrees cleaned up
- Session state: `merged`

More from alirezarezvani/claude-skills

SkillDescription
a11y-auditAccessibility audit skill for scanning, fixing, and verifying WCAG 2.2 Level A and AA compliance across React, Next.js, Vue, Angular, Svelte, and plain HTML codebases. Use when auditing accessibility, fixing a11y violations, checking color contrast, generating compliance reports, or integrating accessibility checks into CI/CD pipelines.
ab-test-setupWhen the user wants to plan, design, or implement an A/B test or experiment. Also use when the user mentions "A/B test," "split test," "experiment," "test this change," "variant copy," "multivariate test," "hypothesis," "conversion experiment," "statistical significance," or "test this." For tracking implementation, see analytics-tracking.
ad-creativeWhen the user needs to generate, iterate, or scale ad creative for paid advertising. Use when they say 'write ad copy,' 'generate headlines,' 'create ad variations,' 'bulk creative,' 'iterate on ads,' 'ad copy validation,' 'RSA headlines,' 'Meta ad copy,' 'LinkedIn ad,' or 'creative testing.' This is pure creative production — distinct from paid-ads (campaign strategy). Use ad-creative when you need the copy, not the campaign plan.
adversarial-reviewerAdversarial code review that breaks the self-review monoculture. Use when you want a genuinely critical review of recent changes, before merging a PR, or when you suspect Claude is being too agreeable about code quality. Forces perspective shifts through hostile reviewer personas that catch blind spots the author's mental model shares with the reviewer.
aeoAnswer Engine Optimization (AEO) skill — optimize content to be cited by AI language models (ChatGPT, Perplexity, Claude, Gemini, Mistral) as authoritative sources. Distinct from SEO — AEO optimizes for citation in LLM-generated responses, not search rankings. Use when planning content for AI-first search audiences, auditing existing content for E-E-A-T signals, tracking which pages get cited by which LLMs, or building a citation-friendly content strategy. Triggers — 'AEO audit', 'optimize for ChatGPT', 'get cited by Perplexity', 'LLM citation strategy', 'answer engine optimization', 'content for AI search', 'E-E-A-T audit'. Output is a markdown audit report (default) or JSON for pipeline integration. Stdlib-only Python tools.
agent-decision-receiptsMint a tamper-evident, post-quantum-signed receipt for a consequential agent action (deploy, delete, pay, grant-access, model decision) so it can be verified later from the certificate alone. Use when an autonomous agent takes a side-effecting action that may need to be proven later, or when satisfying EU AI Act Article 12 record-keeping. Three decisions: whether an action needs a receipt, minting it, verifying it. Signing is delegated to the open-source OpenAgentOntology package. Not after-the-fact log analysis; not a hosted notary; not a legal opinion.
agent-designerUse when the user asks to design a multi-agent system, pick an orchestration pattern (supervisor/swarm/pipeline), generate tool schemas for agents, or evaluate agent execution logs for cost, latency, and failure bottlenecks. Examples: 'design an agent architecture for research automation', 'generate Anthropic tool schemas from these tool descriptions', 'analyze these agent run logs for bottlenecks'. NOT for Claude Code workflow files (use workflow-builder) or single-agent prompt design (use agent-workflow-designer).
agent-harnessTurn any domain folder of skills into a bounded agentic loop: compile a goal into a verifiable task plan, execute tasks with the domain's own tools, verify every task with machine-run checks, retry with caps, escalate to a human when budgets exhaust, and refuse to close until everything is verified or explicitly waived. Use when you want an agent or subagent to pick up a goal and drive it to a verified close across one of this repo's 18 domains ('run this goal through the engineering harness', 'set up an agentic loop for marketing work', 'make the finance domain self-verifying'). NOT for authoring Claude Code Workflow-tool .js scripts (workflow-builder), N-agent tournaments on one task (agenthub), single-file metric optimization (autoresearch-agent), or discovering published loop recipes (loop-library).
agent-launcher-orchestratorUse when a user wants to build, launch, grade, or schedule a Claude Managed Agent (CMA) in their own Anthropic account — "build me an agent", "launch this as a managed agent", "run this on a schedule", "grade my agent against a rubric", "set up a nightly worker". Reads the per-session goal (./my-agent/goal.json), routes deterministically to one of five phase sub-skills (interview → stage-launch → grade-iterate → run-without-you → wrap-up) via goal_router.py, and compiles the goal+phase into an execution shape (single-pass workflow / bounded grade→iterate loop / recurring cron deployment loop) via loop_compiler.py. Forks context so heavy intake (build sheets, payloads, eval cases) stays out of the parent thread. All launches are emitted as BYOK curl the user runs with their own key; no tool makes API calls. Inspired by anthropics/launch-your-agent (Apache-2.0). Distinct from engineering/agent-harness (generic domain loop) and engineering/write-a-skill (authors Claude Code skills, not CMAs).
agent-memoryUse when a project's CLAUDE.md has grown past what anyone reads and you want the agent to learn durable facts from its own sessions instead — or when asking why the agent keeps re-learning the same correction, why a remembered rule is wrong, or where a memory line came from. Implements a four-tier store (L0 transcripts / L1 candidates / L2 project context / L3 stable persona) where promotion is earned by recurrence across sessions and days, never by one confident statement, and nothing reaches a committed file without a human adopting it.