Auto Merge logo

Auto Merge

Organization
aeonfun
auto-merge

Automatically merge open PRs that have passing CI, no blocking reviews, and no conflicts

Overview

Publisheraeonfun
Repositoryaeon
Skill nameauto-merge
Stars
771
Forks
280
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by aeonfun on GitHub. Read the source before you install it.

Installation

Install the Auto Merge AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/aeonfun/aeon/tree/main/skills/auto-merge
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/aeonfun/aeon.git /tmp/aeon
mkdir -p .claude/skills
cp -r /tmp/aeon/skills/auto-merge .claude/skills/auto-merge
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Auto Merge in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Auto Merge on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Auto Merge is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

${var} — Repo (owner/repo) to target. If empty, uses every repo in memory/watched-repos.md. Env: AUTO_MERGE_DRY_RUN=1 logs intent without merging. MAX_AUTO_MERGE=N caps merges per run (default 3).

Merge open PRs that are fully green and pass an explicit safety policy. The policy exists because this skill runs autonomously with write access — a bug in the gate is a bug that ships to main.

Read memory/MEMORY.md and memory/watched-repos.md for repos to target. Read the last 2 days of memory/logs/ to avoid re-logging PRs already merged.

Safety policy

A PR merges only when every one of the following holds:

  • Author allowlist: author.login is one of dependabot[bot], renovate[bot], github-actions[bot], OR appears under a ## Trusted Authors section in memory/watched-repos.md. No allowlist → only the three bot logins are eligible.
  • Size cap: additions + deletions ≤ 500. Override by applying the label auto-merge-large on the PR.
  • Base branch: baseRefName is main or master. Refuse any other target.
  • Not a fork: isCrossRepository == false (fork CI can be tampered with).
  • Not draft: isDraft == false.
  • Not already queued: autoMergeRequest == null (avoid fighting GitHub's native auto-merge if a human enabled it) and the PR is not already in a merge queue (mergeQueueEntry == null, checked in step 2b).
  • No opt-out label: none of {do-not-merge, wip, hold, needs-review, blocked} present.
  • Mergeable state: mergeStateStatus == "CLEAN" (this is stricter than mergeable == "MERGEABLE" — CLEAN additionally requires branch-protection gates to be satisfied).
  • Reviews: reviewDecision != "CHANGES_REQUESTED".
  • Checks: every entry in statusCheckRollup has conclusion in {SUCCESS, NEUTRAL, SKIPPED}. Any FAILURE, TIMED_OUT, CANCELLED, PENDING, or null conclusion disqualifies the PR.
  • Retry cap: this PR has been attempted fewer than 3 times. A PR that has hit MERGE_FAIL three times across runs is paused — repeated failure on a CLEAN-looking PR usually means something subtle (a required check that didn't surface, branch-protection drift, token scope drift). Surface it and stop looping.

Steps

  1. Bootstrap state — per-PR retry counter lives in memory/topics/auto-merge-state.json:

    bash
    mkdir -p memory/topics
    [ -f memory/topics/auto-merge-state.json ] || echo '{"prs":{},"last_run":null}' > memory/topics/auto-merge-state.json

    Schema:

    json
    {
      "last_run": "2026-05-23T08:00:00Z",
      "prs": {
        "owner/repo#123": {
          "first_seen": "2026-05-21T10:00:00Z",
          "last_attempt": "2026-05-23T08:00:00Z",
          "attempts": 2,
          "last_outcome": "merge_failed",
          "last_error": "Pull Request is in unstable state"
        }
      }
    }

    PR keys are <owner>/<repo>#<number> so state survives multi-repo runs. Cap to 50 most-recent entries (LRU by last_attempt). Validate with jq empty after write; restore from .bak on failure.

  2. List open PRs for each watched repo with the full field set:

    bash
    gh pr list -R owner/repo --state open --json number,title,author,isDraft,mergeable,mergeStateStatus,reviewDecision,statusCheckRollup,autoMergeRequest,isCrossRepository,labels,additions,deletions,baseRefName
  3. Handle UNKNOWN state — GitHub computes mergeStateStatus lazily. If a PR returns UNKNOWN, sleep 3 seconds and re-query once:

    bash
    sleep 3 && gh pr view NUMBER -R owner/repo --json mergeStateStatus,mergeable,statusCheckRollup

    If still UNKNOWN after the retry, skip the PR with reason UNKNOWN-persistent and let the next run retry.

    2b. Skip PRs already in a merge queue. A PR this skill queued on an earlier run is still open, so it would be merged again, bump attempts, and hit the retry cap while it waits. gh pr list does not expose the queue, so ask GraphQL per PR:

    bash
    gh api graphql -f query='query($o:String!,$r:String!,$n:Int!){repository(owner:$o,name:$r){pullRequest(number:$n){mergeQueueEntry{state}}}}' \
      -f o=owner -f r=repo -F n=NUMBER --jq '.data.repository.pullRequest.mergeQueueEntry.state // ""'

    A non-empty state (e.g. QUEUED, AWAITING_CHECKS, MERGEABLE) skips the PR as SKIP:already-queued:<state>. A failed lookup skips it as SKIP:merge-queue-unknown. Neither counts as an attempt: do not touch its attempts, and leave its state entry as is.

  4. Apply the safety policy to each PR. Record a verdict for every PR: either MERGE or SKIP:<specific-reason>. Reasons must name the failing gate — e.g. SKIP:author-not-allowlisted:contributor123, SKIP:size-cap:823-lines, SKIP:mergeStateStatus=BEHIND, SKIP:label:do-not-merge, SKIP:check-failed:lint, SKIP:retry-cap:3-attempts. Vague reasons like SKIP:not-ready are not acceptable.

  5. Merge qualifying PRs, up to MAX_AUTO_MERGE (default 3):

    • If AUTO_MERGE_DRY_RUN=1, log DRY_RUN:would-merge #N and continue — do NOT invoke merge.
    • Otherwise:
      bash
      gh pr merge NUMBER -R owner/repo --squash --delete-branch
      A zero exit is not proof of a merge: on a repo with a merge queue, gh pr merge exits 0 after only adding the PR to the queue. Confirm with GitHub before counting or reporting it:
      bash
      gh pr view NUMBER -R owner/repo --json state,mergeCommit --jq '[.state, (.mergeCommit.oid // "")] | @tsv'
      Only MERGED with a commit SHA is a merge: report that SHA, never one inferred from the command output. OPEN means it was queued: log QUEUED #N, record last_outcome: queued, and report it as queued, not merged. Any other result, or a failed lookup, is logged as MERGE_UNCONFIRMED #N and reported as such. Both count toward MAX_AUTO_MERGE: a merge was attempted and GitHub accepted the command, so the cap still bounds how much this run can send to main. Increment state.prs["<owner>/<repo>#<N>"].attempts on every attempt regardless of outcome. Set first_seen if absent. Reset to 0 (delete the entry) for PRs that no longer appear in the open list (already merged or closed since the last run). If the merge fails (non-zero exit), capture stderr and log MERGE_FAIL #N: <stderr>. Record last_outcome: merge_failed and last_error: <stderr ≤200 chars> on the state entry. A failed merge does NOT count toward the per-run MAX_AUTO_MERGE cap — continue to the next qualifying PR. A PR whose attempts has reached 3 is filtered out in step 3 with SKIP:retry-cap:3-attempts; surface it in step 5b instead of retrying.
  6. Send a notification only when at least one real (non-dry-run) merge succeeded or at least one PR has hit the retry cap (5b below). No merges and no cap hits → no notification, just a log entry.

    5a. At least one merge succeeded:

    *Auto Merge — ${today}*
    Merged N PR(s) on owner/repo:
    - #123: PR title (+45/-12, by @author) — squash merged abc1234   (the mergeCommit SHA GitHub returned)
    Queue cleared. Self-improve cycle unblocked.

    5b. Retry cap reached on ≥1 PR (AUTO_MERGE_RETRY_CAP) — include in the same message if both fire, otherwise stand-alone:

    *Auto Merge — retry cap*
    Hit retry cap (3 attempts) on:
    - owner/repo#40 — last error: "Pull Request is in unstable state"
    Stopping auto-merge attempts on this PR. Investigate manually.

    Dedup: suppress re-notify if the exact same set of cap-hit PR keys already notified within the last 24h (grep memory/logs/ for prior AUTO_MERGE_RETRY_CAP entries).

  7. Persist state — write the updated memory/topics/auto-merge-state.json. Update last_run to current timestamp. Validate with jq empty; on failure restore from a .bak written before this run.

  8. Log to memory/logs/${today}.md under an ### auto-merge heading:

    • Mode: live | dry-run
    • Repo(s): list
    • Merged: #N title @author +A-D SHA per line, confirmed MERGED by GitHub
    • Queued: #N per line (merge queue accepted it; not merged yet)
    • Skipped: #N SKIP:<reason> per line
    • Retry-capped: owner/repo#N — <last_error> per line (empty if none)
    • Totals: merged=X queued=Q qualified=Y considered=Z retry_capped=R
    • If zero qualified, include a verdict breakdown: AUTO_MERGE_SKIP: 0/Z qualifying (behind=B blocked=L failing=F draft=D already-queued=U author-blocked=A size-blocked=S retry-capped=R)

Network note

gh authenticates via the workflow's GITHUB_TOKEN — no curl needed. If gh pr merge fails with Resource not accessible by integration, the workflow token lacks merge permission on that repo; log once and notify at most once per 7 days (check memory/logs/ for prior notification) to avoid alert spam.

Constraints

  • Never merge a PR whose author is not allowlisted, even if every other gate is green.
  • Never bypass the size cap without the explicit auto-merge-large label (set by a human, not a bot).
  • Never auto-retry a MERGE_FAIL within the same run — if the first merge attempt fails, log and move on.
  • After 3 failed attempts across runs, stop retrying that PR. Surface it once via the retry-cap notification and let the operator investigate.
  • Do not modify PR state other than merging (no comments, no label edits, no branch updates).

Running this as an agent-shipping loop

To close the loop on PRs the agent itself opens (from feature, external-feature, self-improve, etc.), add the agent's GitHub identity under a ## Trusted Authors section in memory/watched-repos.md:

markdown
## Trusted Authors
- aeon-bot
- claude-code[bot]

Once allowlisted, agent PRs flow through the same safety policy as bot PRs and get auto-merged on green CI. The retry cap protects against runaway behavior on a stuck PR.

Frequently asked questions

What does the Auto Merge AI skill do?

Automatically merge open PRs that have passing CI, no blocking reviews, and no conflicts

Why use Auto Merge on TypingMind?

Because you install it once and use it with any model. Auto Merge is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Auto Merge in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/aeonfun/aeon/tree/main/skills/auto-merge. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Auto Merge?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Auto Merge?

As many as you like. As long as a model supports skills, you can use Auto Merge with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Auto Merge AI skill free?

Yes. It is published on GitHub by aeonfun under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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